example of hypothesis with mediating variable

The Three Most Common Types of Hypotheses

In this post, I discuss three of the most common hypotheses in psychology research, and what statistics are often used to test them.

• Post author By sean
• Post date September 28, 2013
• 37 Comments on The Three Most Common Types of Hypotheses

Simple main effects (i.e., X leads to Y) are usually not going to get you published. Main effects can be exciting in the early stages of research to show the existence of a new effect, but as a field matures the types of questions that scientists are trying to answer tend to become more nuanced and specific.  In this post, I’ll briefly describe the three most common kinds of hypotheses that expand upon simple main effects – at least, the most common ones I’ve seen in my research career in psychology – as well as providing some resources to help you learn about how to test these hypotheses using statistics.

Incremental Validity

“Can X predict Y over and above other important predictors?”

This is probably the simplest of the three hypotheses I propose. Basically, you attempt to rule out potential confounding variables by controlling for them in your analysis.  We do this because (in many cases) our predictor variables are correlated with each other. This is undesirable from a statistical perspective, but is common with real data. The idea is that we want to see if X can predict unique variance in Y over and above the other variables you include.

In terms of analysis, you are probably going to use some variation of multiple regression or partial correlations.  For example, in my own work I’ve shown in the past that friendship intimacy as coded from autobiographical narratives can predict concern for the next generation over and above numerous other variables, such as optimism, depression, and relationship status ( Mackinnon et al., 2011 ).

“Under what conditions does X lead to Y?”

Of the three techniques I describe, moderation is probably the most tricky to understand.  Essentially, it proposes that the size of a relationship between two variables changes depending upon the value of a third variable, known as a “moderator.”  For example, in the diagram below you might find a simple main effect that is moderated by sex. That is, the relationship is stronger for women than for men:

With moderation, it is important to note that the moderating variable can be a category (e.g., sex) or it can be a continuous variable (e.g., scores on a personality questionnaire).  When a moderator is continuous, usually you’re making statements like: “As the value of the moderator increases, the relationship between X and Y also increases.”

“Does X predict M, which in turn predicts Y?”

We might know that X leads to Y, but a mediation hypothesis proposes a mediating, or intervening variable. That is, X leads to M, which in turn leads to Y.  In the diagram below I use a different way of visually representing things consistent with how people typically report things when using path analysis.

I use mediation a lot in my own research. For example, I’ve published data suggesting the relationship between perfectionism and depression is mediated by relationship conflict ( Mackinnon et al., 2012 ). That is, perfectionism leads to increased conflict, which in turn leads to heightened depression. Another way of saying this is that perfectionism has an indirect effect on depression through conflict.

Helpful links to get you started testing these hypotheses

Depending on the nature of your data, there are multiple ways to address each of these hypotheses using statistics. They can also be combined together (e.g., mediated moderation). Nonetheless, a core understanding of these three hypotheses and how to analyze them using statistics is essential for any researcher in the social or health sciences.  Below are a few links that might help you get started:

Are you a little rusty with multiple regression? The basics of this technique are required for most common tests of these hypotheses. You might check out this guide as a helpful resource:

https://statistics.laerd.com/spss-tutorials/multiple-regression-using-spss-statistics.php

David Kenny’s Mediation Website provides an excellent overview of mediation and moderation for the beginner.

http://davidakenny.net/cm/mediate.htm

http://davidakenny.net/cm/moderation.htm

Preacher and Haye’s INDIRECT Macro is a great, easy way to implement mediation in SPSS software, and their MODPROBE macro is a useful tool for testing moderation.

http://afhayes.com/spss-sas-and-mplus-macros-and-code.html

If you want to graph the results of your moderation analyses, the excel calculators provided on Jeremy Dawson’s webpage are fantastic, easy-to-use tools:

http://www.jeremydawson.co.uk/slopes.htm

• Tags mediation , moderation , regression , tutorial

37 replies on “The Three Most Common Types of Hypotheses”

I want to see clearly the three types of hypothesis

Thanks for your information. I really like this

Thank you so much, writing up my masters project now and wasn’t sure whether one of my variables was mediating or moderating….Much clearer now.

Thank you for simplified presentation. It is clearer to me now than ever before.

Thank you. Concise and clear

hello there

I would like to ask about mediation relationship: If I have three variables( X-M-Y)how many hypotheses should I write down? Should I have 2 or 3? In other words, should I have hypotheses for the mediating relationship? What about questions and objectives? Should be 3? Thank you.

Hi Osama. It’s really a stylistic thing. You could write it out as 3 separate hypotheses (X -> Y; X -> M; M -> Y) or you could just write out one mediation hypotheses “X will have an indirect effect on Y through M.” Usually, I’d write just the 1 because it conserves space, but either would be appropriate.

Hi Sean, according to the three steps model (Dudley, Benuzillo and Carrico, 2004; Pardo and Román, 2013)., we can test hypothesis of mediator variable in three steps: (X -> Y; X -> M; X and M -> Y). Then, we must use the Sobel test to make sure that the effect is significant after using the mediator variable.

Yes, but this is older advice. Best practice now is to calculate an indirect effect and use bootstrapping, rather than the causal steps approach and the more out-dated Sobel test. I’d recommend reading Hayes (2018) book for more info:

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed). Guilford Publications.

Hi! It’s been really helpful but I still don’t know how to formulate the hypothesis with my mediating variable.

I have one dependent variable DV which is formed by DV1 and DV2, then I have MV (mediating variable), and then 2 independent variables IV1, and IV2.

How many hypothesis should I write? I hope you can help me 🙂

Thank you so much!!

If I’m understanding you correctly, I guess 2 mediation hypotheses:

IV1 –> Med –> DV1&2 IV2 –> Med –> DV1&2

Thank you so much for your quick answer! ^^

Could you help me formulate my research question? English is not my mother language and I have trouble choosing the right words. My x = psychopathy y = aggression m = deficis in emotion recognition

thank you in advance

I have mediator and moderator how should I make my hypothesis

Can you have a negative partial effect? IV – M – DV. That is my M will have negative effect on the DV – e.g Social media usage (M) will partial negative mediate the relationship between father status (IV) and social connectedness (DV)?

Thanks in advance

Hi Ashley. Yes, this is possible, but often it means you have a condition known as “inconsistent mediation” which isn’t usually desirable. See this entry on David Kenny’s page:

Or look up “inconsistent mediation” in this reference:

MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.

This is very interesting presentation. i love it.

This is very interesting and educative. I love it.

Hello, you mentioned that for the moderator, it changes the relationship between iv and dv depending on its strength. How would one describe a situation where if the iv is high iv and dv relationship is opposite from when iv is low. And then a 3rd variable maybe the moderator increases dv when iv is low and decreases dv when iv is high.

This isn’t problematic for moderation. Moderation just proposes that the magnitude of the relationship changes as levels of the moderator changes. If the sign flips, probably the original relationship was small. Sometimes people call this a “cross-over” effect, but really, it’s nothing special and can happen in any moderation analysis.

i want to use an independent variable as moderator after this i will have 3 independent variable and 1 dependent variable…. my confusion is do i need to have some past evidence of the X variable moderate the relationship of Y independent variable and Z dependent variable.

Dear Sean It is really helpful as my research model will use mediation. Because I still face difficulty in developing hyphothesis, can you give examples ? Thank you

Hi! is it possible to have all three pathways negative? My regression analysis showed significant negative relationships between x to y, x to m and m to y.

Hi, I have 1 independent variable, 1 dependent variable and 4 mediating variable May I know how many hypothesis should I develop?

Hello I have 4 IV , 1 mediating Variable and 1 DV

My model says that 4 IVs when mediated by 1MV leads to 1 Dv

Pls tell me how to set the hypothesis for mediation

Hi I have 4 IVs ,2 Mediating Variables , 1DV and 3 Outcomes (criterion variables).

Pls can u tell me how many hypotheses to set.

Thankyou in advance

I am in fact happy to read this webpage posts which carries tons of useful information, thanks for providing such data.

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what if the hypothesis and moderator significant in regrestion and insgificant in moderation?

Thank you so much!! Your slide on the mediator variable let me understand!

Very informative material. The author has used very clear language and I would recommend this for any student of research/

Hi Sean, thanks for the nice material. I have a question: for the second type of hypothesis, you state “That is, the relationship is stronger for men than for women”. Based on the illustration, wouldn’t the opposite be true?

Yes, your right! I updated the post to fix the typo, thank you!

I have 3 independent variable one mediator and 2 dependant variable how many hypothesis I have 2 write?

Sounds like 6 mediation hypotheses total:

X1 -> M -> Y1 X2 -> M -> Y1 X3 -> M -> Y1 X1 -> M -> Y2 X2 -> M -> Y2 X3 -> M -> Y2

Clear explanation! Thanks!

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Methodology

• Mediator vs. Moderator Variables | Differences & Examples

Mediator vs. Moderator Variables | Differences & Examples

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A mediating variable (or mediator ) explains the process through which two variables are related, while a moderating variable (or moderator ) affects the strength and direction of that relationship.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. These variables are important to consider when studying complex correlational or causal relationships between variables.

Including these variables can also help you avoid or mitigate several research biases , like observer bias , survivorship bias , undercoverage bias , or omitted variable bias

Table of contents

What’s the difference, mediating variables, moderating variables, other interesting articles, frequently asked questions about mediators and moderators.

You can think of a mediator as a go-between for two variables. For example, sleep quality (an independent variable ) can affect academic achievement (a dependent variable) through the mediator of alertness. In a mediation relationship, you can draw an arrow from an independent variable to a mediator and then from the mediator to the dependent variable.

In contrast, a moderator is something that acts upon the relationship between two variables and changes its direction or strength. For example, mental health status may moderate the relationship between sleep quality and academic achievement: the relationship might be stronger for people without diagnosed mental health conditions than for people with them.

In a moderation relationship, you can draw an arrow from the moderator to the relationship between an independent and dependent variable.

Prevent plagiarism. Run a free check.

A mediator is a way in which an independent variable impacts a dependent variable. It’s part of the causal pathway of an effect, and it tells you how or why an effect takes place.

If something is a mediator:

• It’s caused by the independent variable.
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

Mediation analysis is a way of statistically testing whether a variable is a mediator using linear regression analyses or ANOVAs .

In full mediation , a mediator fully explains the relationship between the independent and dependent variable: without the mediator in the model, there is no relationship.

In partial mediation , there is still a statistical relationship between the independent and dependent variable even when the mediator is taken out of a model: the mediator only partially explains the relationship.

You use a descriptive research design for this study. After collecting data on each of these variables, you perform statistical analysis to check whether:

• Socioeconomic status predicts parental education levels,
• Parental education levels predicts child reading ability,
• The correlation between socioeconomic status and child reading ability is greater when parental education levels are taken into account in your model.

A moderator influences the level, direction, or presence of a relationship between variables. It shows you for whom, when, or under what circumstances a relationship will hold.

Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds. For example, while social media use can predict levels of loneliness, this relationship may be stronger for adolescents than for older adults. Age is a moderator here.

Moderators can be:

• Categorical variables such as ethnicity, race, religion, favorite colors, health status, or stimulus type,
• Quantitative variables such as age, weight, height, income, or visual stimulus size.
• years of work experience predicts salary, when controlling for relevant variables,
• gender identity moderates the relationship between work experience and salary.

This means that the relationship between years of experience and salary would differ between men, women, and those who do not identify as men or women.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .

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Mediator Variable / Mediating Variable: Simple Definition

Types of Variables >

A mediator variable explains the how or why of an (observed) relationship between an independent variable and its dependent variable .

In a mediation model, the independent variable cannot influence the dependent variable directly, and instead does so by means of a third variable, a ‘middle-man’.

In psychology, the mediator variable is sometimes called an intervening variable . In statistics, an intervening variable is usually considered to be a sub-type of mediating variable. However, the lines between the two terms are somewhat fuzzy, and they are often used interchangeably.

Mediator Variable Examples

A mediator variable may be something as simple as a psychological response to given events . For example, suppose buying pizza for a work party leads to positive morale and to the work being done in half the time.

• Pizza is the independent variable,
• Work speed is the dependent variable,
• The mediator, the middle man without which there would be no connection, is positive morale .

Although we may observe a definite effect on work speed when and if pizza is bought, the pizza itself does not have the power to affect work rates: only by affecting morale of the workers can it make an actual difference.

Full Mediation and Partial Mediation

Full mediation is when the entire relationship between the independent & dependent variables is through the mediator variable. If you take away the mediator, the relationship disappears. Since the real world is a complicated place with many interactions, this is less common than partial mediation.

Partial mediation happens when the mediating variable is only responsible for a part of the relationship between independent & dependent variables. If the mediating variable is eliminated, there will still be a relationship between the independent and dependent variables; it just won’t be as strong.

Mediational Hypotheses

Mediational hypotheses, by definition, include full (complete) mediation. In other words, the independent variable has zero effect on the dependent variable; the causal relationship depends entirely on the mediator.

Baron and Kenny’s Four Steps

Baron and Kenny (1986), Judd and Kenny (1981), and James and Brett (1984) outlined the following steps to identify the mediational hypothesis. If the steps are met, then variable M is said to completely mediate the X-Y relationship. The steps are

• Show that a the independent variable (X) is correlated with the mediator (M).
• Demonstrate that the dependent variable (Y) and M are correlated .
• Demonstrate full mediation on the process. The effect of X on Y, controlling for M (i.e. controlling for paths a and b in the image at the top of this page), should be zero. If the results for this step are anything but zero, then there is partial mediation.

The authors state that three regression analyses are needed:

• X as the predictor variable and M as the outcome variable .
• X as the predictor variable and Y as the outcome variable.
• X and M as the predictor variables and Y as the outcome variable.

The procedures come with some hefty explanations, which are beyond the scope of this article. I recommend reading Baron and Keny’s original text. Or, as an excellent (plain English) alternative, read Paul Jose’s Doing Statistical Mediation and Moderation: Methodology in the Social Sciences , which includes Baron and Kenny’s steps starting on page 20.

Mediator versus Moderator variables. Retrieved from http://psych.wisc.edu/henriques/mediator.html on June 26, 2018. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. Retrieved June 26, 2018 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.169.4836&rep=rep1&type=pdf on June 26, 2018 Butler, Adam. Mediation Defined. Retrieved from https://sites.uni.edu/butlera/courses/org/modmed/moderator_mediator.htm on June 26, 2018

• Chapter 1: Introduction
• Chapter 2: Indexing
• Chapter 3: Loops & Logicals
• Chapter 4: Apply Family
• Chapter 5: Plyr Package
• Chapter 6: Vectorizing
• Chapter 7: Sample & Replicate
• Chapter 8: Melting & Casting
• Chapter 9: Tidyr Package
• Chapter 10: GGPlot1: Basics
• Chapter 11: GGPlot2: Bars & Boxes
• Chapter 12: Linear & Multiple
• Chapter 13: Ploting Interactions
• Chapter 14: Moderation/Mediation
• Chapter 15: Moderated-Mediation
• Chapter 16: MultiLevel Models
• Chapter 17: Mixed Models
• Chapter 18: Mixed Assumptions Testing
• Chapter 19: Logistic & Poisson
• Chapter 20: Between-Subjects
• Chapter 21: Within- & Mixed-Subjects
• Chapter 22: Correlations
• Chapter 23: ARIMA
• Chapter 24: Decision Trees
• Chapter 25: Signal Detection
• Chapter 26: Intro to Shiny
• Chapter 27: ANOVA Variance
• Download Rmd

Chapter 14: Mediation and Moderation

Alyssa blair, 1 what are mediation and moderation.

Mediation analysis tests a hypothetical causal chain where one variable X affects a second variable M and, in turn, that variable affects a third variable Y. Mediators describe the how or why of a (typically well-established) relationship between two other variables and are sometimes called intermediary variables since they often describe the process through which an effect occurs. This is also sometimes called an indirect effect. For instance, people with higher incomes tend to live longer but this effect is explained by the mediating influence of having access to better health care.

In R, this kind of analysis may be conducted in two ways: Baron & Kenny’s (1986) 4-step indirect effect method and the more recent mediation package (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014). The Baron & Kelly method is among the original methods for testing for mediation but tends to have low statistical power. It is covered in this chapter because it provides a very clear approach to establishing relationships between variables and is still occassionally requested by reviewers. However, the mediation package method is highly recommended as a more flexible and statistically powerful approach.

Moderation analysis also allows you to test for the influence of a third variable, Z, on the relationship between variables X and Y. Rather than testing a causal link between these other variables, moderation tests for when or under what conditions an effect occurs. Moderators can stength, weaken, or reverse the nature of a relationship. For example, academic self-efficacy (confidence in own’s ability to do well in school) moderates the relationship between task importance and the amount of test anxiety a student feels (Nie, Lau, & Liau, 2011). Specifically, students with high self-efficacy experience less anxiety on important tests than students with low self-efficacy while all students feel relatively low anxiety for less important tests. Self-efficacy is considered a moderator in this case because it interacts with task importance, creating a different effect on test anxiety at different levels of task importance.

In general (and thus in R), moderation can be tested by interacting variables of interest (moderator with IV) and plotting the simple slopes of the interaction, if present. A variety of packages also include functions for testing moderation but as the underlying statistical approaches are the same, only the “by hand” approach is covered in detail in here.

Finally, this chapter will cover these basic mediation and moderation techniques only. For more complicated techniques, such as multiple mediation, moderated mediation, or mediated moderation please see the mediation package’s full documentation.

1.1 Getting Started

If necessary, review the Chapter on regression. Regression test assumptions may be tested with gvlma . You may load all the libraries below or load them as you go along. Review the help section of any packages you may be unfamiliar with ?(packagename).

2 Mediation Analyses

Mediation tests whether the effects of X (the independent variable) on Y (the dependent variable) operate through a third variable, M (the mediator). In this way, mediators explain the causal relationship between two variables or “how” the relationship works, making it a very popular method in psychological research.

Both mediation and moderation assume that there is little to no measurement error in the mediator/moderator variable and that the DV did not CAUSE the mediator/moderator. If mediator error is likely to be high, researchers should collect multiple indicators of the construct and use SEM to estimate latent variables. The safest ways to make sure your mediator is not caused by your DV are to experimentally manipulate the variable or collect the measurement of your mediator before you introduce your IV.

Total Effect Model.

Basic Mediation Model.

c = the total effect of X on Y c = c’ + ab c’= the direct effect of X on Y after controlling for M; c’=c-ab ab= indirect effect of X on Y

The above shows the standard mediation model. Perfect mediation occurs when the effect of X on Y decreases to 0 with M in the model. Partial mediation occurs when the effect of X on Y decreases by a nontrivial amount (the actual amount is up for debate) with M in the model.

2.1 Example Mediation Data

Set an appropriate working directory and generate the following data set.

In this example we’ll say we are interested in whether the number of hours since dawn (X) affect the subjective ratings of wakefulness (Y) 100 graduate students through the consumption of coffee (M).

Note that we are intentionally creating a mediation effect here (because statistics is always more fun if we have something to find) and we do so below by creating M so that it is related to X and Y so that it is related to M. This creates the causal chain for our analysis to parse.

2.2 Method 1: Baron & Kenny

This is the original 4-step method used to describe a mediation effect. Steps 1 and 2 use basic linear regression while steps 3 and 4 use multiple regression. For help with regression, see Chapter 10.

The Steps: 1. Estimate the relationship between X on Y (hours since dawn on degree of wakefulness) -Path “c” must be significantly different from 0; must have a total effect between the IV & DV

Estimate the relationship between X on M (hours since dawn on coffee consumption) -Path “a” must be significantly different from 0; IV and mediator must be related.

Estimate the relationship between M on Y controlling for X (coffee consumption on wakefulness, controlling for hours since dawn) -Path “b” must be significantly different from 0; mediator and DV must be related. -The effect of X on Y decreases with the inclusion of M in the model

Estimate the relationship between Y on X controlling for M (wakefulness on hours since dawn, controlling for coffee consumption) -Should be non-significant and nearly 0.

