research paper about a standard temperament analysis program

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Peer-reviewed

Research Article

TECLA: A temperament and psychological type prediction framework from Twitter data

Contributed equally to this work with: Ana Carolina E. S. Lima, Leandro Nunes de Castro

Roles Conceptualization, Investigation, Methodology, Software, Writing – original draft

* E-mail: [email protected]

Affiliation Natural Computing and Machine Learning Laboratory, Mackenzie Presbyterian University, São Paulo, Brazil

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – review & editing

• Ana Carolina E. S. Lima, 
• Leandro Nunes de Castro

• Published: March 12, 2019
• https://doi.org/10.1371/journal.pone.0212844
• Reader Comments

Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user’s social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey’s model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair.

Citation: Lima ACES, de Castro LN (2019) TECLA: A temperament and psychological type prediction framework from Twitter data. PLoS ONE 14(3): e0212844. https://doi.org/10.1371/journal.pone.0212844

Editor: King-wa Fu, The University of Hong Kong, HONG KONG

Received: May 10, 2018; Accepted: February 12, 2019; Published: March 12, 2019

Copyright: © 2019 Lima, de Castro. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data underlying the study is third-party data belonging to the authors of the "Personality Traits on Twitter—or—How to Get 1,500 Personality Tests in a Week". This data is available from https://bitbucket.org/bplank/wassa2015 . The authors of the present study confirm that they did not have any special access to this data that others would not have.

Funding: This work was supported by Mackenzie Presbyterian University, Mackpesquisa, CNPq, and Capes to ACESL as well as FAPESP. Intel also supported this research as an Artificial Intelligence Center of Excellence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: Intel supported this research as an Artificial Intelligence Center of Excellence. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The study of psychological types or temperament lead us to the understanding of how a person relates with the world, either by the choices he makes or the way he absorbs information. For a long time, this theme has been researched and associated with well-being, lifestyle, employment, leadership, study, etc. One way of knowing a person’s psychological type is by submitting him to questionnaires about his habits and choices, for example the MBTI (Myers-Briggs Type Indicator), which returns the psychological type of a person and is based on the studies of Jung and Myers-Briggs, and the Keirsey Temperament Sorter (KTS), which returns a profile associated with the temperament taxonomy created by David Keirsey.

In general, such forms involve many questions and can be biased by the environment in which the respondent is. One way to balance this bias would be to extract information in a passive way, for example, in the interactions (posts, likes, etc.) within social media, a service increasingly present in our daily lives. Social media can be seen as repositories of actions, behaviors and preferences that can be mapped onto psychological features. This occurs due to a user-free content creation, where each person has a role in creating and sharing content [ 1 ]. Wiszniewski and Coyne [ 2 ] argue that whenever an individual interacts in a social sphere he paints before himself a mask of his identity that becomes even more pronounced as the individual needs to fill in a profile.

The goal of this research is to identify if there are behavioral patterns in the information shared in social media that can be mapped with high precision into the psychological types of the MBTI or the temperaments of Keirsey. This is, therefore, an exploratory paper on the ability of traditional text mining techniques and natural language processing to assist in the extraction and classification of patterns. From our literature review we expand the combinations of text pre-processing techniques and classification algorithms in relation to the papers presented here. We also mapped a database of MBTI results in the Artisan, Guardian, Idealist and Rational types in order to demonstrate the applicability also in the concept of temperament proposed by David Keirsey. In terms of application, it is useful for the preparation of marketing campaigns, more accurate hiring and promotion processes, turnover reduction, improvement of working environment quality, and many other applications related to human capital recruitment, selection and maintenance.

The combination of human behavior research and text/data mining techniques provides insights about the virtual persona, such as his/her influence on others [ 3 , 4 ], how much they trust one another [ 5 , 6 ], their life satisfaction [ 7 ], personality [ 8 , 1 , 9 , 10 , 11 , 12 ], emotions [ 13 , 14 ], political preferences [ 15 , 16 ], emotion and mood state [ 17 , 18 ], depression [ 19 , 20 ], disorders [ 21 , 22 ], among many others.