2.3 Interpreting Barron & Kenny Results

Here we find that our total effect model shows a significant positive relationship between hours since dawn (X) and wakefulness (Y). Our Path A model shows that hours since down (X) is also positively related to coffee consumption (M). Our Path B model then shows that coffee consumption (M) positively predicts wakefulness (Y) when controlling for hours since dawn (X). Finally, wakefulness (Y) does not predict hours since dawn (X) when controlling for coffee consumption (M).

Since the relationship between hours since dawn and wakefulness is no longer significant when controlling for coffee consumption, this suggests that coffee consumption does in fact mediate this relationship. However, this method alone does not allow for a formal test of the indirect effect so we don’t know if the change in this relationship is truly meaningful.

There are two primary methods for formally testing the significance of the indirect test: the Sobel test & bootstrapping (covered under the mediatation method).

The Sobel Test uses a specialized t-test to determine if there is a significant reduction in the effect of X on Y when M is present. Using the sobel function of the multilevel package will show provide you with three of the basic models we ran before (Mod1 = Total Effect; Mod2 = Path B; and Mod3 = Path A) as well as an estimate of the indirect effect, the standard error of that effect, and the z-value for that effect. You can either use this value to calculate your p-value or run the mediation.test function from the bda package to receive a p-value for this estimate.

In this case, we can now confirm that the relationship between hours since dawn and feelings of wakefulness are significantly mediated by the consumption of coffee (z’ = 3.84, p < .001).

However, the Sobel Test is largely considered an outdated method since it assumes that the indirect effect (ab) is normally distributed and tends to only have adequate power with large sample sizes. Thus, again, it is highly recommended to use the mediation bootstrapping method instead.

2.4 Method 2: The Mediation Pacakge Method

This package uses the more recent bootstrapping method of Preacher & Hayes (2004) to address the power limitations of the Sobel Test. This method computes the point estimate of the indirect effect (ab) over a large number of random sample (typically 1000) so it does not assume that the data are normally distributed and is especially more suitable for small sample sizes than the Barron & Kenny method.

To run the mediate function, we will again need a model of our IV (hours since dawn), predicting our mediator (coffee consumption) like our Path A model above. We will also need a model of the direct effect of our IV (hours since dawn) on our DV (wakefulness), when controlling for our mediator (coffee consumption). When can then use mediate to repeatedly simulate a comparsion between these models and to test the signifcance of the indirect effect of coffee consumption.

2.5 Interpreting Mediation Results

The mediate function gives us our Average Causal Mediation Effects (ACME), our Average Direct Effects (ADE), our combined indirect and direct effects (Total Effect), and the ratio of these estimates (Prop. Mediated). The ACME here is the indirect effect of M (total effect - direct effect) and thus this value tells us if our mediation effect is significant.

In this case, our fitMed model again shows a signifcant affect of coffee consumption on the relationship between hours since dawn and feelings of wakefulness, (ACME = .28, p < .001) with no direct effect of hours since dawn (ADE = -0.11, p = .27) and significant total effect ( p < .05).

We can then bootstrap this comparison to verify this result in fitMedBoot and again find a significant mediation effect (ACME = .28, p < .001) and no direct effect of hours since dawn (ADE = -0.11, p = .27). However, with increased power, this analysis no longer shows a significant total effect ( p = .08).

3 Moderation Analyses

Moderation tests whether a variable (Z) affects the direction and/or strength of the relation between an IV (X) and a DV (Y). In other words, moderation tests for interactions that affect WHEN relationships between variables occur. Moderators are conceptually different from mediators (when versus how/why) but some variables may be a moderator or a mediator depending on your question. See the mediation package documentation for ways of testing more complicated mediated moderation/moderated mediation relationships.

Like mediation, moderation assumes that there is little to no measurement error in the moderator variable and that the DV did not CAUSE the moderator. If moderator error is likely to be high, researchers should collect multiple indicators of the construct and use SEM to estimate latent variables. The safest ways to make sure your moderator is not caused by your DV are to experimentally manipulate the variable or collect the measurement of your moderator before you introduce your IV.

Basic Moderation Model.

3.1 Example Moderation Data

In this example we’ll say we are interested in whether the relationship between the number of hours of sleep (X) a graduate student receives and the attention that they pay to this tutorial (Y) is influenced by their consumption of coffee (Z). Here we create the moderation effect by making our DV (Y) the product of levels of the IV (X) and our moderator (Z).

3.2 Moderation Analysis

Moderation can be tested by looking for significant interactions between the moderating variable (Z) and the IV (X). Notably, it is important to mean center both your moderator and your IV to reduce multicolinearity and make interpretation easier. Centering can be done using the scale function, which subtracts the mean of a variable from each value in that variable. For more information on the use of centering, see ?scale and any number of statistical textbooks that cover regression (we recommend Cohen, 2008).

A number of packages in R can also be used to conduct and plot moderation analyses, including the moderate.lm function of the QuantPsyc package and the pequod package. However, it is simple to do this “by hand” using traditional multiple regression, as shown here, and the underlying analysis (interacting the moderator and the IV) in these packages is identical to this approach. The rockchalk package used here is one of many graphing and plotting packages available in R and was chosen because it was especially designed for use with regression analyses (unlike the more general graphing options described in Chapters 8 & 9).

3.3 Interpreting Moderation Results

Results are presented similar to regular multiple regression results (see Chapter 10). Since we have significant interactions in this model, there is no need to interpret the separate main effects of either our IV or our moderator.

Our by hand model shows a significant interaction between hours slept and coffee consumption on attention paid to this tutorial (b = .23, SE = .04, p < .001). However, we’ll need to unpack this interaction visually to get a better idea of what this means.

The rockchalk function will automatically plot the simple slopes (1 SD above and 1 SD below the mean) of the moderating effect. This figure shows that those who drank less coffee (the black line) paid more attention with the more sleep that they got last night but paid less attention overall that average (the red line). Those who drank more coffee (the green line) paid more when they slept more as well and paid more attention than average. The difference in the slopes for those who drank more or less coffee shows that coffee consumption moderates the relationship between hours of sleep and attention paid.

4 References and Further Reading

Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

Cohen, B. H. (2008). Explaining psychological statistics. John Wiley & Sons.

Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological methods, 15(4), 309.

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological methods, 7(1), 83.

Nie, Y., Lau, S., & Liau, A. K. (2011). Role of academic self-efficacy in moderating the relation between task importance and test anxiety. Learning and Individual Differences, 21(6), 736-741.

Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). Mediation: R package for causal mediation analysis.

15 Mediating Variable Examples

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A mediating variable is a factor that explains the process through which an independent variable affects a dependent variable.

Here is a scholarly definition from Veronica Hefner (2017):

“A mediating variable is a variable that links the independent and the dependent variables , and whose existence explains the relationship between the other two variables.  A mediating variable is  also known as a mediator variable or an intervening variable.”

For example, in a study exploring the link between exercise and mental well-being, self-esteem might serve as a mediating variable, meaning that exercise boosts self-esteem, which then enhances mental well-being. It is that hidden ‘middle step’.

Mediating Variable Examples

1. the link between social media usage and loneliness.

Independent Variable: Social media usage Dependent Variable: Feelings of loneliness Mediating Variable: Quality and frequency of face-to-face interactions

If social media usage reduces the amount or quality of face-to-face time with others, it can lead to feelings of loneliness. Therefore, the relationship between extensive social media usage and feelings of loneliness might be mediated by the diminished quality and frequency of in-person interactions.

2. The Link Between Physical Activity and Mental Health

Independent Variable: Physical activity Dependent Variable: Improved mental health Mediating Variable: Endorphin release

When an individual engages in physical activity, the body releases endorphins, which are known as “feel-good” hormones. These endorphins play a significant role in enhancing mood and reducing feelings of anxiety and depression. Therefore, the positive relationship between physical activity and improved mental health might be mediated by the release of endorphins.

3. The Link Between Sleep Duration and Academic Performance

Independent Variable: Sleep duration Dependent Variable: Academic performance Mediating Variable: Cognitive function and attention span

Adequate sleep duration is crucial for optimal cognitive functioning and attention span. When students get adequate sleep, their cognitive abilities like memory, decision-making, and problem-solving are enhanced, leading to better academic performance. Thus, the relationship between sleep duration and academic performance might be mediated by improvements in cognitive function and sustained attention span.

4. The Link Between Job Satisfaction and Employee Turnover

Independent Variable: Job satisfaction Dependent Variable: Employee turnover Mediating Variable: Organizational commitment

Employees who are satisfied with their job are more likely to develop a stronger commitment to their organization. This commitment often results in greater loyalty and a decreased likelihood to leave the company. Therefore, the relationship between job satisfaction and reduced employee turnover might be mediated by the increased sense of organizational commitment.

5. The Link Between Dietary Habits and Physical Health

Independent Variable: Dietary habits Dependent Variable: Physical health Mediating Variable: Nutrient intake

If someone consistently consumes a balanced diet, they intake essential nutrients that promote good health. The relationship between dietary habits and physical health might be mediated by the level of essential nutrients consumed, ensuring proper body function and preventing deficiencies.

6. The Link Between Classroom Environment and Student Engagement

Independent Variable: Classroom environment Dependent Variable: Student engagement Mediating Variable: Student’s perception of safety and belonging

A positive and inclusive classroom environment can make students feel safe and like they belong. When students perceive that they are in a safe environment where they are valued, they are more likely to engage actively in learning. Thus, the relationship between the classroom environment and student engagement might be mediated by the student’s feelings of safety and belonging.

7. The Link Between Work-Life Balance and Employee Burnout

Independent Variable: Work-life balance Dependent Variable: Employee burnout Mediating Variable: Stress levels

Employees with a poor work-life balance often experience heightened stress levels due to the overlapping demands of their job and personal life. Elevated stress levels over extended periods can lead to feelings of burnout. Therefore, the relationship between work-life balance and employee burnout might be mediated by the levels of stress an employee experiences.

8. The Link Between Urban Green Spaces and Mental Well-being

Independent Variable: Presence of urban green spaces Dependent Variable: Mental well-being Mediating Variable: Frequency of nature interactions

When urban areas have more green spaces, residents tend to interact more frequently with nature, either by walking, exercising, or simply spending time in these areas. These interactions with nature have been shown to reduce stress and increase feelings of relaxation. Therefore, the relationship between the presence of urban green spaces and mental well-being might be mediated by the frequency of nature interactions.

9. The Link Between Employee Training and Job Performance

Independent Variable: Employee training Dependent Variable: Job performance Mediating Variable: Skill acquisition and competence

Regular and quality employee training sessions equip employees with new skills and enhance their competence in their roles. As they become more skilled and competent, their performance at their job tends to improve. Thus, the relationship between employee training and job performance might be mediated by the level of skill acquisition and competence achieved.

10. The Link Between Plant Ownership and Reduced Stress

Independent Variable: Plant ownership Dependent Variable: Reduced stress Mediating Variable: Increased interaction with nature and nurturing behavior

Caring for plants allows individuals to interact with nature even in indoor environments. Additionally, the act of nurturing plants and seeing them grow can be therapeutic and rewarding. These interactions and behaviors can lead to relaxation and a reduction in stress levels. Therefore, the relationship between plant ownership and reduced stress might be mediated by the increased interaction with nature and the nurturing behavior associated with plant care.

11. The Link Between Music Lessons and Cognitive Development

Independent Variable: Music lessons Dependent Variable: Cognitive development Mediating Variable: Development of discipline and concentration

Engaging in music lessons often requires students to practice regularly, fostering discipline. Additionally, mastering an instrument necessitates concentration and focus. These attributes can positively impact other areas of life, including academic pursuits. Thus, the relationship between music lessons and cognitive development might be mediated by the enhanced discipline and concentration developed through musical practice.

12. The Link Between Outdoor Play and Physical Health in Children

Independent Variable: Outdoor play Dependent Variable: Physical health in children Mediating Variable: Physical activity levels

Children who engage in outdoor play are often more physically active than those who spend more time indoors, as they run, jump, climb, and engage in other physical activities. This increased level of physical activity is essential for cardiovascular health, muscle development, and overall physical well-being. Therefore, the relationship between outdoor play and physical health in children might be mediated by the levels of physical activity they engage in.

13. The Link Between Personal Financial Management and Life Satisfaction

Independent Variable: Personal financial management Dependent Variable: Life satisfaction Mediating Variable: Financial security and reduced monetary stress

Individuals who effectively manage their finances tend to achieve a higher degree of financial security. This security can alleviate stress and anxiety related to monetary concerns, leading to a more content and satisfied life. Thus, the relationship between personal financial management and life satisfaction might be mediated by the sense of financial security and reduced monetary stress achieved through effective financial practices.

14. The Link Between Reading Habits and Vocabulary Size

Independent Variable: Reading habits Dependent Variable: Vocabulary size Mediating Variable: Exposure to diverse words and contexts

Individuals who read regularly encounter a wide variety of words in different contexts. This repeated exposure enhances their vocabulary as they come across and internalize new words. Therefore, the relationship between reading habits and vocabulary size might be mediated by the degree of exposure to diverse words and contexts through reading.

15. The Link Between Community Involvement and Personal Well-being

Independent Variable: Community involvement Dependent Variable: Personal well-being Mediating Variable: Sense of belonging and purpose

Engaging with and contributing to one’s community can foster a sense of belonging and purpose. Feeling connected and knowing that one’s actions positively impact others can lead to enhanced personal well-being. Thus, the relationship between community involvement and personal well-being might be mediated by the heightened sense of belonging and purpose derived from active community participation.

Mediating vs Moderating vs Confounding Variables

Mediating, moderating, and confounding variables are three of the most common types of ‘ third variable ‘. They are similar in that they need to be observed or controlled in order to better understand the relationship between the independent and dependent variables (Stapel & van Beek, 2015).

However, the three differ in important ways.

Let’s start with some definitions:

• Mediating Variables: These explain the process through which an independent variable influences a dependent variable.
• Moderating Variables : These influence the strength or direction of the relationship between an independent and a dependent variable (Nestor & Schutt, 2018).
• Confounding Variables : These are external factors that, if not controlled, can cause a false perception of a relationship between the independent and dependent variables (Boniface, 2019).

The table below shows how they differ:

Boniface, D. R. (2019).  Experiment Design and Statistical Methods For Behavioural and Social Research . CRC Press. ISBN: 9781351449298.

Hefner, V. (2017). Variables, Moderating Types . In Allen, M. (Ed.) The SAGE Encyclopedia of Communication Research Methods . SAGE Publications.

Nestor, P. G., & Schutt, R. K. (2018). Research Methods in Psychology: Investigating Human Behavior . SAGE Publications.

Scharrer, E., & Ramasubramanian, S. (2021).  Quantitative Research Methods in Communication: The Power of Numbers for Social Justice . Taylor & Francis.

Stapel, B. & van Beek, R.J. (2015). Confounders, moderators and mediators. In Mellenbergh, G. J., & Adèr, H. J. (Eds.). Advising on Research Methods: Selected Topics 2014 . Johannes van Kessel Advising.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Number Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Word Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Outdoor Games for Kids
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 50 Incentives to Give to Students

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Research Methods Course Pack

Chapter 10 moderating, mediating, and confounding variables, 10.1 more than the iv and the dv.

In this section, we’ll expand our understanding of variables in the study. So far, we have discussed three types of variables:

Independent variable (IV): The variable that is implied (quasi-experiment, non-experiment) or demonstrated to be (experiment) the cause of an effect. When there is a manipulation, the variable that is manipulated is the IV.

Dependent variable (DV): The variable that is implied or demonstrated to be the outcome.

Confounding variable: Also called a nuisance variable or third variable. This is a third variable that causes a change in both the IV and the DV at the same time. To borrow an example, we might observe a correlation between ice cream consumption and snake bites. We might wonder if eating ice cream causes snake bites on the basis of this result. Although this seems ridiculous, it’s easier for us to make these sorts of conclusions when the variables are psychological constructs (for example, personality and job outcomes). In this example, the weather is a confounding variable. When the weather is warmer, ice cream consumption (it’s warm) and snake bites (people go on hikes) increase.

From this example, you might wonder if other factors matter, such as the location (regions with lots of snakes versus regions with fewer). Some of these other variables may affect the DV in other ways, such as by weakening the relationship between the IV and the DV. Therefore, confounding variables are one type of extraneous variable. Extraneous variables include anything we have not included in our study.

Some extraneous variables are not likely to affect anything. In this example, the gender of people buying ice cream probably does not affect their likelihood of snake bites. Other extraneous variables can affect the relationship we are trying to observe in our study. Whenever you design a study, an important step is to stop and consider “what else could be affecting this relationship?” When you do this, you will brainstorm a list of possible confounding and extraneous variables. Then, you’ll decide if the variables are likely to affect the relationship of interest. If they are, then usually you can redesign your study to avoid them.

To summarize: A study is essentially a search to identify and explain relationships between IVs and DVs. When claims about the relationship between an IV and DV are true, the claim has internal validity.

Next, we will explore two more complex relationships between variables that develop when we add a second IV to our model.

10.2 Moderating Variables: Interaction Effects

Interactions are also called moderated relationships or moderation. An interaction occurs when the effect of one variable depends on the value of another variable. ** For example, how do you increase the sweetness of coffee? Imagine that sweetness is the DV, and the two variables are stirring (yes vs no) and adding a sugar cube (yes vs no).

We diagram a moderated relationship using this notation:

Diagram of a moderated relationship with IV 2 and IV 1 interacting to affect DV

And, when we have group means for every condition, we can see the impact of these two factors (factor is a fancy word for IV) in a table:

When is the coffee sweet? Stirring alone does not change the taste of the coffee. Adding a sugar cube alone also doesn’t change the taste of the coffee, since the sugar will just sink to the bottom. It’s only when sugar is added, and the coffee is stirred that it tastes sweet.

We can say there is an interaction between adding sugar and stirring coffee. The effect of the stirring depends on the value of another variable (whether or not sugar is added).

10.3 Some Terminology

When more than one IV is included in a model, we are using a factorial design. Factorial designs include 2 or more factors (or IVs) with 2 or more levels each. In the coffee example, our design has two factors (stirring and adding sugar), each with two levels.

In factorial designs (i.e., studies that manipulate two or more factors), participants are observed at each level of each factor. Because every possible combination of each IV is included, the effects of each factor alone can be observed. We also get to see how these factors impact each other. We say this design is fully crossed because every possible combination of levels is included.

10.4 Main Effects

A main effect is the effect of one factor. There is one potential main effect for each factor.

In this example, the potential main effects are stirring and adding sugar. To find the main effects, find the mean of each column (i.e., add the two numbers and divide by 2). If there are differences in these means, there is a significant main effect for one of the factors. Next, find the mean of each row (add going across and divide by 2). If there are differences in these row means, then there is a main effect for the other factor.

In our example, we see two main effects. Adding a sugar cube (mean of 50) differs from not adding sugar (mean of 0). That’s the first main effect. The second is stirring; stirring (mean of 50) differs from not stirring (mean of 0).

10.5 Simple Effects

When an interaction effect is present, each part of an interaction is called a simple effect. To examine the simple effects, compare each cell to every other cell in the same row. Next, compare each cell to ever other cell in the same column. Simple effects are never diagonal from each other.

In our example, we see a simple effect as we go from Stir+Sugar to NoStir+Sugar. There is no simple effect between Stir+NoSugar and NoStir+NoSugar (both are 0). What makes this an interaction effect is that these two simple effects are different from one another.

On the vertical, there is a simple effect from Stir+Sugar to Stir+NoSugar. There is no simple effect from NoStir+Sugar to NoStir+NoSugar (both are 0). Again, this is an interaction effect because these two simple effects are different.

10.6 Interaction Effect

When there is at least one (significant) simple effect that differs across levels of one of the IVs (as demonstrated above), then you can say there is an interaction between the two factors. In a two-way ANOVA, there is one possible interaction effect. We sometimes show this with a multiplication symbol: Sugar*Stir. In our example, there is an interaction between sugar and stirring.

In summary: An interaction effect is when the impact of one variable depends on the level of another variable.

Interaction effects are important in psychology because they let us explain the circumstances under which an effect occurs. Anytime we say that an effect depends on something else, we are describing an interaction effect.

10.7 Mediators and Mediated Relationships

A mediated relationship is a chain reaction; one variable causes another variable (the mediator), which then causes the DV. Please forgive another silly example; I am including it to keep the example as simple as possible. Here is how we diagram it:

This is a totally different situation that the previous one. The first variable is a preference for sweetness; do you like sweet foods and beverages? If participants prefer sweetness, then they will add more sugar. If they don’t prefer sugar in their coffee, then they will add less (or no) sugar. Thus, preference for sweetness is an IV that causes a change in the mediator, adding sugar. Finally, adding sugar is what causes the coffee to taste sweet. Any time we can string together three variables in a causal chain, we are describing a mediated relationship.

In summary: A mediated relationship occurs when one variable affects another (the mediator), and that variable (the mediator), affects something else.

Mediated relationships are important in psychology because they let us explain why or how an effect happens. The mediator is the how or the why. Why do participants who prefer sweetness end up with sweeter coffee? It is because they added sugar.

• cognitive sophistication
• tolerance of diversity
• exposure to higher levels of math or science
• age (which is currently related to educational level in many countries)
• social class and other variables.
• For example, suppose you designed a treatment to help people stop smoking. Because you are really dedicated, you assigned the same individuals simultaneously to (1) a "stop smoking" nicotine patch; (2) a "quit buddy"; and (3) a discussion support group. Compared with a group in which no intervention at all occurred, your experimental group now smokes 10 fewer cigarettes per day.

• There is no relationship among two or more variables (EXAMPLE: the correlation between educational level and income is zero)
• Or that two or more populations or subpopulations are essentially the same (EXAMPLE: women and men have the same average science knowledge scores.)

• the difference between two and three children = one child.
• the difference between eight and nine children also = one child.
• the difference between completing ninth grade and tenth grade is  one year of school
• the difference between completing junior and senior year of college is one year of school
• In addition to all the properties of nominal, ordinal, and interval variables, ratio variables also have a fixed/non-arbitrary zero point. Non arbitrary means that it is impossible to go below a score of zero for that variable. For example, any bottom score on IQ or aptitude tests is created by human beings and not nature. On the other hand, scientists believe they have isolated an "absolute zero." You can't get colder than that.

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Baron & Kenny’s Procedures for Mediational Hypotheses

Mediational hypotheses are the kind of hypotheses in which it is assumed that the affect of an independent variable on a dependent variable is mediated by the process of a mediating variable and the independent variable may still affect the independent variable. In other words, in mediational hypothesis, the mediator variable is the intervening or the process variable.  The mediational hypothesis assumes the complete mediation in the variables

The term complete mediation in mediational hypothesis means that the independent variable does not at all affect the dependent variable after the mediator variable has controlled it.  The mediation model involved in mediational hypothesis is a causal model.

Baron & Kenny’s procedures describes the analyses which are required for testing various mediational hypothesis.

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The first step involved in Baron & Kenny’s procedures is that the researcher must be shown that the initial variable is being correlated with the outcome variable.  In other words, the first step in Baron & Kenny’s procedures involves the establishment of an effect which may be mediated.

The second step involved in Baron & Kenny’s procedures is that the researcher must be shown that the initial variable is being correlated with the mediator. In other words, the second step in Baron & Kenny’s procedures involves treating the mediator variable as an outcome variable.

The third step in Baron & Kenny’s procedures involves an establishment of the correlation between the mediator variable and the outcome variable.  In this step of Baron & Kenny’s procedures, there exists correlation between the mediator and the outcome variable because they both are caused due to the initial variable.  In other words, in Baron & Kenny’s procedures, the initial variable must be controlled while establishing the correlation between the two other variables.

The next step in Baron & Kenny’s procedures involves the establishment of the complete mediation across the variables.  This establishment in the last step of Baron & Kenny’s procedures can only be achieved if the affect of the initial variable over the outcome variable while controlling for mediator variable is zero.

If all four steps of Baron & Kenny’s procedures are met, then the data is consistent with the mediational hypothesis.  If, however, only the first three steps of Baron & Kenny’s procedures are satisfied, then partial mediation is observed in the data.

The researcher should keep in mind that if the steps involved in Baron & Kenny’s procedures are completely satisfied, it still does not imply that the mediation has occurred as there are other less plausible models that are consistent with the data.

The mediator variable in mediation hypothesis can be caused by the outcome variable.  This happens when the initial variable is a manipulated variable—then it cannot be caused either by the mediator or the outcome in mediation hypothesis.  However, since both the mediator and the outcome variables are not manipulated, they may cause each other in mediational hypothesis.

It is always sensible to swap the mediator variable and the outcome variable and have the outcome cause the mediator in mediational hypothesis.

Related Pages:

• Mediator Variable

Dr Martin Lea

• 5. Example of a Basic Test of Mediation

The simplest mediation analysis involves a single independent variable, a dependent variable, and a hypothesized mediator.

The unmediated model is represented by the direct effect of x on y, quantified as c.

However, the effect of X on Y may be mediated by a process, or mediating variable M.

Partial mediation is the case in which the path from X to Y is reduced in absolute size but is still different from zero when the mediator is controlled for.

In path analysis, an independent variable is called an exogenous variable. Any variable that is predicted by another variable acts as a dependent variable and is called an endogenous variable.

Example: Effects of visual anonymity on attraction to the group

Here's an example of a simple mediation analysis relating to my own research.

In this example I test some ideas about deindividuation theory that derive from a social identity approach to group behaviour.

My basic hypothesis was that visual anonymity among group members increases attraction to the group.

I test this by regressing group attraction onto a measure of visual anonymity, and found that visual anonymity significantly affected group attraction with a Beta of .40.

However, I also hypothesized that this effect was mediated by the extent to which group members perceived or categorized themselves as part of the group – a self-categorization variable. I next test this mediated effect and measure the change in the direct effect, with the following results….

As you can see from the path diagram, visual anonymity had an effect on self-categorization, which in turn had an effect on group attraction. At the same time, the direct effect of anonymity on group attraction (which was previously .50) is now reduced to just about zero, after the mediated effect is taken into account. In other words the hypothesized mediated effect accounted for just about all of the effect of anonymity on group attraction.

Note that self-categorization is considered here to be a mediator, not a moderator. That is, my model was not that visual anonymity increases group attraction when group members self-categorize as part of the group.

Instead, my model was that anonymity increases group attraction because it increased self-categorization.

In other words, my model addressed how and why anonymity achieves its effect, not when it achieves its effect.

NEXT: 6. Mediation Analysis: Procedures and Tests

Statistics Training: Introduction to Path Analysis

• 9. Causal Steps to Establish Mediation: Steps 3 & 4
• 8. Causal Steps to Establish Mediation: Step 2
• 7. Causal Steps to Establish Mediation: Step 1
• 6. Mediation Analysis: Procedures and Tests
• 4. Example of the Difference between Moderation and Mediation
• 3. Moderation and Mediation Explained
• 2. A Quick Review of Regression
• 13. Sobel's Test of Significant Mediation
• 12. Testing for Significant Mediation
• 11. An Example of a Mediator Acting as a Suppressor
• 10. Barron and Kenny (1986) Criteria for Mediation
• 1. What is Path Analysis?

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DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders

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Shuoyang Wang, Yuan Huang, DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders, Biostatistics , Volume 25, Issue 3, July 2024, Pages 818–832, https://doi.org/10.1093/biostatistics/kxad037

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Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.

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ORIGINAL RESEARCH article

Servant leadership and project success: the mediating roles of team learning orientation and team agility.

• 1 School of Economics and Management, Liaoning University of Technology, Jinzhou, Liaoning, China
• 2 SolBridge International School of Business, Woosong University, Daejeon, Republic of Korea

Drawing from social learning theory, this study aims to explore the mediating effects of team learning orientation and team agility on the relationship between servant leadership and project success in the context of construction projects. Based on data collected from 306 construction project members in China, the findings reveal that servant leadership exerts a positive influence on project success. Additionally, servant leadership significantly enhances both team learning orientation and team agility, which in turn contribute to project success. Furthermore, the results demonstrate the serial and parallel mediating roles of team learning orientation and team agility between servant leadership and project success. Theoretical and practical implications were also provided based on the findings.

1 Introduction

Traditionally, projects have been viewed as merely technical systems with an emphasis placed on employing sophisticated methodologies and tools ( Imam and Zaheer, 2021 ). However, despite such techniques, numerous projects have continued encountering failures ( Ellahi et al., 2022 ). The primary causes of project failures in China are often associated with management issues rather than technical aspects ( Zhou et al., 2019 ). An evolving perspective in the project management literature has brought attention to the significant influence of human behavior and dynamics as pivotal success factors, rather than just technical aspects ( Jugdev and Müller, 2005 ). This shifting focus underscores leadership’s vital role, with studies showing 80% of project failures attributed to ineffective leadership ( Fareed et al., 2023 ). Accordingly, different leadership styles have been increasingly investigated on project success (PS), including transformational leadership ( Aga et al., 2016 ), ethical leadership ( Bhatti et al., 2021 ), servant leadership ( Ellahi et al., 2022 ), and shared leadership ( Imam and Zaheer, 2021 ).

Among the leadership styles, servant leadership (SL), characterized by its focus on people, holds particular potential for project contexts marked by complexity and uncertainty. Existing studies support that leaders centered on people tend to exhibit greater effectiveness in ensuring the successful delivery of projects ( Thamhain, 2004 ; Behrendt et al., 2017 ), including the specific context of China ( Chen and Tjosvold, 2014 ). In addition, SL has been linked to favorable outcomes, including intrinsic motivation ( Xue et al., 2022 ), work engagement ( Bao et al., 2018 ), and emotional intelligence ( Miao et al., 2021 ). These elements are empirically supported to contribute to PS ( Ellahi et al., 2022 ; Malik et al., 2022 ). In the meta-analysis by Lee et al. (2020) , SL shows incremental validity compared to other leadership styles like authentic, ethical, and transformational leadership. Thus, SL acts as an effective leadership style in the context of project-based organizations characterized by complexity and uncertainty.

There is a growing focus on how SL contributes to PS within the project management literature ( Bilal et al., 2020 ; Nauman et al., 2022a ). SL is demonstrated to impact PS directly and indirectly. Specifically, some mediators between SL and PS have been identified by previous studies, including work engagement and project work withdrawal ( Nauman et al., 2022b ), emotional intelligence and job stress ( Malik et al., 2022 ), team motivation and team effectiveness ( Ellahi et al., 2022 ). However, research gaps remain in incorporating team-level process variables like team learning orientation (TLO) and team agility (TA) into the research framework and exploring their mediating mechanisms in the relationship between SL and PS. Malik et al. (2022) suggest that more studies need to explore the mediating mechanisms between SL and PS. Similarly, Nauman et al. (2022a) suggest that future work could incorporate intervening variables in this relationship. To respond to these calls, this study posits that TLO and TA serve as mediators between SL and PS. Exploring TLO and TA underlying SL and PS holds significance for the following reasons.

The swift environmental changes present both challenges and chances for successfully managing projects ( Ali et al., 2021 ). For example, the environments surrounding construction projects, both internal and external, tend to be changing and not stable ( Love et al., 2002 ). The increasing dynamism necessitates work teams to proactively engage in continuous learning and self-improvement to effectively respond to changes ( Pearsall and Venkataramani, 2015 ). TLO, marked by a shared understanding that values active learning, serves as a critical mechanism, motivating members to participate in learning behaviors ( Chiu et al., 2021 ). It is worth noting that TLO significantly influences positive team processes, including team task reflexivity ( Wang and Lei, 2018 ), team planning processes ( Pearsall and Venkataramani, 2015 ), and adaptive behaviors ( Bunderson and Sutcliffe, 2003 ), all of which are essential for PS. Moreover, leadership has been identified as an effective predictor of learning orientation ( Coad and Berry, 1998 ). As a result, by exploring the mediating role of TLO between SL and PS, this study aims to offer deeper insights into the mechanisms through which SL impacts PS.

In addition, TA is another effective response to the rapidly changing environment ( Krüger, 2023 ). TA assists teams in swiftly adapting to uncertainties during projects ( Conforto et al., 2014 ), constituting a fundamental component for long-lasting success ( Denning, 2013 ). Empirical studies have connected TA to constructive team outcomes like performance ( Liu et al., 2015 ) and shared mental models ( Krüger, 2023 ). Moreover, academics have explored agility determinants, identifying leadership as a potent one ( Akkaya and Tabak, 2020 ). Thus, by investigating the mediating role of TA between SL and PS, this study provides a better understanding of how to effectively leverage the influence of SL in dynamic environments to achieve PS.

Furthermore, the complex and dynamic nature of projects implies that TLO and TA may serially mediate the link between SL and PS. As noted by Hayes (2018) , investigating serial mediation is critically important for delineating the distinct effects of causation. Servant leaders prioritize the needs of subordinates and facilitate subordinates’ growth to their full potential ( Graham, 1991 ). Moreover, servant leaders focus on stewardship motivates teams to question old assumptions and seek new knowledge ( Yoshida et al., 2014 ). These processes nurture TLO which focuses on acquiring new skills and knowledge. As noted by Edmondson (1999) , team learning behaviors fostered team flexibility which is the prerequisite for TA. Ultimately, despite uncertainty, TA to adapt and respond to changes enhances team adaptation, which in turn helps the project reach its objectives ( Vázquez-Bustelo et al., 2007 ). Examining this serial mediation of TLO to TA between SL and PS will provide nuanced understanding of how servant leaders can translate their impact and improve project delivery.

To address these questions, drawn from social learning theory (SLT). This study employed Structural Equation Modeling (SEM) to analyze the correlation among the variables based on a survey of 306 construction project members in China. The data gathered from this survey will be analyzed to investigate the proposed model, as depicted in Figure 1 . The paper aims to offer substantial practical insights and contribute valuable theoretical perspectives on the mechanisms through which SL exerts its influence on PS.

Figure 1 . Conceptual Framework.

2 Theoretical underpinning

The SLT was first proposed by Albert Bandura in the 1960s and 1970s ( Bandura and Walters, 1977 ). This theory posits that human behavior is largely learned through observation, modeling, and vicarious reinforcement ( Bandura, 1999 ). It highlights that individuals can learn new behaviors by observing others with a process termed observational learning or modeling ( Liden et al., 2014 ). Through this process of observation and modeling, individuals can anticipate the potential outcomes of their own actions in similar situations, thereby adjusting and regulating their behaviors accordingly ( Davis and Luthans, 1980 ).

Drawing from SLT, servant leaders act as role models for followers to observe and emulate ( Sendjaya et al., 2008 ). This modeling effect can be particularly powerful in shaping organizational team climates, norms, and practices ( Argote, 2011 ). For example, the modeling influence of servant leaders can foster positive knowledge-sharing climates and service climates ( Hunter et al., 2013 ; Song et al., 2015 ), creating a beneficial team environment for team learning. Moreover, the influences that servant leaders exert on followers manifest collectively and iteratively, subsequently stimulating positive changes within teams ( Russell and Gregory Stone, 2002 ). In addition, SLT consistently finds support for the modeling of behavior, both through laboratory experiments and practical applications, regarding the influence of leaders’ behaviors on their subordinates’ ( Eva et al., 2019 ; Nauman et al., 2022a ). Therefore, SLT provides a useful theoretical underpinning for understanding how servant leaders can model desired behaviors and competencies to facilitate team performance.

3 Literature review

3.1 servant leadership.

The expression “servant leadership” was originally developed by Greenleaf (1970) , and SL has garnered increasing attention from scholars in recent years ( Gardner et al., 2020 ). Servant leaders refer to leaders who “place the needs of their subordinates before their own needs and center their efforts on helping subordinates grow to reach their maximum potential and achieve optimal organizational and career success” ( Liden et al., 2008 , p. 163). Different from traditional leadership styles that highlight the leader’s power and authority, SL emphasizes the leader’s responsibility to serve by prioritizing the requirements of subordinates ( Bilal et al., 2020 ).

Servant leadership is different from other value-based leaderships ( Schowalter and Volmer, 2023 ). Unlike transformational leadership, which prioritizes organizational goals over followers’ needs, SL accentuates fulfilling the psychological necessities of subordinates with greater weight, designating it as a principal objective ( van Dierendonck et al., 2014 ). In addition, SL is characterized by a propensity for altruistic behavior, driven by the motive to serve others, rather than solely focusing on being authentic in interpersonal interactions like authentic leadership ( Eva et al., 2019 ). Furthermore, comparing to ethical leadership, where leaders typically influence followers to be ethically conscientious and act morally ( Ko et al., 2018 ), servant leaders provide more attention to specific directions for followers, an aspect that is relatively absent in the approach of ethical leaders.

In an analytical review conducted by Eva et al. (2019) , they critically examined 16 extant instruments assessing SL, evaluating their scale development and validation. The measurement tool called SLBS-6 developed by Sendjaya et al. (2019) emerged as noteworthy for its meticulous construction and validation processes. SLBS-6 authentically reflects the conceptualization of Greenleaf (1970) and Graham (1991) that spirituality is the core of SL, and followers are impacted by leaders’ humility ( Eva et al., 2019 ). Moreover, recent empirical studies have also confirmed that the SLBS-6 instrument has demonstrated satisfactory psychometric properties in terms of reliability and validity ( Khan et al., 2022 ). Furthermore, the original SLBS-35 developed by Sendjaya et al. (2008) is demonstrated that the multiple dimensions of this measurement are most accurately viewed as one higher construct ( Sendjaya and Cooper, 2011 ). Thus, given that this study examines the overall effect of SL without distinguishing between dimensions, SLBS-6 has been adopted as the measurement for SL.

Extant empirical research has substantiated the positive connection between SL and anticipated outcomes such as enhanced performance and organizational citizenship behaviors ( Schowalter and Volmer, 2023 ). Moreover, accumulating evidence suggests that SL nurtures antecedent conditions conducive to PS, including fostering project identification ( Nauman et al., 2022b ), cultivating a collaborative culture ( Nauman et al., 2022a ), and bolstering team motivation ( Ellahi et al., 2022 ). Consequently, SL appears particularly well-suited for project-based organizational contexts. However, the specific mechanisms through which SL is translated into improved project outcomes require further examination.

3.2 Team learning orientation

In recent decades, scholars have extensively delved into the exploration of learning orientation ( Hakala, 2011 ; Gemici and Zehir, 2021 ). Learning orientation refers to “a concern for, and dedication to, developing one’s competence” ( Gong et al., 2009 , p. 765). It stands as a critical foundation for nurturing learning competence, a trait that is prominently displayed and interwoven across various organizational levels, including both individuals and collectives. Notably, Senge (1990) posits that teams, instead of individuals, represent the basic component of learning within organizations. Moreover, Khedhaouria et al. (2017) also underscored the presence of team learning and emphasized the crucial need for exploration at the team level.

The TLO refers to “an emergent group climate characterized by team members’ shared understanding that continual learning and self-development is an essential team objective” ( Chiu et al., 2021 , p. 190). It plays a crucial role in determining team members’ learning behaviors ( Edmondson, 1999 ). Teams that do not engage in appropriate learning activities tend to be less effective at both individual and team levels of performance ( Savelsbergh et al., 2012 ). TLO lies in its ability to motivate members to undertake various learning actions, thereby facilitating team adaptability and effectiveness ( Chiu et al., 2021 ).