The goal of automating the prediction of temperament and psychological type is not to replace the use of tests already validated, but, instead, to provide a new tool based on a completely different and passive data to support specialists. More specifically, this research will be based on Twitter data as case study, mainly due to its flexibility in providing open data for collection and analysis. This paper presents a series of classifiers evaluations to map the behavior of social media users, based on their Twitter posts, in relation to the temperament and psychological type and summarize the methodology in a structure called Temperament Classification Framework (TECLA).

To assess the performance of the proposed framework we used a dataset from the literature containing over a million tweets from 1,500 users. Five classification algorithms were evaluated: Naïve Bayes (NB); Support Vector Machines (SVM); Decision Tree (J48); Multilayer Perceptron (MLP); and K-Nearest Neighbors (KNN). We compare these algorithms with Twitter features and three text representation schemes (MRC, LIWC, Apache OpenNLP) to find a suitable combination to determine the temperament and psychological types based on Twitter messages.

The paper is organized as follows. Section 2 provides a brief historical perspective on temperament theories, emphasizing the models proposed by Myers-Briggs and later Keirsey. Section 3 brings a brief review of the works in the literature dealing with the automatic classification of temperament and psychological types. The Temperament Classification Framework (TECLA) is presented in Section 4, and its performance is analyzed in Section 5. The paper is concluded in Section 6 with a general discussion and perspectives for future research.

A Brief historical perspective on temperament theories

Temperament characterizes a set of mental tendencies related to the way someone perceives, analyzes and makes daily decisions [ 23 ]. It represents the uniqueness and intensity of psychic affects and the dominant structure of mood and motivation in each individual. It is a form of reaction and sensitivity of a person to the world, which is revealed by his/her attitudes and behaviors, thus composing his/her organic basis [ 24 ]. This set of trends is innate, that is, it appears from birth, and is closely linked to biological or physiological determinants, which therefore change relatively little with development [ 25 ]. It can change and weakens throughout life, but it is never eliminated [ 24 ]. In the present research, temperament is defined as a set of innate and hereditary tendencies , responsible for how one perceives and interacts with the world .

The literature is filled with different terminologies to refer to temperament, based on the authors’ view of such characteristics. For instance, Hippocrates called it the four humors, Carl Jung, Isabel Myers Briggs and Katharine Cook Briggs called it psychological types, and Carlos Galeno and David Keirsey, called it temperament [ 25 , 26 , 27 ]. We summarize the temperament as a concept that converges to a set of innate characteristics of an individual, closely linked to biological or physiological determinants, which change relatively little during the personal development [ 25 ].

We adopted the temperament model proposed by David Keirsey [ 27 ] and the psychological types introduced by Myers and Briggs [ 28 ]. Keirsey’s model maps temperament into four types: artisan; guardian; idealist; and rational. This model is widely accepted for the understanding of professional trends, thus being potentially applicable in recruitment and selection processes, promising areas for social media data analysis. The Myers and Briggs’ model has a set of 16 psychological types that were investigated and defined from the studies of Carl Jung on the psychological types.

Carl Gustav Jung proposed one of the most comprehensive and well-known temperament typologies in his book Psychological Types [ 29 ]. Jung analyzed the temperament according to the workings of the mind. For him the mind is composed of an association between attitudes and functions . The attitudes ( extroversion (E) and introversion (I)) would be the source of psychic energy and the functions correspond to the way each individual acquires and processes information. Jung related four functions, two referring to obtaining information: sensation (S) and intuition (N); and two for decision-making: thought (T) and feeling (F) [ 25 ]. Then, Isabel Myers and Katheryn Myers Briggs added a new pair of functions: judgment (J) and perception (P), which assess whether an individual’s orientation to the outside world comes from a rational ( judging ) or irrational ( perceiving ) function.

D. Keirsey [ 27 ] focused his research on the parallel between the Myers-Briggs taxonomy and the observation of temperament in action at the time of choices, behavior patterns, logic and consistency. He assumed that the temperament associated with character forms the personality of the individual; the temperament being innate and the character emergent, developed by the interaction of temperament with the environment. Thus, the types are driven by aspirations and interests, which is what motivates us to live, act, move and play a role in society [ 27 ]. He noted that the interests and aspirations are more related to the perception (S-N), totally instinctive, more than to decision-making (T-F), which is fully rational. The sensation (S) can combine with judgment (J) or perception (P), while intuition (N) with feeling (F) or thinking (T). This observation resulted in four temperament types: Guardian (SJ); Artisan (SP); Idealist (NF); and Rational (NT) [ 23 , 27 ].