Social learning theory emphasizes the procedure by which individuals obtain knowledge and skills by observing, imitating, and interacting with others ( Bandura and Walters, 1977 ). The conducive climate fostered by TLO facilitates this learning process effectively ( Chiu et al., 2021 ). Thus, the interactions within teams are expected to yield favorable outcomes. Empirical studies have also consistently demonstrated that TLO represents a robust antecedent of positive team behaviors, including task reflexivity ( Wang and Lei, 2018 ), team planning processes ( Pearsall and Venkataramani, 2015 ), and adaptive behaviors ( Bunderson and Sutcliffe, 2003 ). Additionally, scholars have claimed that the development of efficient intra-project learning can advance project-based organizations’ competitiveness ( Jugdev and Mathur, 2013 ). Nevertheless, the exploration of antecedents for TLO remains limited.

3.3 Team agility

Team agility originated from software development to improve handling of changing requirements, productivity, and business alignment ( Brown and Eisenhardt, 1995 ; Campanelli and Parreiras, 2015 ). TA is defined as “a team’s ability to respond to unpredictable changes in proper ways and to take advantage of these changes as opportunities” ( Liu et al., 2015 , p. 297). It represents the manifestation of agility at the team level, enabling organizations to translate their agile capabilities into action. Research shows that agile teams respond to change, take action, and make decisions more quickly than traditional teams, and TA is regarded as an emerging pillar of project management that can enable sustained success ( Krüger, 2023 ).

Although TA originated from the software development domain, its underlying principles and practices have demonstrated potential applicability across various project-based industries and contexts beyond software. Several studies have explored the adoption of agile methodologies in non-software projects, such as construction ( Loforte Ribeiro and Timóteo Fernandes, 2010 ; Kashikar et al., 2016 ), product development ( Lill and Wald, 2021 ), and marketing ( Kalaignanam et al., 2021 ). The core tenets of TA, including responsiveness to change, customer collaboration, iterative delivery, and self-organization, can be valuable in any project environment characterized by uncertainty, complexity, and evolving requirements ( Conforto et al., 2014 ). Notably, the uncertain and dynamic nature of construction projects aligns well with the strengths of TA, making it a potentially suitable approach for addressing the unpredictable conditions inherent in such project environments ( Layton et al., 2020 ).

In addition, TA plays a crucial role in fostering PS. The notion of TA derives from the agile principles and values outlined in the Agile Manifesto ( Beck et al., 2001 ). The Agile Manifesto emphasizes the primacy of interactions over processes and tools, as well as the necessity of responding to change rather than rigidly adhering to predetermined plans. These core values underscore the importance of teamwork and flexibility, which are fundamental elements that can facilitate the achievement of successful project outcomes ( Zaman et al., 2019 ; Ali et al., 2021 ). Moreover, agile practices have been demonstrated to enhance trust and teamwork among team members, rendering them particularly well-suited for complex, uncertain projects characterized by evolving requirements ( McHugh et al., 2012 ; Qureshi et al., 2014 ). By embracing the principles of TA, project teams can cultivate an environment that promotes adaptability, collaboration, and continuous improvement, thereby increasing their capacity to navigate uncertainties, respond to changes, and ultimately contribute to the realization of PS.

Since TA is a relatively new approach for project teams ( Conforto et al., 2016 ), studies on TA remain limited. Scholars have explored organizational agility antecedents, identifying leadership as an influential factor ( Akkaya and Tabak, 2020 ; AlNuaimi et al., 2022 ). Additionally, empirical studies have linked TA to positive team outcomes like performance ( Liu et al., 2015 ) and shared mental models ( Krüger, 2023 ). Based on these findings, this study argues that TA could potentially act as a mediator between SL and PS.

3.4 Project success

Project success was originally defined as completing a project within the expected schedule, budget, and quality ( Atkinson, 1999 ). However, PS’s definition has evolved over time, with different organizations and scholars using varying criteria. The Project Management Institute (PMI) expanded the definition to include meeting stakeholders’ diverse concerns and expectations ( Project Management Institute, 2000 ). While the British Association for Project Management stated that satisfying stakeholders’ needs should be included in PS ( Fareed and Su, 2022 ). Academically, Ika (2009) indicated that achieving strategic objectives and sponsor satisfaction are two critical factors for PS. Joslin and Müller (2015) argue that the anticipated project outcome should also be included with the definition of PS.

Although a consensus definition remains elusive, understanding of PS has broadened from the traditional constraints of schedule, budget and quality to a multifaceted success incorporating diverse perspectives ( Pollack et al., 2018 ). The review of Joslin and Müller (2015) found that Pinto and colleagues’ frameworks most comprehensive for measuring PS. Aga et al. (2016) later adapted and expanded this measurement. Thus, based on the study from Aga et al. (2016) , this study employs the composite measurement to assess PS.

4 Hypothesis development

4.1 sl and ps.

Social learning theory posits that followers learn behaviors by perceiving and copying role models ( Bandura and Walters, 1977 ). In the context of projects, the leader acts as a salient role model for team members. Servant leaders prioritize their teams’ growth and wellbeing over personal interests ( Eva et al., 2019 ). By showing voluntary subordination, responsible morality, and transforming influence, servant leaders demonstrate service-oriented conduct ( Sendjaya et al., 2019 ). Project teams observe and internalize similar servant leader behaviors. They become more motivated to emulate the altruism, kindness, and community stewardship exhibited by their leader ( Krog and Govender, 2015 ). Via the procedure of social learning, servant leaders shape team dynamics to be more collaborative and committed to shared goals. As supported by Raziq et al. (2018) and Nauman et al. (2022a) , collaboration culture and goal clarity have been examined to be effect predictors for PS. In addition, SL has been recognized as a determinant of positive outcomes. Ruiz-Palomino et al. (2023) found that SL improves team performance. Moreover, both Malik et al. (2022) and Nauman et al. (2022b) have provided evidence that SL has a positive effect on PS. Thus, the hypothesis is suggested:

H1 : SL positively influences PS.

4.2 SL and TLO

According to SLT, the behaviors exhibited by leaders impact and motivates subordinates’ actions, promoting the emulations of similar behavior across the organizational hierarchy ( Mayer et al., 2012 ). SL demonstrates that the central role of a leader is to serve the subordinates ( Nauman et al., 2022b ). They prioritize assisting the needs of subordinates by open communication and transcend their own interests to facilitate subordinates’ growth to their potential ( Graham, 1991 ). By modeling openness to feedback, reflection on failures, and striving for self-betterment, servant leaders demonstrate a group climate with continual learning and self-development. Moreover, servant leaders focus on stewardship motivates teams to question old assumptions and seek new knowledge ( Yoshida et al., 2014 ). In this scenario, team members will gradually develop learning-focused behaviors. Their emphasis on growth and reflection establishes norms that learning is valuable. As a result, SL fosters an environment optimized for continuous team learning and development. A number of studies recommend that group climate where learning is emphasized can nurture TLO ( Bunderson and Sutcliffe, 2003 ). In addition, SL has shown a positive connection with organizational learning ( Goestjahjanti et al., 2022 ) and team-based learning ( Grobler and Flotman, 2021 ). Thus, the hypothesis is proposed:

H2a : SL positively influences TLO.

4.3 TLO and PS

The TLO decides the extent and significance of members’ learning behaviors ( Edmondson, 1999 ). A high level learning orientation motivates engagement in uncovering others’ interests and developing plans to optimize collective performance ( Pearsall and Venkataramani, 2015 ). When members observe their colleagues actively engaging in learning processes and accept exploration as valued group norms, they become more likely to pursue novel endeavors ( Rosenthal and Zimmerman, 2014 ). TLO also enables adaptation to changing project-based environments through continual work process optimization and outcome improvements ( Wang and Lei, 2018 ). Moreover, research indicates TLO positively impacts team reflexivity and performance ( Wang and Lei, 2018 ), goal mental models and planning processes ( Pearsall and Venkataramani, 2015 ). Thus, the hypothesis is suggested:

H2b : TLO positively influences PS.

4.4 The mediating role TLO

Servant leaders exhibit openness to learning, a willingness to admit mistakes, and a focus on the collective interest ( Eva et al., 2019 ). According to SLT ( Bandura and Walters, 1977 ), project members emulate these learning-focused behaviors from servant leaders. Additionally, servant leaders cultivate a learning-oriented climate by questioning old assumptions and seeking new knowledge ( Yoshida et al., 2014 ). Members engaged in this process recognize the value and importance of learning. TLO emerges when all team members collectively value, seek out, and reflect on knowledge, skills, and feedback to enhance team performance ( Bunderson and Sutcliffe, 2003 ). Consequently, TLO encourages behaviors such as information sharing, seeking help, expressing concerns, and reflecting on processes. These behaviors enable teams to identify problems, build knowledge, and improve ( Edmondson, 1999 ). By enhancing team knowledge, coordination, and performance, TLO contributes to superior project outcomes and success. Therefore, the hypothesis is suggested:

H2c : TLO plays a positive mediating role between SL and PS.

4.5 SL and TA

The SL may enhance TA through the provision of followers’ empowerment and autonomy. Servant leaders authorize their followers by delegating significant obligations, granting them to cope with situations autonomously, and actively encouraging independent decision-making ( Chiniara and Bentein, 2016 ). This process helps team members feel valued and motivated to adapt to changing demands. Furthermore, servant leaders inherently highlight the fulfillment of followers’ needs by fostering open communication and transcending self-interest to support followers’ growth to their fullest potential ( Sendjaya et al., 2019 ). By developing individuals to their maximum capacity, servant leaders equip team members with the knowledge, skills, and confidence to take initiative and adjust quickly as required by the team ( Greenleaf, 2002 ). Scholars have also identified team empowerment (2015) and autonomy ( Werder and Maedche, 2018 ) as crucial predictors of TA. In summary, SL facilitates empowerment and autonomy, thereby enhancing TA. Therefore, the hypothesis is proposed:

H3a : SL positively influences TA.

Agile teams exhibit a high degree of TA, and this has demonstrated positive impacts on PS through various mechanisms. Firstly, agile teams possess the ability to promptly respond to shifting priorities and changes in project scope, acknowledging change as an inherent aspect of the project lifecycle rather than resisting it ( Werder, 2016 ). This adaptability enables them to meet evolving customer needs and align project goals accordingly. Secondly, agile teams emphasize frequent inspection and adaptation in short iterations, facilitating accelerated learning and timely course corrections ( Krüger, 2023 ). Early identification and resolution of issues contribute to preventing escalation. Regular evaluation of progress and results through constant feedback loops enhances the likelihood of achieving PS. Thirdly, agile teams prioritize people and communications over processes and tools, fostering greater autonomy and ownership among team members ( Beck et al., 2001 ). Bourgault et al. (2008) and Imam (2021) have supported that autonomy is a critical antecedent for PS. As a result, the agility exhibited by project teams exerts a positive influence on project outcomes. Therefore, the hypothesis is proposed:

H3b : TA positively influences PS.

4.7 The mediating role of TA

Servant leaders exhibit behaviors such as empowering followers through delegation of responsibility, encouraging autonomous decision-making, and actively supporting followers’ personal growth ( Chiniara and Bentein, 2016 ). Team members feel valued and motivated to adapt to changing demands ( Sendjaya et al., 2019 ), and they build confidence to take action quickly, which is required by the team ( Greenleaf, 2002 ). By equipping team members in this way, servant leaders develop a high-level agility in the team. In turn, an agile team is better able to survive in response to shifting priorities and cope with uncertainties characterized by projects. Furthermore, TA has been empirically associated with enhanced team performance and positive outcomes ( Liu et al., 2015 ; Werder, 2016 ). Agile teams outperform non-agile teams on various project outcomes, including meeting scope, schedule, and customer requirements ( Conforto et al., 2014 ). In summary, SL enables TA by empowerment and prioritization on their growth and autonomy. In turn, agile teams achieve PS through their ability to take adaptations swiftly, learn and adjust. Therefore, the hypothesis is suggested:

H3c : TA plays a positive mediating role between SL and PS.

Teams with high lever TLO exhibit openness to new ideas, and a willingness to challenge assumptions. TLO further enables a collective unit to accustom to evolving contexts, persistently refine procedures and operations, and ascertain novel and superior approaches for accomplishing team goals ( Bunderson and Sutcliffe, 2003 ). This learning mindset has been linked to greater TA. Edmondson (1999) found that team learning behaviors fostered team flexibility and adaptability. By activating the mechanisms of SLT, positive behaviors are duplicated from servant leaders and new knowledge is disseminated among members, learning-oriented teams are better equipped to adjust strategies and meet changing demands. In rapidly evolving environments, learning teams are able to quickly perceive cues, re-evaluate assumptions, and find innovative solutions ( Gong et al., 2009 ). They accumulate experience and insights that enable them to adjust adeptly ( Jyothi and Rao, 2012 ). In contrast, teams fixed in their ways of thinking and operating tend to lack the agility to adapt and perform well. In summary, teams that emphasize continuous learning and growth develop the adaptability needed in dynamic contexts. Fostering TLO can positively impact TA and performance. In addition, empirical study confirms that TLO is positively associated with TA-related factors, including adaptive behaviors ( Bunderson and Sutcliffe, 2003 ), team planning processes ( Pearsall and Venkataramani, 2015 ), and team task reflexivity ( Wang and Lei, 2018 ). Thus, the hypothesis is proposed:

H4 : TLO positively influences TA.

H5 : TLO and TA play sequential mediating roles between SL and PS.

5.1 Sample and procedure

The sample for this study encompassed 306 individuals engaged in construction projects in China. Participants represented diverse roles within these construction roles, including civil engineers, quantity surveyors, and MEP (Mechanical, Electrical, and Plumbing) engineers who contributed data through the survey. Data acquisition was facilitated through two avenues, including the China State Construction Association (CSCA) and the alumni network.

The data collection process was conducted using an online survey platform called Wenjuanxing 1 , a widely used professional tool in China. The initial questionnaire was designed based on the research objectives and a comprehensive literature review. The questionnaire consisted of three main parts. The first part provided an introduction to the survey, explaining its purpose, the confidentiality of responses, and instructions for completion. The second part focused on demographic questions, gathering information about the respondents’ background, such as their ages, years of experience, and educational qualifications. The third part contained the variable measurement scales, which included questions related to the key constructs of the study.

To ensure the validity and reliability of the questionnaire, a rigorous adaptation process was employed. First, the items utilized in previous relevant studies were translated from English into Chinese. Then, a group of six members, including five graduate students and one professor with expertise in construction management, carefully reviewed the questionnaire to prevent any inconsistencies. Additionally, pilot tests were conducted with a small sample of 20 respondents. The questionnaire was revised according to their feedback before being administered to the target respondents.

The process of gathering data began with the recruitment of 30 individuals who had participated in construction projects in China, each with more than 2 years of experience. To achieve a varied sample, the initial recruitment consisted of 11 quantity surveyors, 12 civil engineers, and 7 MEP engineers. These individuals were selected and invited via the CSCA and alumni associated with engineering management and cost disciplines. Following this, a snowball sampling method was utilized, wherein each initial participant forwarded the questionnaire link to other eligible participants. The questionnaire provided detailed instructions, emphasizing the confidentiality of responses. Participants were encouraged to circulate the survey among colleagues meeting the standards. The survey link was distributed through various channels, including social media and email. To ensure the uniqueness of our respondents and to prevent any individual from submitting the survey more than once, this study implemented IP address tracking and browser cookie checks. These technical safeguards effectively prevented any duplication of responses, thereby maintaining the integrity and uniqueness of our data collection process.

After around 3 months of spreading, 343 questionnaires were gathered, with 306 responses deemed valid for analysis. Thirty-seven responses were excluded based on the following criteria. Firstly, responses with a completion time of less than 120 s were discarded. This criterion was based on the average completion time observed from 15 students majoring in Engineering Management and Engineering Cost. Their educational background, which includes familiarity with industry-specific language and concepts, enables a precise evaluation of the time needed to complete the questionnaire. These students were part of a separate preliminary time trial and were not included in the final study population. Secondly, responses with identical answers across all questions were deemed uncommon and were excluded. Thirdly, illogical responses, such as reporting an age of 25 with over 12 years of experience, were also excluded. Table 1 presents the demographic information of the remaining participants.

Table 1 . Demographics information.

5.2 Measurement

Participants, unless otherwise specified, utilized a five-point Likert scale, ranging from 1, indicating “Strongly Disagree,” to 5, denoting “Strongly Agree.” The subsequent section delineates measurements employed in this study:

Servant leadership employed SLBS-6 from Sendjaya et al. (2019) . A sample item was the following: “My project manager uses power in service to others, not for his or her ambition.” (Cronbach’s α  = 0.887).

Team learning orientation was measured by five items adapted from Bunderson and Sutcliffe (2003) . Participants in this study utilized a five-point Likert scale, ranging from 1, indicating “Very Low Extent,” to 5, denoting “Very High Extent.” A sample item was the following: “Our team looks for opportunities to develop new skills and knowledge” (Cronbach’s α  = 0.908).

Team agility measurement utilized four items from Liu et al. (2015) . A sample item is as follows: “Our team’s responsiveness to changing organizational conditions is timely.” (Cronbach’s α  = 0.814).

Project success was measured using seven items from Aga et al. (2016) . The questionnaire included items such as: “The project was completed on time.” (Cronbach’s α  = 0.892).

In line with prior studies, the analysis incorporated demographic elements such as gender, professional expertise, age, and educational background, acknowledging their potential impact on respondents’ evaluations. Furthermore, team size and project duration were also taken into account ( Barrick et al., 2007 ; Aga et al., 2016 ).

6 Analysis and results

RStudio Version 2023 was utilized to analyze the data. SEM techniques were utilized to evaluate the proposed model and examine the postulated assumptions. Confirmatory factor analysis (CFA) was implemented to authenticate the measurement patterns denoting the variables within the overarching structural equation model. Data Analysis employs the SEM methodology, a sophisticated statistical approach that integrates factor analysis with multiple regression analysis. This technique is adept at scrutinizing the intricate relationships between observable variables and the underlying latent constructs, all within the context of a theoretical framework. Furthermore, the application of bootstrap methods alongside SEM provides a robust mechanism for assessing the hypothesized relationships, thereby ensuring a comprehensive and rigorous examination of the conceptual framework.

6.1 Reliability and validity

Cronbach’s alpha ( α ) coefficients were considered to evaluate reliability and internal consistency. The value of α above 0.7 is generally considered indicative of satisfactory reliability ( Vaske et al., 2017 ). As displayed in Table 2 , the α coefficients for all constructs met this threshold. Composite reliability (CR) was also examined to confirm internal consistency. CR values above 0.7 are favorable ( Bagozzi and Yi, 1988 ). The CR values for SL, TLO, TA, and PS, displayed in Table 2 , all exceeded 0.7, demonstrating satisfactory internal consistency. Additionally, all items’ loadings surpassed 0.5, denoting adequate item reliability. Furthermore, the average variance extracted (AVE) for each variable is more than 0.5, as seen in Table 2 , indicating that the constructs adequately captured their intended concepts ( Fornell and Larcker, 1981 ). In addition, as shown in Table 2 , the value of AVE surpassed the correlations for all constructs, suggesting satisfactory discriminant validity and that each construct measured a distinct underlying concept. Moreover, Table 3 displays the model fit indices, all of which surpass the recommended thresholds as suggested by Doğan and Özdamar (2017) . The results indicated the acceptable adequacy of the model.

Table 2 . Reliability and validity.

Table 3 . Model fitness.

6.2 Common method bias

Two approaches were utilized to avoid the common method bias (CMB). First, the initial principal component elucidated 34.847% of total variance, falling under the 50% threshold indicative of substantial CMB ( Podsakoff et al., 2003 ). In addition, the proposed model demonstrated a significantly improved fit from the single-factor model (Δ χ 2  = 1354.626, Δdf = 6, p  < 0.001), providing evidence against CMB ( Guo et al., 2016 ). As shown in Table 4 .

Table 4 . Measurement model fit indices.

6.3 Hypothesis testing

This study employed the Bootstrap method with 5,000 samples to conduct path analysis and assess hypotheses. Direct effects of the model are summarized in Table 5 , with a detailed breakdown provided subsequently. Table 5 outlines the outcomes for Hypotheses 1, 2a, 2b, 3a, 3b, and 4. These results reveal significant support for Hypotheses 1 ( β  = 0.224, p  < 0.05), 2a ( β  = 0.498, p  < 0.001), 2b ( β  = 0.361, p  < 0.001), 3a ( β  = 0.224, p < 0.05), 3b ( β  = 0.392, p  < 0.001), and 4 ( β  = 0.233, p  < 0.05), indicating their statistical significance. Additionally, our analysis reveals that none of the control variables were validated. As shown in Table 5 .

Table 5 . Structural model results.

Table 6 shows the endorsement of Hypothesis 2c, suggesting the mediating effect of TLO between SL and PS. The 95% confidence interval (CI) for the coefficients (0.096, 0.279) excludes zero, thus affirming TLO’s mediating role between SL and PS. Similarly, Hypothesis 3c, proposing the mediation of TA is demonstrated. Examination indicates that the 95% CI for the coefficients (0.022, 0.174) does not encompass zero, thereby confirming TA’s mediating role between SL and PS. Furthermore, Hypothesis 5, which postulates the sequential mediating effects of TLO and TA, is supported. The 95% CI (0.015, 0.086) does not include zero, showing this sequential mediation.

Table 6 . Analysis of mediating effects.

7 Discussion

Based on SLT, this study examined the direct and indirect relationships between SL and PS in Chinese construction projects. As predicted, a positive correlation between SL and PS was found. This finding confirms the notion that leadership is a fundamental factor for PS ( Ellahi et al., 2022 ). Moreover, this finding is aligned with previous studies but in different contexts. For instance, Ellahi et al. (2022) found this relationship in the context of software projects, while Malik et al. (2022) demonstrated this relationship in non-governmental organizations, and Nauman et al. (2022b) supported this relationship in vocational training organizations. However, these findings were primarily confirmed in eastern countries like Pakistan. As suggested by Zhang et al. (2021) , the relationships between SL and its outcomes are moderated by cultural factors such as traditionality. Both Pakistan and China are Eastern societies that emphasize collectivistic values ( Hofstede, 2001 ; Basabe and Ros, 2005 ). SL is likely to exhibit more positive effects in a collectivistic cultural environment. Collectivism values cooperation, mutual respect, and concern for others, which aligns with the core tenets of SL ( Sendjaya and Pekerti, 2010 ). In collectivistic cultures, people tend to prioritize the collective interests of the team over individual goals. Servant leaders, by exemplifying service, and empowering team members, can enhance team cohesion, which is conducive to collaboration among project members and, consequently, to achieve PS ( Kyriazis et al., 2017 ). Thus, it is essential to note that the effects of SL may vary across different cultures ( Eva et al., 2019 ). Future research is encouraged to investigate the effects of SL across diverse cultural contexts.

Another finding of this study was the positive connection between TLO and PS in Chinese construction projects. This finding confirms the conclusion of Bunderson and Sutcliffe (2003) that team focus on learning can yield positive outcomes for team effectiveness. It also provides supportive evidence that TLO can promote favorable team outcomes, aligning with prior studies that have positioned TLO as an antecedent of team performance indicators, such as employee creativity ( Qian and Kee, 2023 ) and team goal mental models and team planning processes ( Pearsall and Venkataramani, 2015 ). The significance of TLO stems from its ability to motivate members to participate in various learning activities, thereby enhancing team effectiveness ( Chiu et al., 2021 ). In the dynamic and complex environment of construction projects, where teams frequently encounter challenges and uncertainties, a learning-oriented mindset can facilitate the acquisition, sharing, and application of new knowledge and skills, enabling teams to respond proactively to changes and contribute to PS.

Additionally, TA was found to have a positive relationship with PS in Chinese construction projects. This finding supports the existing literature that highlights the importance of TA in project management ( Conforto and Amaral, 2016 ). Conforto et al. (2014) contend that the existence of specific enablers for agile project management indicates the potential to extend the application of agile project management theories and practices beyond the software industry. The present study provides empirical evidence supporting the generalization of agile methods to industries other than software, such as the construction sector. Moreover, this finding suggests that TA is particularly critical in the Chinese construction context, where projects often involve intricate coordination among multiple stakeholders, complex regulatory frameworks, and fluctuating market conditions ( Tang et al., 2012 ). Agile teams can navigate these complexities more effectively, mitigating potential risks and capitalizing on favorable circumstances. Furthermore, the study by Liu et al. (2015) confirms that collectivism, a prominent cultural value in China, is conducive to TA. The emphasis on group cohesion, coordination, and responsiveness to environmental changes in collectivistic cultures aligns well with the context of Chinese construction projects.

Next, another finding is the positive mediation of TLO between SL and PS. It confirms the importance of team learning processes in translating the influence of leadership styles into improved team and organizational outcomes ( Bucic et al., 2010 ). Moreover, the results also support the argument that person-focused leaders foster team learning ( Koeslag-Kreunen et al., 2018 ). Specifically, our results indicate that SL fosters a learning-oriented mindset within teams, characterized by a shared commitment to continuous learning, knowledge sharing, and embracing challenges as opportunities for growth ( Liden et al., 2014 ). This collective learning orientation, in turn, contributes to project goals and objectives, acting as a critical mediating mechanism linking SL and PS.

Furthermore, TA was also found to be a positive mediator between SL and PS. This finding is consistent with previous studies that have emphasized the importance of TA in enhancing team effectiveness and performance, particularly in dynamic and rapidly changing environments ( Werder, 2016 ). In the context of construction projects, where teams often face intricate coordination challenges, complex regulatory frameworks, and fluctuating market conditions, the capability to swiftly adapt to changes and respond effectively to unforeseen circumstances is paramount. Servant leaders, by prioritizing the growth of team members, create a climate that fosters trust, empowerment, and humility ( Sousa and Van Dierendonck, 2016 ; Lee et al., 2020 ). This supportive climate enables teams to develop a heightened sense of agility, allowing them to reconfigure resources, adjust strategies, and coordinate actions efficiently in response to emerging challenges or opportunities, which in turn, engender PS.

Finally, perhaps most importantly, this is the first study finding that TLO and TA play sequential mediating effects between SL and PS. Despite TA is important for PS, both parallel and sequential mediation analyses revealed that the indirect effect through TLO is critically significant. In other words, compared to the enhancement of TA, the facilitation of TLO is a relatively more efficacious mechanism through which SL improves PS. This finding emphasizes the vital role of TLO, especially in agile teams. As Edmondson (1999) suggests, a team’s learning orientation is a fundamental enabler for agility, as it promotes team flexibility which is essential for teams to rapidly adapt to changing environments. The critical importance of TLO in mediating the SL-PS relationship could be attributed to the fact that a learning-oriented mindset not only facilitates the TA but also nurtures the ability to achieving PS. A team’s collective commitment to learning enables it to proactively identify and address challenges, adapt to changing circumstances, and leverage collective knowledge and expertise to drive project outcomes.

7.1 Theoretical implication

The findings offer several theoretical implications. First, the positive connection between TLO and PS in the context of construction projects extends the understanding of the role of team learning processes in driving project outcomes. This finding is in line with previous studies that have positioned TLO as an antecedent of team performance ( Bunderson and Sutcliffe, 2003 ; Wang and Lei, 2018 ), and further generalizes this relationship to construction projects.

Second, the positive relationship between TA and PS in the construction project context aligns with existing literature that highlights the importance of TA in enhancing team effectiveness and performance, particularly in dynamic environments ( Krüger, 2023 ). The study provides empirical evidence supporting the generalization of agile methods and practices beyond the software industry, extending their application to the construction sector.

Finally, the paper is the first study to demonstrate the sequential mediations of TLO and TA between SL and PS. This novel finding extends our understanding of the complex mechanisms through which SL influences project outcomes. The parallel mediating mechanisms of TLO and TA indicate that servant leaders can foster both a learning-oriented mindset and TA simultaneously, which in turn contribute to PS through distinct yet complementary pathways. This finding highlights the multifaceted nature of SL and its ability to positively influence multiple team processes concurrently, ultimately leading to improved project performance. In addition, the sequential mediating effects of TLO and TA provide insights into the potential sequential effects of SL on team processes and project outcomes. Specifically, this finding suggests that servant leaders may first nurture a learning-oriented mindset within teams, which then facilitates the development of TA, ultimately leading to enhanced PS. This sequential model offers a nuanced understanding of the relationships among leadership, team processes, and project performance, and suggests a potential causal chain through which SL exerts its influence.

7.2 Practical implication

This study also provides several practical implications. Firstly, the findings underscore the potential of SL to cultivate TLO and TA, ultimately contributing to PS. The identification of SL as a catalyst for TLO and TA suggests that organizations and project leaders can enhance project outcomes by adopting and promoting SL behaviors within their teams. Given that project managers often coordinate members with diverse specialties and cultures ( Bell and Kozlowski, 2002 ), SL appears particularly well-suited for this scenario. SL, characterized by a leader’s emphasis on serving others and facilitating their growth, aligns with the dynamic where team members possess specialized expertise. In such contexts, the project manager can leverage SL principles to foster a supportive and empowering environment, allowing team members to thrive and contribute their expertise fully. Therefore, embracing SL practices can lead to more effective project management, better utilization of team members’ skills, and improved overall project outcomes.

Additionally, the study highlights the importance of recognizing TA as a critical mediator between SL and PS. This finding underscores the need for project managers and organizations to actively foster TA within their project teams. Agile teams possess the ability to swiftly adapt to changes, reconfigure resources, and coordinate actions effectively, which is crucial for navigating the complexities and uncertainties inherent in project environments. By promoting practices that enhance TA, such as empowerment, cross-functional collaboration, and rapid decision-making, project managers can increase the likelihood of achieving PS. Moreover, it is crucial for project teams to reach a balance between agility and regulated processes. Project managers should establish clear guidelines and frameworks that provide structure while allowing for flexibility and adaptability. This balance can help harness the benefits of TA while ensuring adherence to project standards and best practices.

Furthermore, understanding the sequential mediating effects of TLO and TA between SL and PS offers a nuanced perspective for practitioners. The sequential mediating roles can inform the alignment of team processes with different project phases. For instance, during the initial stages of a project, emphasis can be placed on cultivating a learning-oriented mindset, while later stages may prioritize agile practices and rapid adaptation as the project progresses and evolves.

7.3 Limitations

This study possesses several limitations. Firstly, the use of cross-sectional data impedes the establishment of causal relationships. The observed correlations among SL, TLO, TA, and PS do not necessarily indicate a causal relationship. Future studies could embrace longitudinal data, enabling a more comprehensive examination. Secondly, using a single respondent to complete the questionnaire may introduce CMB. This bias occurs when measurements of both predictor and criterion variables are obtained from the same source, which can potentially influence the proposed relationships among the variables ( Podsakoff and Organ, 1986 ). However, two methods were employed to assess CMB, and the results indicate that CMB may not be a significant concern. The final limitation pertains to the single-country data collection. Cultural influences may impact the generalizability of these empirical findings. Future studies are encouraged to collect data across multiple countries, especially those with diverse cultural backgrounds.

8 Conclusion

This study sought to explore the mediating effects of TLO and TA between SL and PS in Chinese construction projects. The results show that TLO and TA act as both parallel and sequential mediators between SL and PS. The findings offer valuable insights for practitioners to achieve PS, emphasizing the importance of cultivating a learning-oriented mindset, promoting TA, and developing SL, particularly in Chinese construction projects.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institutional Review Board of SolBridge International School of Business. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because details about the study purpose and participant rights were clearly stated in the distributed questionnaire.

Author contributions

HH: Writing – original draft, Validation, Supervision, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. XZ: Writing – review & editing, Visualization, Investigation.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Youth Program L21CJL002 of the Liaoning Province Social Science Planning Fund Project, China.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: servant leadership, team learning orientation, team agility, project success, project management

Citation: Han H and Zhang X (2024) Servant leadership and project success: the mediating roles of team learning orientation and team agility. Front. Psychol . 15:1417604. doi: 10.3389/fpsyg.2024.1417604

Received: 15 April 2024; Accepted: 22 July 2024; Published: 01 August 2024.

Reviewed by:

Copyright © 2024 Han and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Huibin Han, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Examining the mediating effect of the rural economic dynamization between the socio-environmental heritage and sustainability of protected areas.

1. Introduction

2. literature review, 2.1. socio-environmental heritage (seh), 2.2. sustainability of protected areas (spa), 2.3. rural economic dynamization (red), 3. materials and methods, 3.1. design, 3.2. working unit, 3.3. sample size, 3.4. instrument, 3.5. procedure, 4.1. evaluation of the first- and second-order models, 4.2. evaluation of the structural model, 4.3. importance–performance map analysis (ipma), 5. discussion, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Compulsive citizenship behavior and turnover intention: a two-stage moderated mediation model of emotional exhaustion and mindfulness

• Published: 29 July 2024

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• Huai-Liang Liang   ORCID: orcid.org/0000-0002-0897-7778 1  

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This study aimed to investigate the moderating effect of mindfulness on the relationship between compulsive citizenship behavior (CCB) and emotional exhaustion, which, in turn, affects turnover intention. Specifically, this study developed and evaluated a two-stage moderated mediation model in which employee mindfulness moderates the pathway from CCB to turnover intention through emotional exhaustion, which is achieved by suppressing emotional and behavioral responses. To validate the proposed model, the author conducted two studies involving the measurement and manipulation of CCB and mindfulness. Study 1 employed a time-lag design to explore these relationships among 421 employees, whereas Study 2 extended this analysis to 592 employees through a scenario-based experimental laboratory study. Study 1 did not demonstrate the expected attenuation of the relationship between CCB and emotional exhaustion among individuals with higher trait mindfulness. Study 2 confirmed these findings through a scenario-based laboratory study that elucidated the critical moderating role of mindfulness in these relationships. This study also presents relevant theoretical and practical implications for future research.

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The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to restrictions e.g., their containing information that could compromise the privacy of research participants.

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Liang, HL. Compulsive citizenship behavior and turnover intention: a two-stage moderated mediation model of emotional exhaustion and mindfulness. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06341-6

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Guidelines for the Investigation of Mediating Variables in Business Research

David p. mackinnon.

Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA

Stefany Coxe

Amanda n. baraldi.

Business theories often specify the mediating mechanisms by which a predictor variable affects an outcome variable. In the last 30 years, investigations of mediating processes have become more widespread with corresponding developments in statistical methods to conduct these tests. The purpose of this article is to provide guidelines for mediation studies by focusing on decisions made prior to the research study that affect the clarity of conclusions from a mediation study, the statistical models for mediation analysis, and methods to improve interpretation of mediation results after the research study. Throughout this article, the importance of a program of experimental and observational research for investigating mediating mechanisms is emphasized.

This piece is the sixth in the Method Corner series featured by this Journal. This series focuses on some of the methodological issues encountered by business psychologists. Past pieces described aggregation of multidimensional constructs ( Johnson et al. 2011 ), methods to identify the importance of regression models ( Tonidandel and LeBreton 2011 ), polynomial regression ( Shanock et al. 2010 ), and method bias ( Conway and Lance 2010 ). The detection of mediators is also a methodological issue important to business psychology. Many theories in business research postulate a mediator ( M ) that transmits the effect of a predictor variable ( X ) to an outcome variable ( Y ) in a causal sequence such that X causes M and M causes Y. In more general terms, a mediating variable explains the process by which one variable causes another. Theories across many disciplines focus on mediating processes and many research questions lend themselves to these models. In intervention research, theory and prior empirical research determine which mediating variables are included as part of study design. If an intervention substantially changes a mediating variable that is causally related to an outcome, then a change in the mediator will produce a change in the outcome. For example, if organizational skills create more efficiency among employees, an employee program teaching organizational skills should increase organizational skills, resulting in greater employee efficiency.

Mediation theory is also applicable to studies that do not include an intervention. An observational variable can serve as a predictor or antecedent variable in a mediation model. For example, it has been suggested that the effects of psychological climate perceptions on performance are mediated by employee work attitudes ( Parker et al. 2003 ), where psychological climate does not represent an intervention but is an observed variable measured for each employee. Psychological climate is an observed variable and is not randomized, thus limiting conclusions regarding the causal nature of the mediating process. The lack of randomization makes it difficult to rule out alternative explanations of the relationship. Examples of alternative explanations include changes in employee work attitudes causing changes in psychological climate or that there is another variable causing changes in both psychological climate and employee work attitudes.

Since the classic articles on mediation by Alwin and Hauser (1975) , Judd and Kenny (1981) , James and Brett (1984) , and Baron and Kenny (1986) , thousands of articles have applied mediation analysis in many fields, including psychology (e.g., Fritz and MacKinnon 2007 ; Mackinnon et al. 2002 ), medicine ( Begg and Leung 2000 ), business ( Chiaburu and Byrne 2009 ; Hung et al. 2009 ), and many other fields (see MacKinnon 2008 ). The popularity of mediation analysis is growing because the method focuses on what is often the central scientific hypothesis: the process by which one variable affects another.

Mediators and Moderators and Confounders and Covariates

Before delving into the details of mediation analysis, we begin with definitions of several key terms that come into play when considering how three variables can be related. These potential relationships are important to understanding when mediation analysis is the appropriate choice for answering specific research questions. When considering the relationship of an independent variable ( X ) and a dependent variable ( Y ), an additional third variable ( Z ) may fill one of several roles. Each role for the third variable describes both a different theoretical model of the relationship between X , Y , and Z , as well as a different approach to the statistical analysis.

A third variable that is both unrelated to the predictor X and has little to no effect on the relationship between X and Y is called a covariate ; a covariate is not often of primary theoretical interest but is used to account for additional variation in the outcome Y. A third variable, Z , can be related to both X and Y in such a way that the inclusion of Z changes the relationship between X and Y. Such a variable is called a confounder , because it confounds or conceals the simple relation between X and Y (see Greenland and Morgenstern 2001 for information on confounders). A variable that is a moderator affects the direction and strength of the relationship between two variables such that the relationship between X and Y is different for varying levels of Z . A moderator is typically expressed as an interaction between the independent variable and the moderator, such that the effect of the independent variable on the dependent variable is conditional on the level of the moderator. A moderator may be a factor in an experimental manipulation with random assignment to varying levels (e.g., time between treatments) or a moderator may be a non-manipulated variable (e.g., age or gender). The understanding of a moderator effect is often a critical component to the generalizability of research findings to other populations, locations, and domains.

The focus of this article is on the third variable as a mediator variable. A simple mediation model is shown in Fig. 1 . A mediation relationship is one in which the independent variable causes the mediator which then causes the dependent variable ( Mackinnon 2008 ). Although variations of the definition of mediation exist in the literature (e.g., Holmbeck 1997 identifies terminological inconsistencies), we will assume a mediator to be a variable that transmits the effect of an independent variable to a dependent variable. Although the terms “mediator” and “mediated effect” will be used throughout this manuscript, other terms are used to describe these variables and effects in different areas of research. For example, the “mediated effect” is often referred to as the “indirect effect” because it represents the effect of the independent variable effect on the dependent variable effect via the mediator variable (i.e., indirectly rather than directly). The “mediator” is sometimes called the “intervening variable” because it is intermediate between the independent and the dependent variables.