Although the characteristics of Myers-Briggs model is binary (dichotomic), there are studies that suggest that a better representation would be continuous with degrees of belonging to each function and attitude [ 30 , 31 , 32 ]. The inventory provided by Myers and Briggs aims to determine which of two functions or attitudes is preferred. The score indicates the tendency in the dichotomy. Results with low scores suggest a tension between the opposite pairs rather than an indication of equal preference. However, the tension is unclear whether the equal represents strength in both pair, equal weakness in both areas, or equal neutrality in both areas [ 33 ]. We have adopted the binary standard due to our methodology for acquiring a dataset since the disclosure of the MBTI result by a social media user occurs through the label (ENTJ, INFP, etc.), without direct association with the score in each pair.

Automatic temperament classification: A literature review

Understanding social media users involves the analysis of their behaviors and interactions in social media, like their followers, mentions, messages, friends, photos, videos and comments. Understanding the users means being able to quantify and qualify how they present themselves [ 34 ]. The automatic recognition of temperament by means of computational techniques can help many business sectors and social researchers in understanding social media users. To date, there are only a few works related to the automatic temperament/psychological types classification in the literature, that is, Keirsey and MBTI labels. The main reason for the scarcity of works in this area is the difficulty in finding data for training classifiers. This section provides a review of the specific works found in the literature related with these two topics. Although there are many other works addressing the prediction of user characteristics from social media data, these are out of the scope of the present paper.

Luyckx and Daelemans [ 35 ] created a 200,000-word Personae corpus consisting of 145 undergraduate student essays about an Artificial Life documentary written in Dutch. Besides, the students submitted their MBTI profile. In this work, the authors performed an authorship attribution and personality prediction. The Memory-Based Shallow Parser (MBSP), n-gram and Lexical features were used to extract the text features. For personality prediction, a 10-fold cross-validation training was performed with a method based on the K-NN algorithm, called TiMBL (Memory-based learning). The experiments contained 84 binary classification tasks, each one for the MBTI dichotomy. The authors concluded that the prediction of introverted-extraverted and intuitive-sensing were fairly accurate, with average F-measures of 65.38% and 61.81%, respectively.

Komisin and Guinn [ 36 ] developed a system based on the classification of documents to determine the psychological type according to Myers-Briggs model. In their experiments, they used a Naïve Bayes classifier and Support Vector Machines. Data were collected as part of a postgraduate course in conflict management offered to undergraduate students, in which students performed the MBTI and Best Possible Future Self (BPFS) tests. The BPFS contains self-descriptive elements, in present and future, in different contexts (e.g., work, school, family, finances). Data were collected over three semesters between 2010 and 2011. The n-gram and Linguistic Inquiry and Word Count (LIWC) were used to provide a representation of texts. The authors concluded that the dichotomies Thinking/Feeling (T/F) were predicted with over 75% accuracy for the precision and recall measures using Naïve Bayes with leave-one-out cross validation. For the Intuitive/Sensing (N/S) dichotomy, the LIWC features resulted in less successful predictions. Introversion/Extroversion (I/E) and Judgement/Perception (J/P) did not achieve good precision and recall results.

Brinks and White [ 37 ] used various algorithms to detect the Myers-Briggs temperament types in tweets. The aim of the project was to develop a computer system capable of performing the function of the human analyst trained to apply the MBTI based on textual communication. The authors argued that although the results of the MBTI are confidential, many individuals openly reveal their type in a variety of ways and media, including Twitter. They showed that, in a search on Twitter with the term “#INFP” messages were found such as: “I just reread the Myers-Briggs description of my #INFP personality type. It’s scary accurate”. Thus, the data were collected from users that revealed their temperament profiles. 6,358 Twitter users were observed and it were collected two hundred tweets from each. In total, it was analyzed 960,715 tweets. On average, classifiers achieved a precision of 66.25%.