Illustration of the mediation model using path diagrams

Though the primary focus of this article is mediation analysis, we feel obligated to spend some time comparing mediation and moderation effects. Both mediation effects and moderation effects are examined in psychological research with some frequency and involve a third variable. This often results in difficulties for researchers who are inexperienced in the nuances of these two types of effects; some may be confused about whether they should be performing a mediation analysis or not, while others may perform a mediation when their research question actually involves moderation (or vice versa). We would like to emphasize that determining whether to investigate a mediator effect versus a moderator effect depends entirely on the research question of interest. In general, moderators provide information on the circumstance under which effects are present , whereas mediators address the mechanisms by which an effect occurs . Mediation effects are exemplified by the question “How did it work?” because mediation examines the means by which the intervention affects outcomes. Moderation effects are exemplified by the question “Who did it work for?” because moderation examines which subgroups (e.g., boys vs. girls) show effects of the intervention on outcomes ( James and Brett 1984 ; Fairchild and MacKinnon 2008 ; MacKinnon 2011 ).

Mediation in Business Psychology

Theories across many substantive disciplines focus on mediating processes as explanations for how and why an antecedent variable is related to an outcome variable; business psychology is no exception. A casual review of the Journal of Business and Psychology found that between 2007 and 2010, over 30 articles purported to be addressing theoretical questions involving mediation or using mediation analysis. Of these, more than 20 articles cited classic mediation sources such as Baron and Kenny (1986) and Sobel (1982) .

Basic Mediation Model

A simple mediation model with one independent variable, X , one mediator, M , and one outcome variable, Y , provides information to investigate mediation by estimating three regression equations. The relationships between X , M , and Y are shown as path diagrams in Fig. 1 . Consider the study by Leach et al. (2009) , in which the relationship between meeting design characteristics ( design ) and perceived meeting effectiveness ( perception ) was mediated by attendees’ involvement during the meeting ( involvement ). In this example, design is the independent variable X , perception is the outcome variable Y , and involvement is the mediator M .

Equation (1) represents the relationship between the independent variable X and the dependent variable Y :

In terms of the example, this equation represents the relationship between design and perception , where the coefficient c represents the effect of design on perception , i is the intercept, and e 1 is the residual variance (i.e., the part of perception that is not explained by design ). Equation 2 represents the relationship between the independent variable X and the mediator M :

In the example, this equation represents the relationship between design and involvement . Equation 3 represents the somewhat more complex relationship between X , M , and Y :

This equation shows how perception can be predicted by both design and involvement . Since there are two predictors here, both c ′ and b are partial regression coefficients ; each regression coefficient is the effect of that predictor on the outcome, controlling for the effect of the other predictor. Using the example, the b coefficient is the effect of involvement on perception , controlling for design , and the c ′ coefficient is the effect of design on perception , controlling for involvement . There are several important assumptions of this single mediator model, including temporal precedence of the X → M → Y relationship and the assumption that no variables are omitted from the relationship; these assumptions, some of which are testable and some of which are not testable, are discussed later. As an aside, although we are presenting the mediation model in terms of three regression equations, regression is a special case of a structural equation model (SEM) and the methods described can often be done in either the regression or the structural equation model framework. However, structural equation modeling allows for more comprehensive modeling of measurement error, change over time, and multivariate dependent variables that are impossible or cumbersome with multiple regression analysis.

Decisions Prior to Mediation Analysis

Despite the extensive use of complex statistical modeling in the behavioral sciences, the quality of a research project is largely determined by the design decisions that are made before any analysis is done and even before the study is conducted. The conceptualization of a mediation analysis requires forethought about the relationships between the variables of interest and the theoretical meaning behind those relationships. Several other issues are important for researchers to consider prior to conducting a mediation analysis. Some of these decisions are common to all studies, but we will focus on the decisions that are of particular importance when planning a mediation analysis. In any study, a researcher must address the issues of manipulation versus observation, omitted variables that may be influencing results, reliability and validity of measures, and sample size to adequately detect effects. In addition, the theoretical task of choosing which variables will serve as mediators is critical. Table 1 summarizes some of the issues for consideration prior, during, and after mediation analysis.

Summary of issues before, during, and after mediation analysis

Randomization

Random assignment of subjects to experimental conditions is the gold standard for making causal inference about the relationship between two variables. In the case where X represents randomly assigned condition, the coefficients a and c represent causal effects under certain reasonable assumptions. The coefficients b and c ′ represent adjusted relations. Even though there is random assignment to experimental groups, the b and c ′ coefficients do not have a clear interpretation as causal effects because participants select their own value of the mediating variable. This ambiguity of self-selection to value of the mediator is a primary focus of modern causal inference approaches to mediation to be described later. Random assignment to the levels of X is common in many mediation studies but a second random assignment to the value of M (called “double randomization”) is rare and often difficult for ethical or logistical reasons. In double randomization studies, one randomized study evaluates the X to M relation and a second randomized study evaluates the M to Y relation adjusting for X ( MacKinnon 2008 ; MacKinnon and Pirlott 2011 ; Stone-Romero and Rosopa 2011 ; Spencer et al. 2005 ).

A second type of design to obtain some level of randomization of the mediating variable is called a blockage design. In this design, a manipulation is used to block or prevent the mediation process thereby demonstrating that the mediator was crucial ( MacKinnon 2008 ; Robins and Greenland 1992 ). If the blocking manipulation removes the mediation relation, this provides support for a mediational process. As an example of blockage design, return to the example in which the relationship between meeting design characteristics ( design ) and perceived meeting effectiveness ( perception ) was mediated by attendees’ involvement during the meeting ( involvement ). Using a blockage design, participants in the study may be assigned to a blocking treatment condition where deep involvement in the meeting was prevented (e.g., by a mildly distracting task or by not allowing communication with others at the meeting) in addition to the manipulation of the meeting design characteristics. If involvement is a mediator of the design-to-perception relationship, mediation effects should be related to the amount of involvement across groups and participants in the blockage treatment condition should not show as large of a mediated effect as participants in the control condition because the mediating process was blocked. A closely related type of design is the enhancement design which seeks to enhance (rather than eliminate as in the blockage design) the mediated effect in the treatment group. In the meeting perception example, participants in the study may be assigned to an enhancement treatment condition which creates even deeper involvement in the meeting (e.g., by telling them that a promotion will be offered to the person who learns the most from the meeting). If involvement is a mediator of the design to perception relationship, mediation effects should again be related to the amount of involvement across groups and participants in the enhancement treatment condition should show larger mediated effects than the participants in other examples (see other examples in MacKinnon 2008 ; Maxwell et al. 1986 ; Klesges et al. 1986 ).

Several options exist to strengthen causal arguments when randomization of X and/or M is not possible, including the selection of covariates before the study that may explain the X to M and the M to Y relations. Similarly, these covariates may be used in a propensity score model to address omitted variable explanations of mediated effects ( Coffman 2011 ; Jo et al. 2011 ). Instrumental variables may be used to estimate causal effects when randomization (particularly of X ) is not feasible ( MacKinnon 2008 , Chap. 13; Lockhart et al. 2010 ). In addition to statistical adjustments, experimental design methods such as the blockage and enhancement designs can strengthen causal interpretation by focusing on testing the consistency and specificity of mediation relations across different contexts, subgroups, and measures of the mediating and outcome variables ( MacKinnon and Pirlott 2011 ).

Omitted Variables

The term “ignorability” refers to the assumption that the relationship between two variables is unaffected by other variables (such as covariates, confounders, or moderators). Mediation analysis contains two major relationships that may be influenced by other variables: the X → M relationship and the M → Y relationship. Mediation therefore assumes a two part sequential ignorability assumption. There are many issues that arise in the causal interpretation of the single mediator model which stem from the two part sequential ignorability assumption ( Imai et al. 2010 ; Lynch et al. 2008 ; ten Have et al. 2007 ). The ignorability assumption for the X → M relationship can largely be addressed by randomizing the levels of X ; the ignorability assumption for the M → Y relationship is more difficult to justify and represents a challenging aspect of mediation analysis. Ignorability for the M to Y relation assumes randomization of participants at each level of X . In most research, this randomization is not possible and participants usually self-select their value of M. The extent to which sequential ignorability is a valid assumption may differ depending on the type of mediating variable. For example, if the mediators are selected because theory and prior empirical research suggest that they are causally related to the outcome variable, it may be reasonable to conclude that the b effect is known. Thus, it is only required that the levels of M be changed. In this case, the manipulation that changes the X to M relation will have the same expected change in the M to Y relation. Replication experiments can also further clarify the actual mediator from a host of other potential omitted variables. In this respect, replication studies with different manipulations are critical for identifying mediating variables.

Reliability

As in all research, the reliability and validity of proposed measures are best assessed prior to conducting the study. The reliability of a measurement is the extent to which a measure consistently reproduces values of the underlying true score. Valid measures measure the construct they are designed to measure. A program of research is typically needed to develop reliable and valid measures. Measurement is critical to mediation analysis and the search for mediating variables can be considered a measurement problem where science is advanced by more accurate measures of the mediating process ( MacKinnon 2008 ).

Studies on measurement error highlight the need for reliable measures when detecting a mediator. Hoyle and Kenny (1999) demonstrated that as the reliability of M decreases (i.e., as the reliability coefficient departs from one), the observed effect of M on the Y and b is underestimated and the observed effect of X on the Y and c ′ is overestimated. This results in an underestimation of the mediated effect and a decrease in the statistical power to detect the mediated effect. Due to the potential impact unreliability has on masking mediational effects, reliable mediating measures are crucial. One way to increase reliability is to obtain multiple indicators of the variable of interest and create a latent construct representing the variable of interest. The use of a latent variable model allows the estimation of associations between latent variables which are free of measurement error. However, using a latent variable model requires sufficiently large sample sizes and this is not always plausible. Modeling approaches which incorporate the effects of multiple methods such as multiple reporters or item types may yield more reliable relations than those ignoring method effects ( Geiser and Lockhart, under review ) and may provide promising tools for investigating mediation effects with more reliable measures.

Sample Size

Selection of sample size for adequate statistical power is an important part of designing any study. Although a large a sample size is ideal, sample size is often limited for reasons outside the control of a researcher, such as a small available population (e.g., local individuals of a specific age) or financial issues (e.g., excessive time or cost of measurements). However, even with an extremely large sample size, it will be important to obtain some measure of effect size to judge the importance or size of an effect. Mediation studies have traditionally been underpowered because the sample size requirements are much larger than those of simpler models, such as simple linear regression. Fritz and MacKinnon (2007) used simulations to determine sample sizes to obtain 0.80 power for small, medium, and large effect sizes in the single mediator model. A sample size of approximately 74 is required to detect a mediation effect when the path for the X to M relation and the M to Y relation is medium. A more complex mediation model would require a larger sample. The careful use of covariates can decrease the required sample size and repeated measures and longitudinal data can also improve the ability to detect effects. Required sample size and statistical power for more complicated mediation models has been outlined by Thoemmes et al. (2010) using a Monte Carlo approach in a covariance structure analysis program.

Choosing Mediators

The theoretical interpretation of the links in a mediation model can be thought of in terms of the theory for the process underlying the manipulations. These two processes are called the action theory and the conceptual theory ( Chen 1990 ; MacKinnon 2008 ). As shown in Fig. 2 , action theory corresponds to how the manipulation will affect the mediators (the relationship between X and M ) and conceptual theory focuses on how the mediators are related to the outcome of interest (the relationship between M and Y ). Consider an intervention designed to increase knowledge of the benefits of exercise and nutrition designed to increase employee well-being, which is measured as the number of sick days the employee uses. The action theory is that the program will increase the employees’ knowledge of exercise and nutrition. Conceptual theory says that knowledge of the benefits of exercise and nutrition will increase employee well-being, reducing the number of sick days. The use of action and conceptual theory can be used to demonstrate how a manipulation leads to changes in the dependent variable ( Ashby 1956 ; Lipsey 1993 ; MacKinnon 2008 ).

Action and conceptual theory

The informed choice of possible mediators often emerges from action and conceptual theory. Typically, conceptual theory is based on prior research that provides information about the relationship between a potential mediator and the outcome of interest. However, action theory can also inform the selection of mediators, based on what variables are able to be changed by experimental manipulation or intervention. For example, an experimental manipulation can change an individual’s beliefs about a product, but it typically cannot change aspects of an individual’s personality ( Table 1 ).

Table 2 summarizes six methods suggested by Mackinnon (2008) to choose mediators. Depending on the information available and the current state of a particular research area, any one of the six approaches may be a viable approach to choosing mediators. When the area of study is well-researched and a great deal of prior research is available on which to build, mediators can be chosen by performing a literature review to determine empirical relations between potential mediating variables and the outcome variable of interest, by targeting mediators based on an established theoretical framework in the area of substantive research, or based on prior mediation research. In particular, the “theory-driven” approach has received considerable support in the literature ( Chen 1990 ; Lipsey 1993 ; Sidani and Sechrest 1999 ). When the area of research is new and little prior research is available to guide the selection of mediator variables, different approaches to selecting mediators must be employed. Mediators may be identified by studying correlates of the outcome measure to identify mediators based on the conceptual theory of the outcome, using qualitative methods such as focus groups, or on the basis of common sense or intuition about what seems to be the best target of a program. Although these are less scientifically driven methods, they may be a good approach in newly developing fields.

Methods of choosing /mediators

Testing Multiple Mediators

Many studies include more than one mediator of an X – Y relationship. For example, Chen and Chiu (2008) examined several mediators of the relationship between supervisor support of employees and employee organizational citizenship behavior. They found that job satisfaction, person-organization fit, and job tension mediated the relationship between supervisor support and citizenship behavior. When there are multiple mediators, a simple approach is to evaluate one mediator at a time. Using the Chen and Chiu study as an example, one may initially examine the supervisor support → job satisfaction → citizenship behavior relationship. Looking at a single mediator at a time is a useful approach because specific theoretical hypotheses often focus on single mediators rather than groups of mediators. It is also wise to examine any potential moderator or interaction effects (discussed in more detail shortly) at the single-mediator stage. If the study involves many mediators, it will be necessary to implement some control for experiment-wise error due to multiple tests; the alpha or Type I error rate increases rapidly with multiple tests.

A model including all measured mediators should be estimated in addition to single mediator models. This is accomplished by expanding Eq. 3 to include all mediating variables. For example, in the case of three mediating variables, Eq. 3 can be reexpressed as:

Evaluating a model that includes all measured mediators is important because it is possible that the effect of a mediator may change in the presence of other mediators. Recall that the b and c ′ coefficients in these models are partial regression coefficients controlling for all other predictors, so the exclusion of some mediators could potentially change these values in the complete model. Including all measured mediators also produces a model which more closely matches reality, where all potential mediators are present. An additional benefit of multiple mediator models is the identification of mediation pathways that lead to beneficial relations on the outcome measures and mediating pathways that are actually counterproductive. These inconsistent mediation models, defined as mediation models where at least one mediated effect has a different sign than the direct effect, are more clearly identified in multiple mediator models ( MacKinnon et al. 2000 ).

Moderator Effects in Mediation Models

As previously described, moderation is an effect involving a third variable that changes the direction or magnitude of the relationship between two other variables. For example, the relationship between stress and health is moderated by social contacts; individuals with many friends show little relationship between stress and health while individuals with few friends have a strong positive relationship between stress and health ( Cohen and Wills 1985 ).

Moderator relationships can occur with mediation relationships. The combination of moderation and mediation can take on several forms. We briefly discuss these situations involving both mediation and moderation because they occur frequently within psychological research and can be confusing to understand. Moderation of a mediated effect occurs when a moderator variable ( Z ) affects the direction or strength of any or all the mediation regression coefficients. There are a number of ways to incorporate moderator effects into a mediation model. If the moderator is binary, such as gender, moderator effects can be evaluated by conducting analyses by group. Individual regression coefficients or estimates of the mediated effect can be compared across groups using t tests (see MacKinnon 2008 , p. 292). This method is straightforward and makes interpretation simpler, but it can only be used for binary moderators. More complex approaches are needed for continuous moderators and moderators with several categories.

In order to include continuous moderators in a mediation model, the moderators are incorporated into Eqs. 1 – 3 as interaction terms. For example, if a continuous variable, Z, is hypothesized to moderate the effect of X on M , Eq. 2 becomes:

where coefficient f represents the main or conditional effect of the moderator Z on the mediator M and coefficient g represents the interaction or moderator effect of X and Z . Tein et al. (2004) present a framework for testing moderation of all four mediation paths. This framework also allows for the inclusion of baseline covariates. For example, in evaluating a program to improve management and supervisor skills, a researcher may wish to control for pre-program level of skill by including this variable as a covariate.

Timing and Longitudinal Effects

The mediation model is a longitudinal model in that X precedes M and M precedes Y. However, in practice, tests of mediation may be conducted using cross-sectional data. There are a number of problems and limitations with using cross-sectional data to investigate longitudinal mediational processes, as outlined by several researchers ( Cheong et al. 2003 ; Cole and Maxwell 2003 ; MacKinnon 2008 ; Maxwell and Cole 2007 ). Conceptually, a problem arises because mediation is inherently a process that unfolds over time and cross-sectional data do not measure this unfolding over time. Statistically, several studies have shown that estimates of the cross-sectional-mediated effect are severely biased when compared to the estimates of the longitudinal mediated effect ( Maxwell and Cole 2007 ). The bias may be either positive or negative, further complicating the use of cross-sectional data.

The best-designed studies employ repeated measures because power to detect mediated effects is greatly enhanced. In addition, longitudinal studies allow for the measure of change in response to a manipulation ( Cohen 1988 ; Singer and Willet 2003 ). The methodological literature has emphasized the importance of temporal precedence in the investigation of mediation ( Gollob and Reichardt 1991 ; Judd and Kenny 1981 ; Kraemer et al. 2002 ; MacKinnon 1994 ) and has described methods for assessing longitudinal mediation ( Cheong et al. 2003 ; Cole and Maxwell 2003 ; MacKinnon 2008 ; Maxwell and Cole 2007 ). The evaluation of longitudinal mediation models is an important step in advancing mediation methods. Although there are several choices of longitudinal models described in the literature such as autoregressive models and latent change score models, latent growth curve models are a common choice for longitudinal mediation models ( Cheong 2002 ; Cheong et al. 2003 ). More detailed information on longitudinal mediation models can be found in Mitchell and James (2001) and MacKinnon (2008) .

Although longitudinal mediation modeling is the preferred method for evaluation of the mediation process, there are situations where only cross-sectional data are available. For example, secondary mediation analysis of data from a previously collected study may require the use of cross-sectional data. In this case, estimates of the cross-sectional mediated effect may not reflect the longitudinal mediated effect and researchers must provide evidence for temporal relations from theory or empirical research.

Decisions During Mediation Analysis

Recall the path model in Fig. 1 , which shows the directional relations between X , M , and Y for the single-mediator model. For the mediation framework that is most commonly used in psychology ( Baron and Kenny 1986 ; MacKinnon 2008 ), three regression equations are used to describe the relations in this model. These regression equations describe the effect of X on Y ( Eq. 1 ), the effect of X on M ( Eq. 2 ), and the effect of X on Y , controlling for M ( Eq. 3 ). The c coefficient is the total effect of X on Y. The c ′ coefficient is the direct effect of X on Y , controlling for M . The a coefficient corresponds to the “action theory” for the model, whereas the b coefficient corresponds to the “conceptual theory” for the model ( Fig. 2 ).

Baron and Kenny (1986) proposed a causal steps approach to testing whether statistical mediation is present in such a model. The causal steps approach describes a series of tests of regression coefficients that, together, can show mediation is occurring. The first step in this approach is to test whether changes in X produce changes in Y , i.e., whether there is an effect to be mediated. This is determined by the significance of the c regression coefficient in Eq. 1 . If there is no relation between X and Y , the causal steps approach stops. If there is a relation between X and Y , the next step is to determine if there is a relation between X and M by testing the a regression coefficient in Eq. 2 . Given that the independent variable significantly affects the mediator, the next step is to test whether M is related to Y , after controlling for the effect of X on Y. This is shown by testing the significance of the b regression coefficient in Eq. 3 . Finally, it must be shown that the effect of X upon Y , after controlling for M , is not significantly different than zero. The test of the c ′ coefficient in Eq. 3 should not be significant.

The requirements of the causal steps approach that c ≠ 0 and that c ′ = 0 results in reduced statistical power to detect a mediated effect. The requirement that c ≠ 0 is problematic because statistical tests are not absolute; there is always the potential for a Type I or Type II error in this decision. Additionally, if subgroups of participants (e.g., men vs. women) have opposing effects, ignoring these subgroups could result in a non-significant c value. The causal steps mediation approach also requires that c ′ = 0, meaning all effects from X to Y must be transmitted through M ; this type of mediation model is called a complete mediation model . The complete mediation model is the most defensible mediation conclusion from a research study, but it suffers from low statistical power when the causal steps approach is used. The complete mediation model is discussed in more detail shortly.

Modern methods of mediation analysis use regression (as well as structural equation modeling, an expansion of the regression framework) to quantify the mediated effect as a single number for which confidence intervals and significance tests can be calculated. The theory of mediation states that there is a causal relation in a mediation model, such that X causes M and M causes Y. Therefore, the mediated effect of X to Y via M can be quantified as the product of the regression coefficient relating X to M and the regression coefficient relating M to Y , or ab (using Eq. 2 and above). The test of ab can be more powerful than the test of c because it is a more precise explanation of how X affects Y ; the requirement that c be significant is not necessary for mediation to exist. Although modern methods pose that the test of c may not be as important in determining the mediating effect, the test of c is clearly important in its own right. A lack of statistically significant c is important in assessing manipulation and conceptual theory for future studies.

When both M and Y are observed and continuous (so that linear regression or structural equation modeling is used to estimate Eqs. 1 – 3 ) and there are no missing data, it can be shown that the difference between the total effect of X on Y and ( c ), and the direct effect of X on Y and ( c ′) is numerically equivalent to ab . As previously noted, this result holds only for linear models such as linear regression, but not for non-linear models such as logistic regression ( Pearl, in press ). The c – c ′ estimate of the mediated effect reflects that any difference between the total relation between X and Y (the c coefficient) and the direct effect of X on Y while controlling for M (the c ′ coefficient) must be due to the indirect or mediated effect. Some researchers have suggested that only c – c ′ should be used for making causal inferences. The reasoning behind this stance is that, typically, only X is randomly assigned, whereas M is observed or self-selected by the participant. Therefore, the c – c ′ estimate of the mediated effect involves using only regression coefficients that are based upon random assignment to experimental conditions. The point of contention is often irrelevant because the two quantities ab and only c – c ′ are identical in linear regression and structural equation modeling of continuous measures. For logistic regression or other nonlinear statistical methods, the two estimators of the mediated effect may not be equal and may have different meanings ( Imai et al. 2010 ; Pearl 2011).

Complete Versus Partial Mediation

Some researchers (e.g., James and Brett 1984 ) suggest a slightly different approach to quantifying the mediated effect than has been presented here. James and Brett suggest that the model described by Eqs. 1 – 3 implicitly assumes partial mediation , i.e., the mediated path via M accounts for only some of the effect of X on Y. In other words, this approach implies a non-zero direct path or c ′ coefficient. James et al. (2006) suggest an alternative approach that begins from an assumption of complete mediation (sometimes also called “full mediation”), where the c ′ path is assumed to be zero and all effects of X on Y are transmitted through the mediator M . In the complete mediation framework, two regression coefficients are estimated. First, the effect of X on M is estimated using the a coefficient in Eq. 2 above. Second, the effect of M on Y is estimated using the expression

where b ′ is a new regression coefficient representing the relation of M and Y , completely ignoring X . The mediated effect is calculated as ab ′ and reflects the use of this new coefficient.

The complete mediation approach has several attractive features. First, fixing the value of the c ′ path to zero means that, from a structural equation modeling perspective, the complete mediation model is identified and has degrees of freedom, allowing for goodness-of-fit tests. Goodness-of-fit tests allow a researcher to test how closely the model matches the observed data, in addition to testing whether individual paths and the mediated effect are significantly different from zero. Second, the complete mediation model is a more parsimonious explanation of the causal relation from X to M to Y. From a philosophy of science perspective, a simpler or more parsimonious model is preferred. However, complete mediation is uncommon in many areas of psychology so it is likely that there is a direct effect of X on Y , and testing for complete mediation as a first step may not be informative for psychological research; this is a weakness shared by the causal steps approach to mediation described in Judd and Kenny (1981) , which requires that the null hypothesis that the c ′ path is equal to zero is not rejected.

Tests of Mediation and Confidence Limit Estimation

There are many statistical tests to evaluate the mediated effect. Some tests of the mediated effect involve dividing the estimate of the mediated effect by an estimate of its standard error ( Wald 1943 ) and this ratio is then compared to an appropriate statistical distribution, such as the normal distribution. Other tests of the mediated effect are non-parametric, resampling tests such as bootstrapping which use the observed data to determine the distribution and standard error of the mediation estimate. MacKinnon et al. (2002) provide an evaluation of fourteen different methods of evaluating the mediated effect, including methods of calculating the standard errors for ab and c – c ′.

One of the most common tests of the ab mediated effect is based on the multivariate delta standard error ( Sobel 1982 ). The ratio of the mediated effect to its standard error is compared to a standard normal ( z ) distribution to test significance. This method has reduced power because the product of two normally distributed regression coefficients is not normally distributed and instead follows the distribution of the product. The distribution of the product is variable in shape depending on the magnitude of the coefficients and is often asymmetric and highly kurtotic ( Aroian 1947 ; Craig 1936 ).

As described in MacKinnon et al. (2002) , tests of the mediated effect that are based on the distribution of the product have more accurate Type I error rates and have more power than many other tests. Critical values for the distribution of the product produce more accurate confidence intervals for the mediated effect. PRODCLIN (distribution of the PRODuct Confidence Limits for INdirect effects) automates the selection of critical values for the distribution of the product ( MacKinnon et al. 2007 ). The user inputs values of a , b , their respective standard errors, and the desired Type I error rate (e.g., α = .05); the program returns the upper and lower asymmetric confidence limits for the mediated effect. A new version of this program (RMEdiation; Tofighi and MacKinnon 2011 ) now provides several additional capabilities including plots of the distribution of the product and several options for confidence limits. Mackinnon et al. (2004) found that tests of significance based on the distribution of the product outperformed other single-sample methods in terms of Type I error rates, power, and accuracy of confidence limits.

Bootstrapping is a resampling technique that is often used to evaluate a test statistic (such as the mediated effect) when the true distribution of the statistic is either unknown or difficult to obtain. The bootstrapping method involves taking many (e.g., 1000) repeated “samples” from the observed sample, calculating the statistic of interest, and producing a distribution based on these values of the statistic. Confidence intervals are obtained empirically, i.e., by observing the value in the bootstrapped distribution beyond which a certain proportion of the test statistics lie. For example, for a two-tailed test with an alpha value of .05, order the 1000 bootstrap statistics from lowest to highest, and choose the value of the (1000 × 0.025) = 25th observation as the lower critical bound of the confidence interval and the value of the (1000 × 0.975) = 975th observation as the upper bound of the confidence interval. Bootstrap methods for determining the significance of the mediated effect generally perform well in terms of power and Type 1 error (see MacKinnon et al. 2004 ). Routines to perform bootstrap analyses are included in many common statistical software programs, including AMOS, M plus , and EQS and programs for conducting bootstrap analyses in SAS and SPSS are also available ( Lockwood and MacKinnon 1998 ; Preacher and Hayes 2004 ). Another advantage of bootstrap methods is that they can be easily expanded as the complexity of the model increases; for example, bootstrapping can easily be applied to a multiple mediator model in which X → M 1 → M 2 → Y , where the mediated effect is calculated as the product of three regression coefficients.

In summary, an ideal method includes an estimate of the mediated effect along with a confidence interval for the indirect effect. Confidence intervals may be made with a bootstrap method or the distribution of the product. The effect size for the paths in the mediation model such as the standardized coefficients or partial correlation measure should be reported. Note that it is important to report statistical tests of the relation of X to Y (i.e., the c coefficient from Eq. 1 in the single mediator model), but this relation is not necessary for mediation to exist. In fact, a test of mediation may be more important when there is not a statistically significant relation of X to Y because the path from X to M represents a test of action theory and the path from M to Y represents a test of conceptual theory. When reporting mediation analyses, it is important to provide detailed information regarding the models tested along with the coefficients from these models (e.g. a , b , c ′, and ab ) and the confidence intervals ( Mackinnon 2008 ).

Decisions Following Mediation Analysis

Several assumptions were made for the regression equations described earlier that can be addressed in the design phase of the study or by appropriate statistical analysis. However, several assumptions are untestable and cannot be completely addressed using design or statistical approaches. These assumptions are related to confounders of the mediated effect, higher order relations between X and M , the causal ordering of X , M , and Y , and measurement error. Many aspects of these assumptions can be addressed by sensitivity analysis, a method of assessing how much the results of an analysis may change due to violation of assumptions. Typically, sensitivity analysis involves systematically changing values of specific parameter values in the model (for example, the a path from X to M ) to determine how much the parameter must change in order to change the substantive interpretation (i.e., significance) or change the estimates by a specific, pre-determined amount (e.g., to change the estimate of the mediated effect by 25%). Sensitivity analysis is one of the most challenging aspects of mediation analysis, but there has been considerable development in these methods in recent years.

Confounders

For the ab estimator of the mediated effect and ordinary least squares regression, the errors in Eqs. 2 and 3 are assumed to be independent. The uncorrelated errors assumption may be violated if there are confounding variables that are omitted from the analysis. Confounders can have a substantial effect on the analysis of the mediated effect. Figure 3 shows how confounders can potentially influence multiple paths in a mediation model. Ideally, measures of the potential confounding variables are included in the statistical model, but if they are not, the confounders may result in biased estimates. As with any study, even when some potential confounders are included in the analysis, there is no guarantee that all possible confounders were included.

Confounders of mediation relations. The true model requires d 1 , d 2 , d 3 , and d 4 , otherwise the coefficients are confounded

Sensitivity analysis is one way to assess the influence of omitted variables on the observed mediation relations. Since randomization of X theoretically eliminates confounders in the X → M relationship, the goal of sensitivity analysis in mediation for experimental studies is typically to assess how large a confounder effect on the M → Y relation (i.e., sequential ignorability) must be in order to invalidate the conclusions of the analysis ( Frank 2000 ; Li et al. 2007 ; Lin et al. 1998 ; Rosenbaum 2002 ). The correlation between the errors in Eqs. 2 and 3 reflects the contribution of omitted variables to the observed relation of M to Y , or the degree to which the assumption of sequential ignorability is violated. Thus, by systematically increasing the correlation between the errors in Eqs. 2 and 3 , one can evaluate how much the b and c ′ coefficients change due to violation of this assumption ( Imai et al. 2010 ).

Promising approaches to improving causal inference by addressing the bias introduced by omitted variables have been proposed ( Frangakis and Rubin 2002 ; Holland 1988 ; Jo 2008 ; Murphy et al. 2001 ; Pearl 2009 , in press ; Robins and Greenland 1992 ; Robins et al. 1992 ; Rubin 2004 ; Shipley 2000 ; Sobel 1998 , 2008 ; Winship and Morgan 1999 ) but most have not been extensively evaluated in simulation studies and applied settings. Vander Weele (2008 , 2010 ) has formalized several useful methods to probe bias in mediation relations when one or both assumptions of Sequential Ignorability have been violated. Imai et al. (2010) describe another method and include a computer program to assess the sensitivity of the results to potential confounders. These methods allow the researcher to draw a conclusion about the direction of the bias by suggesting relations of unmeasured confounders on relations in the mediation model.

X – M Interaction

The standard single mediator model assumes that Eqs. 2 and 3 represent causal relations that are linear, additive, and recursive ( Holland 1988 ; James and Brett 1984 ; James et al. 2006 ; McDonald 1997 ). An additivity assumption implies that there is no interaction between X and M ( Collins et al. 1998 ; Judd and Kenny 1981 ), i.e., the effect of X on Y does not depend on the value of M and the effect of M on Y does not depend on the value of X . The additivity assumption can be directly tested by including the interaction of X and M; if the interaction term is significant, the assumption of additivity is violated. In this context, the mediated effect differs across levels of X and further analyses can explore the size and significance of the mediated effect at different values of X .

Causal Ordering

Since mediation is a causal model, it is important to clearly define the causal chain from X to M to Y. The mediation model makes the assumption that the correct causal order has been specified, such that X causes M and M causes Y. When X is randomly assigned, it is clear that X occurs before M and Y. However, the ordering of M and Y is less clear and theory and prior empirical research can help make the causal ordering more concrete.

Hill (1965) outlined nine considerations for clarifying the ordering of causal relations. These points were initially developed to investigate smoking as a cause of cancer but have applications to establishing causal ordering in mediation models. These are substantive considerations rather than statistical tests, so they require a substantive researcher to carefully evaluate the variables involved. The nine criteria are (1) strength, (2) consistency, (3) specificity, (4) temporality, (5) biological gradient, (6) plausibility, (7) coherence, (8) experiment, and (9) analogy. According to Hill, causality is implied by (1) a stronger relation rather than a weaker relation, (2) consistent findings by multiple people in multiple samples, (3) specific findings (e.g., about a specific disease rather than general unhealthiness), (4) the “cause” occurring prior to the “effect” in time, (5) a larger effect seen with larger exposure to the “cause,” (6) a plausible and sensible mechanism by which the causal relationship occurs, (7) agreement between laboratory and observational studies, (8) experimental evidence of the causal relationship, and (9) similar “causes” resulting in similar “effects.” These criteria can be applied to M and Y (or to X , M , and Y , if X is not randomized) to provide evidence that the presented causal ordering is the correct ordering.

Measurement Error

Mediation analysis assumes that the measures are both reliable and valid ( Baron and Kenny 1986 ; Holland 1988 ; James and Brett 1984 ; MacKinnon 2008 ; McDonald 1997 ). As previously discussed, Hoyle and Kenny (1999) showed that unreliability of the mediator leads to underestimation of the b path and overestimation of c ′ which results in underestimation of the mediated effect and lower power to detect the mediated effect.

After the study is completed, a correction for unreliability in measured variable models can be applied to obtain estimates of coefficients if reliability is assumed to be a certain value (see MacKinnon 2008 , p. 189). This new model results in the estimation of coefficients that have been adjusted for more or less reliable measures. Limitations of this method are that the reliability estimate used may not always be accurate and the correction approach may not adequately address measures that are actually composed of more than one factor. In addition, if there are multiple factors for some measures, the relationship of these factors may have important relationships with other variables in the model that will be ignored ( Bagozzi and Heatherton 1994 ).

Planning for the Next Study

Every study can be thought of as a single piece of an overall body of research; each study builds upon previous studies, resulting in an accumulation of knowledge. Studies involving statistical mediation are no different. In this article, we have discussed a number of potential limitations to the interpretation of mediation analysis, particularly causal inference limitations. For example, we discussed potential confounders of the mediated effect, including experimental design methods that can help rule out the presence of potential confounders and newly developed sensitivity analysis methods that can determine the amount of bias caused by omitted confounders. The consideration of limitations of interpretability and generalizability of results may be especially important for mediation studies because of the number of omitted variables that may affect observed results. As an illustration, consider a study involving a non-randomly assigned X variable. There are several limitations of the interpretation of the mediated effects in this study. The relationship between X and M may be biased for several reasons; the true causal ordering of X and M is potentially unclear and there may be confounders that bias the estimate of the relationship between X and M . A follow-up study can address both of these limitations by incorporating a randomly assigned manipulation of the X variable. Random assignment of the X variable ensures the causal ordering of X and M because X is randomly assigned before M is measured; random assignment also ensures that there are no confounders of the relationship between X and M . If the follow-up study produced results that match the initial study, the researcher can be confident that causal inference based on the X → M relationship is sound. If the follow-up study produces conclusions that differ from the initial study, further research is needed; for example, confounders of the M → Y relationship may be affecting the results. In either situation, multiple studies are typically required to produce a clear picture of the true relationships.

Statistical mediation analysis is a powerful tool for testing the process by which an effect occurs in both experimental and observational studies. In this article, we discussed how design decisions made prior to conducting a study and statistical choices made during analysis influence the conclusions that can be drawn from a study that involves statistical mediation. We also discussed the limitations of interpretation of a mediation process for even well-designed and analyzed studies. The major point is that the investigation of mediation processes requires careful planning and is part of a cumulative program of research using evidence from a variety of sources including clinical observation, qualitative studies, and replication ( MacKinnon 2008 ). Mediation analysis is popular because it directly addresses important theoretical questions about processes by which effects occur. This importance of mediating variables for scientific understanding was identified many years ago ( Lazarsfeld 1955 ; Woodworth 1928 ) and there is now a body of statistical techniques to test and evaluate mediation theory. Business research is an ideal area for the application of these techniques to evaluate whether a variable is truly intermediate in a causal sequence.

Acknowledgments

This article was supported in part by Public Health Service Grant DA09757 from the National Institute on Drug Abuse.

Contributor Information

David P. MacKinnon, Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA.

Stefany Coxe, Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA.

Amanda N. Baraldi, Department of Psychology, Arizona State University, Tempe, AZ 85287-1104, USA.

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• Open access
• Published: 29 July 2024

The effect of education level on depressive symptoms in Chinese older adults–parallel mediating effects of economic security level and subjective memory ability

• Ruonan Zhao 1 , 2 ,
• Jian Wang 1 , 2 ,
• Jiaxu Lou 1 , 2 ,
• Mei Liu 1 , 2 ,
• Jiahui Deng 1 , 2 ,
• Derong Huang 3 &
• Huiling Fang 1 , 2  

BMC Geriatrics volume  24 , Article number:  635 ( 2024 ) Cite this article

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Depression in older adults needs urgent attention. Increased education level may reduce depressive symptoms in older adults, and that economic security level and subjective memory ability may also have an impact on depressive symptoms in older adults, but the mechanisms between education level and depressive symptoms in older adults are unclear. This study endeavors to investigate the parallel mediating roles of economic security level and subjective memory ability between education level and depressive symptoms in older adults.

A total of 4325 older adults people aged 60 years and above were selected from the China Family Panel Studies (CFPS) as the study population, and all data were analyzed using SPSS 25.0 software. Spearman correlation analysis was used to explore the correlation between the variables. Model 4 from the SPSS macro was used to assess the parallel mediating role of economic security level and subjective memory ability in the relationship between education level and depressive symptoms in older adults.

Education level, economic security level, and subjective memory ability were significantly associated with depressive symptoms in older adults ( p  < 0.01). Educational level was a negative predictor of depressive symptoms (β=-0.134, P  < 0.001). Education level was a positive predictor of economic security level (β = 0.467, P  < 0.001) and subjective memory ability (β = 0.224, P  < 0.001). Education level, economic security level, and subjective memory ability were significant negative predictors of depressive symptoms (β= -0.039, P  < 0.05; β= -0.122, P  < 0.001; β= -0.169, P  < 0.001). Education level influenced depressive symptoms through parallel mediating effects of economic security level and subjective memory ability, with mediating effects accounting for 42.70% and 28.30% of the total effect, respectively.

Conclusions

Education level not only directly influences depressive symptoms in older adults, but also indirectly through the economic security level and subjective memory ability. Educational level can reduce depressive symptoms in older adults by increasing their economic security level and enhancing their subjective memory ability. The findings of this study emphasize the importance of improving the educational level of the population as it affects people’s mental health in old age.

Peer Review reports

Introduction

Depression is a prevalent mental disorder among older adults. The global prevalence of depression in older adults is 28.4% [ 1 ], and the global prevalence of major depression reaches 13.3% [ 2 ]. The prevalence of depression among the older adults in China is high, reaching 25.55%, and the prevalence of depression tends to continue to increase over time [ 3 ]. The World Health Organization (WHO) ranked major depression as the third leading cause of the global burden of disease in 2008, and the disease is expected to rank first by 2030 [ 4 ]. Depression can be distressing for older adults, cause the breakdown of their families, and may lead to the worsening of existing illnesses and physical disability [ 5 , 6 ]. Depression among the older adults should receive more attention in order to better achieve active aging and improve the physical health, mental health, and quality of life of the older adults.

Education level influences depressive symptoms in older adults, those with less education are at a higher risk of developing depressive symptoms [ 7 , 8 , 9 ]. Education represents people’s ability to access and use health information, and the level of education may have a greater impact on health than income or occupational status [ 10 , 11 , 12 ]. These results can be explained by the life course theory and the cumulative advantage/disadvantage theory. The life course theory, developed by Elder [ 13 ], emphasizes that the different life stages of a person are interconnected and that early life circumstances and experiences have long-term effects on the person [ 14 , 15 ]. Cumulative advantage/disadvantage theory, on the other hand, suggests that early risk factors accumulate over the course of a person’s life and show their greatest impact later in life, meaning that early advantages or disadvantages are magnified over the life course [ 16 , 17 ]. This shows that, a higher level of education is a lasting resource that produces advantages that accumulate over the course of life, increase happiness and joy in later life [ 18 ], and have a protective effect against depression throughout life [ 19 ]. Therefore, the effect of education level on depressive symptoms in older adults cannot be ignored.

Economic security is an important element of social security, retirement benefits, pension insurance and financial support from children are the main components of economic security for the older adults. Education level may affect the economic security level of the older adults. One study found that educated older adults being more likely to receive an occupational pension and receiving higher levels of benefits [ 20 ]. This may be due to the higher average income and social benefits of the educated population, and therefore their higher pension levels [ 21 , 22 ]. Meanwhile, higher levels of pension can alleviate depression [ 23 , 24 ], probably because pension receipt increases older adults’ confidence in the future [ 25 , 26 ]. The marginal effect of pension receipt on enhancing mental health is stronger for older adults with poorer mental health [ 27 ], and pensions are most effective in alleviating depressive symptoms in older adults with low levels of education [ 28 ]. However, some scholars believe that a higher level of economic security may increase people’s depressive symptoms, which may be related to the specific countries and regions, cultural environment, sample population and other factors [ 29 , 30 ].

Subjective memory is used to represent how individuals interpret, feel, or think about their memories, that is, the individual’s perception of memory performance [ 31 ]. The level of education may affect the subjective memory ability of the older adults. Education has a significant protective effect on memory capacity, with older adults with higher levels of education experiencing slower rates of memory decline [ 32 ]. Individuals with lower levels of education may be more likely to have memory deficits, and higher levels of education will reduce memory deficits associated with depressive symptoms [ 33 ]. Self-reported memory is important because it reflects the severity of depressive symptoms in older adults [ 34 ]. Lower memory ability at baseline survey was associated with worse levels of depressive symptoms at follow-up [ 35 ], suggesting that lower levels of memory ability may deepen depressive symptoms in older adults. In the older population, decreased subjective memory ability was associated with increased depression severity [ 36 ], educational interventions can improve memory loss in older adults and can alleviate their future depressive symptoms [ 37 ].

Currently, although the relationship between education level and depressive symptoms in older adults has been investigated, the mechanisms between these two variables are unclear. No scholars have studied the relationship between education level, economic security level, subjective memory ability, and depressive symptoms in older adults. Therefore, the main purpose of this study was to explore the relationship between education level and depressive symptoms in older adults, to examine the parallel mediating role of economic security level and subjective memory ability in this relationship. The theoretical framework of this study is shown in Fig.  1 , and we examined the following three hypotheses:

Hypothesis 1

Educational level has a negative predictive effect on depressive symptoms in older adults.

Hypothesis 2

Economic security level mediates the relationship between education level and depressive symptoms in older adults.

Hypothesis 3

Subjective memory ability mediates the relationship between education level and depressive symptoms in older adults.

Parallel mediation model

Materials and methods

Data source and sample selection.

The data for this study come from the China Family Panel Studies (CFPS), a biennial tracking survey conducted by the China Social Science Research Center at Peking University. The CFPS database collects data at the individual, household, and community levels, and investigates various aspects of Chinese residents’ economic activities, family relationships, and health status. CFPS officially launched the survey in 2010, with a sample covering 25 provinces/municipalities/autonomous regions in China. In this study, the contents of the individual-level questionnaire from the CFPS Round 5 survey in 2020 were selected for analysis, and older adults aged 60 years and above were chosen as the study population. The total number of samples in the 2020 CFPS database was 28,590, with 4,325 samples included after screening and the specific sample selection process is shown in Fig.  2 .

Selection of study subjects

• Education level

The independent variable studied in this paper is the educational level of the older adults, which is measured by the question in the questionnaire “What is the highest level of education you have completed (graduated)?” [ 22 , 38 ]. According to the questionnaire responses, the answers were divided into five levels: “illiterate/semi-literate”, “elementary school”, “junior high school”, “high school”, and “college and above”, which were assigned a score of 1, 2, 3, 4, and 5, respectively, with higher scores indicating higher education levels [ 39 , 40 ].

• Depressive symptoms

The dependent variable studied in this paper was depressive symptoms in older adults. The 2020 CFPS questionnaire uses the CES-D8 to measure depressive symptoms in older adults [ 41 ]. The effectiveness of CES-D8 has been confirmed in previous studies [ 42 ]. SPSS analysis shows that Cronbach alpha of CES-D8 scale is 0.790, which indicates that the scale has good internal consistency reliability [ 43 ]. KMO value is 0.802, and P value in Bartlett’s Test of Sphericity is less than 0.001, which indicates that the scale has good validity [ 44 ]. The CES-D8 contains 8 questions related to depression, in which the respondents indicated the frequency of various feelings or behaviors in the past week according to their actual situation, and the answers were “hardly ever”, “some of the time”, “often”, and “most of the time”. Among these 8 questions, 2 positively stated questions scored 3 (hardly ever) to 0 (most of the time) and 6 negatively stated questions scored 0 (hardly ever) to 3 (most of the time) [ 8 , 45 ]. The total score of the CES-D8 is 24, with higher scores indicating higher levels of depression; scale scores greater than or equal to 10 indicate a higher frequency of depressive symptoms in older adults [ 8 , 46 ].

• Economic security level

The level of economic security studied in this paper includes retirement benefits, pension insurance and financial support from children. The level of economic security is measured by the questionnaire questions “How much do you currently receive per month after tax, including your pension, various types of pension insurance and various allowances?” and “Please convert the gift into cash, how much did your children give you on average in cash in the past 6 months?” [ 47 , 48 ]. The sum of the money answered in these two questions reflects the economic security level of the older adults.

• Subjective memory ability

The subjective memory ability of older adults was measured by the questionnaire “How many major events that happened to you in the last week can you remember?” [ 49 , 50 ]. The answers to this question included “can barely remember”, “can only remember a few”, “can remember half”, “can remember most”, and “can remember completely”, and were assigned a score of 1, 2, 3, 4, and 5, with higher scores indicating better subjective memory ability [ 51 , 52 ].

In this study, a number of confounding factors associated with depressive symptoms in older adults were selected as covariates [ 38 , 39 , 46 , 53 ], including: age, gender, marital status, smoking status, alcohol consumption, neighborhood trust, and kinship. These variables were all measured by the 2020 CFPS individual-level questionnaire.

Statistical analyses

All data were analyzed by SPSS 25.0 software. Firstly, we launched a descriptive statistical analysis of the main study variables. Secondly, there was non-normally distributed data in the study variables, so we used spearman correlation analysis to explore the correlations between education level, economic security level, subjective memory ability, and depressive symptoms in older adults. Finally, we used Model 4 from the SPSS macro developed by Hayes [ 54 , 55 , 56 ] to assess the parallel mediating role of economic security level and subjective memory ability in the relationship between education level and depressive symptoms in older adults. Based on a random sample of 5000, a bootstrapping method was used to estimate 95% confidence intervals to test the significance of the mediating effect. The results were considered statistically significant when the 95% confidence interval did not contain 0 [ 57 ].

Primary analyses

Table  1 is the descriptive statistical analysis results of the research population. Among the 4325 respondents included in this study, 2224 (51.4%) were men and 2101 (48.6%) were women. Respondents’ ages ranged from 60 to 95 years, with a mean age of 68.3 years (SD = 5.8). In terms of education level, 1,718 (39.7%) were illiterate or semi-literate, 970 (22.4%) had elementary school education, 942 (21.8%) had junior high school education, 563 (13.0%) had high school education, and 132 (3.1%) had college education and above. In terms of depressive symptoms, there were 792 (18.3%) older adults with a higher frequency of depressive symptoms. The median economic security level for the older adults is $125.8/month and the interquartile range is $329.6/month. Regarding the subjective memory ability of the older adults, 1268 (29.3%) could barely remember the main events that happened within a week, 761 (17.6%) could only remember a few, 1175 (27.2%) could remember half, 647 (15.0%) could remember most, and 474 (11.0%) could remember completely.

The results of the correlation analysis are shown in Table  2 . Educational level was significantly positively correlated with economic security level (ρ=-0.391, P  < 0.01) and subjective memory ability (ρ = 0.241, P  < 0.01), and significantly negatively correlated with depressive symptoms (ρ=-0.175, P  < 0.01); depressive symptoms were significantly negatively correlated with economic security level (ρ=-0.216, P  < 0.01) and subjective memory ability (ρ=-0.239, P  < 0.01); economic security level was significantly positively correlated with subjective memory ability (ρ = 0.210, P  < 0.01).

Parallel mediation analysis results

Table  3 ; Fig.  3 show the results of regression analysis in the mediating effect model. Under the control of gender, age, marital status, smoking status, alcohol consumption, neighborhood trust and kinship, the parallel mediating effect of economic security level and subjective memory ability between education level and depression symptoms of the older adults was tested. The results of model 1 show that education level has a significant negative predictive effect on depressive symptoms (β=-0.134, P  < 0.001). The results of model 2 and model 3 show that, education level was a positive predictor of economic security level (β = 0.467, P  < 0.001) and subjective memory ability (β = 0.224, P  < 0.001). Model 4 adds education level, economic security level, subjective memory ability and depressive symptoms to the regression model. it was found that education level, economic security level, and subjective memory ability were significant negative predictors of depressive symptoms (β= -0.039, P  < 0.05; β= -0.122, P  < 0.001; β= -0.169, P  < 0.001).

Parallel mediating roles of economic security level and subjective memory ability between education level and depressive symptoms in older adults. *** P  < 0.001, * P  < 0.05

Table  4 shows the results of the parallel mediated effects test with unstandardized effect values. The upper and lower limits of bootstrap 95% confidence intervals for the direct effect of education level on depressive symptoms and the mediating effect of economic security level and subjective memory ability did not include 0. This suggests that education level not only has a direct effect on depressive symptoms, but also has an effect through a parallel mediating effect of economic security level and subjective memory ability. The direct effect of education level on depression level was − 0.147, accounting for 28.99% of the total effect. The mediating effects of economic security level and subjective memory ability were − 0.217 and − 0.144, accounting for 42.70% and 28.30% of the total effect, respectively. The total mediating effect was − 0.361, accounting for 71.01% of the total effect.

Based on the 2020 CFPS database, this study explored the parallel mediating role of economic security level and subjective memory ability between education level and depressive symptoms in older adults. The results of the correlation analysis showed that the education level was positively correlated with the economic security level and subjective memory ability, and negatively correlated with depressive symptoms. The economic security level and subjective memory ability were negatively correlated with depressive symptoms. The results of parallel mediation tests showed that the economic security level and subjective memory ability mediated the relationship between education level and depressive symptoms in older adults. The education level may reduce depressive symptoms in older adults by increasing their economic security level and enhancing their subjective memory ability.

At present, the overall education level of Chinese elder people is low, nearly 40% of them are illiterate or semi-literate, and there is a large gap in education level among them. From the perspective of life course theory and cumulative advantage/disadvantage theory, these gaps in educational attainment during student years may affect people’s future work and social interactions, which in turn may have an impact on depressive symptoms in old age [ 10 , 58 ]. The results of this study show that the mental health of the older adults in China is generally good. There are 18.3% older adults with high incidence of depressive symptoms, and some older adults score close to 24 on CES-D8 scale, which shows that the depression of these people needs urgent attention. The 18.3% prevalence of depressive symptoms among older adults in China is at an intermediate level. The older adults in different countries have different levels of depression, which may be related to the national conditions and social environment. The prevalence of depressive symptoms in adults in the United States is 7.3% [ 59 ], and the proportion of older adults aged 60 and over in the United States who are diagnosed with depression is 8.18% [ 60 ]. The prevalence of depression increases with age, reaching 13.9% among people in their 60s and 70s and 18.4% among people in their 80s or above in Korea [ 61 ]. The proportion of older adults with clinically significant depressive symptoms in Biljand, Iran is 19.94% [ 62 ]. The prevalence of depressive symptoms among the older adults in Vietnam is 20.2% [ 63 ]. Therefore, the 18.3% depression rate of the older adults in China needs attention, and we should actively find measures to reduce the depression of the older adults.

The present study explored the relationship between education level and depressive symptoms in older adults, and the results showed a significant negative predictive effect of education level on depressive symptoms in older adults, which is consistent with previous studies [ 7 , 9 , 64 ], and hypothesis 1 was supported. Education is an important component of socioeconomic status [ 65 ], and the results of this study suggest that there may be potential clinical implications of changing educational patterns. Some scholars have considered the dual effect of education level and family background on depressive symptoms in older adults, and they found that individuals with low education from poor family backgrounds exhibited the highest levels of depressive symptoms [ 66 ], which reminds the government to pay special attention to the depressive status of older adults with low education levels. As indicated by the cumulative advantage/disadvantage theory, although high levels of education have a weaker protective effect on depressive symptoms in early adulthood, they have a stronger protective effect on depressive symptoms in old age, and the effect of education level on depression increases over time [ 19 ]. Higher levels of education can not only prevent major depressive disorder, but also change its presentation to a more anxious phenotype [ 40 ]. The educational level of older adults may also affect the educational level of their children, which in turn may affect their children’s intergenerational support and ultimately their own depressive symptoms [ 67 ].

There has been no study that combines retirement benefits, pension insurance and financial support from children to explore the mediating role of the economic security level in the relationship between education level and depressive symptoms in older adults. The mediating role of economic factors between education level and depressive symptoms in older adults has been explored: the study of Lingli Li suggested that family economic factors play a crucial role between these two factors [ 39 ], the study of Xiwu Xu showed a mediating role of economic level between education and depression [ 38 ], Sandro Sperandei suggested a mediating role of income level between these two factors [ 68 ], and Yaolin Pei demonstrated the mediating role of children’s financial support between education and depressive symptoms in older adults [ 67 ]. This study found that the economic security level plays a mediating role between education level and depressive symptoms in older adults, with a mediating effect of 42.70%, and hypothesis 2 was supported. Higher levels of education increase the level of financial security, which in turn reduces depressive symptoms in older adults. Life course theory and cumulative advantage/disadvantage theory suggest that the different stages of a person’s life are interconnected. The early education level may influence people’s employment and income levels [ 11 ], and even their perceptions of medical care and pension insurance choices [ 21 ], which in turn increases the gap in the economic security level in later life. In contrast, higher economic security levels can reduce depressive symptoms by alleviating financial stress and increasing confidence in the future among older adults [ 23 , 25 ].

In addition to the economic security level, this study also explored the mediating role of subjective memory ability. Subjective memory ability mediated the relationship between education level and depressive symptoms in older adults, with a mediating effect of 28.30%, and hypothesis 3 was supported. Higher education level enhances subjective memory ability, which in turn reduces depressive symptoms in older adults, which is similar to Xiwu Xu’s findings on the mediating role of cognitive level in older adults [ 38 ]. A possible biological explanation is that higher levels of education may improve cognition in the older adults, which in turn inhibits the expression of inflammatory cytokines and ultimately reduces the incidence of depression [ 39 ]. Based on cumulative advantage theory, early school education has exercised people’s cognitive and memory abilities [ 69 , 70 ], and these mindfulness exercises may influence people’s memory and cognitive abilities in their later life. In contrast, higher subjective memory ability can guarantee the living standards and well-being of older adults [ 34 ], which in turn reduces depressive symptoms in older adults. The results of the parallel mediation analysis showed that in terms of the effect of education level on depressive symptoms, the economic security level showed a greater mediation effect than subjective memory ability, and higher education level was more able to reduce depressive symptoms in older adults by increasing the their economic security level.

The main contribution of this study is to further clarify the mechanism between education level and depressive symptoms in older adults. It also reveals the critical role of the economic security level and subjective memory ability in this relationship, which enriches the research on the relationship between education level and depressive symptoms in older adults. The parallel mediation model of this study is based on life course theory, cumulative advantage/disadvantage theory and previous empirical research. The total mediation effect of economic security level and subjective memory ability is -0.361, and the effect ratio is 71.01%. From this, it can be seen that the level of economic security and subjective memory ability play a strong intermediary role between education level and depression symptoms of the older adults, so the results of parallel intermediary analysis are scientific and reasonable. In addition, the baseline sample of CFPS database used in this study covers 25 provinces/municipalities/autonomous regions in China, representing 95% of the population in China, and the sample is highly representative [ 71 ]. This indicates that the results of this study are basically in line with the actual situation of Chinese older adults, and the findings are of practical significance for reducing the level of depression among older adults, as well as for research on depression among older adults in other countries.

However, there are also some limitations of this study. Firstly, our study was cross-sectional in design, which limits our interpretation of the causal relationship between education level and depressive symptoms in older adults. Future scholars can verify the mediating role of the economic security level and subjective memory ability through cohort studies, and can further consider the heterogeneity in terms of urban and rural areas, males and females. Secondly, due to data limitations, we chose subjective memory ability rather than objective cognitive test results as a mediating variable, which may have led to bias in the study results. Future scholars can use the scale test results related to older adults’ cognitive abilities to validate the findings of this study. Finally, we considered only two mediating variables. Future studies can continue to include more specific mediating variables to explore the relationship between education level and depressive symptoms in older adults, such as lifestyle and physical health. Scholars can also explore the intermediary role between education level and depression symptoms of the older adults based on the national conditions and population structure of different countries.

The results of this study suggest that education level not only directly influences depressive symptoms in older adults, but also indirectly through the economic security level and subjective memory ability. Education level may reduce depression in older adults by increasing their economic security level and enhancing their subjective memory ability. In this era of general improvement in people’s education level, it reminds policy makers to pay attention not only to people’s overall education level, but also to education equity. Making a balanced distribution of educational resources among regions can, to a certain extent, promote mental health equity in people’s old age.

Data availability

The raw data is publicly available at https://www.isss.pku.edu.cn/cfps/index.htm .

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Acknowledgements

The authors are grateful for the data provided by the China Family Panel Studies (CFPS) conducted by the China Center for Social Science Research at Peking University. The authors thank all the editors and reviewers.

This study was supported by the Shandong Provincial Natural Science Foundation, China (ZR2023MG050).

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RZ conceived and designed the manuscript, ran the software, analyzed and interpreted the results, and wrote the first draft of the manuscript. JW proofread the final draft and controlled the quality of the articles. JL, ML, and JD revised the manuscript, proofread the manuscript and processed the figures and tables. DH and HF assist with literature searches and data organization. All authors contributed to the article and approved the submitted version.

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Zhao, R., Wang, J., Lou, J. et al. The effect of education level on depressive symptoms in Chinese older adults–parallel mediating effects of economic security level and subjective memory ability. BMC Geriatr 24 , 635 (2024). https://doi.org/10.1186/s12877-024-05233-5

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DOI : https://doi.org/10.1186/s12877-024-05233-5

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