Plank and Hovy [ 38 ] collected 1.2 million tweets classified according to the Myers-Briggs system. For these, the authors monitored messages that mentioned any of the 16 types associated with the words Briggs or Myers. Thus, they obtained 1,500 different users, and collected between 100 and 2,000 of their latest tweets, resulting in a corpus of 1.2 million tweets. The authors structured the messages using n -grams, in addition to the genre information, tweets count, number of followers, number of followings, among other service features. One goal was to find out which attributes would be more characteristic in each dimension of the Myers-Briggs model. They used logistic regression to analyze the attributes in each dimension and concluded that the data can provide enough linguistic evidence to predict the dimensions reliably: Introversion/Extroversion and Feeling/Thinking.

Verhoeven et al [ 39 ] created a MBTI dataset in six languages (Dutch, German, French, Italian, Portuguese and Spanish) with 18.168 users and approximately 34 million tweets in total distributed among the languages. They used the same methodology presented in [ 38 ] to collect the data. After the construction of the database, the authors performed classification tests to predict both gender and Myers-Briggs personality dimensions (I/E, N/S, T/F and J/P). For the experiments the authors used 200 tweets per user and discarded those who had fewer than 200 messages. The authors used LinearSVC with standard parameters with n -grams. The classification was performed using 10-fold cross-validation. Considering all languages, the average F-measure for the I/E dimension was 67.87%, 73.01% for the N/S dimension, 58.45% for the T/F dimension, and 56.06% for the J/P dimension.

Lukito et al. [ 40 ] used Twitter as data source in Indonesia to predict personality and performed an MBTI psychological test with a user base of 97 people. Approximately 240,000 tweets were collected, an average of 2,500 tweets per Twitter user. They selected 15 users for testing and changed the training set size according to the experiment. The classification algorithm used was Naïve Bayes and the messages were structured by n -gram and POS-tag. The best result was achieved for the I/E dichotomy with 80% accuracy, the other dichotomies had the same 60% accuracy levels. The authors compared their results with the work proposed by [ 38 ], concluding that their proposal was superior for the pairs I/E and J/P, being the latter one of the most difficult to predict.

Lima & de Castro [ 1 ] developed a framework called TECLA to predict temperament types (Artisan, Guardian, Idealist, and Rational). The dataset with approximately 29.200 tweets was collected from Twitter. They used LIWC text representation and Twitter user’s account information (like tweets count, number of followers, and number of followings). The authors used NB, KNN, SVM and Decision Tree algorithms to evaluate the proposal. The best accuracy results were in Artisan and Guardian with 87.67% and 83.56%, respectively. The accuracy did not exceed 60.27% for the Idealist temperament and 58.90% for Rational.

Table 1 shows a summary of the papers found, detaching the classification algorithms, main features and performance measures used. It also presents the best results obtained based on the measures adopted. The results of [ 37 , 38 , 40 ], all based on tweets, suggest a higher predisposition for I/E and N/S pairs. The F-measure in [ 36 ] was obtained from the Precision and Recall.

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https://doi.org/10.1371/journal.pone.0212844.t001

TECLA: The temperament classification framework

The Te mperament Cla ssification Framework (TECLA) was developed as an outcome of the use of text mining and natural language processing techniques to classify the temperament or psychological type of social media users. The goal is to provide a modular structure that allows us to use and evaluate different techniques quickly and intuitively. Furthermore, it follows the main steps of KDD ( Knowledge Discovery in Databases ) [ 41 ]. Hence, the TECLA has the following modules: data acquisition module ; message preprocessing module ; temperament classification module ; and evaluation module . Each one of them will be detailed in the following.

Data acquisition module

The data acquisition module is responsible for monitoring and receiving information from the users to be classified. For example, in the case of Twitter, it is necessary to obtain usage information, such as number of tweets, number of followers and followed, plus a set of tweets.

Message pre-processing module

The TECLA framework does not work directly with the tweets, but uses information extracted from them, called meta-attributes. Such information can be divided into two categories: grammatical and behavioral. The behavioral category extracts information about the social media use and is specific to each type of media. In the case of Twitter, it includes the number of tweets, number of followers, followed, favorites, number of listings and number of times the user was favorited. The grammar category considers information from LIWC [ 42 , 43 ], MRC [ 44 ], sTagger [ 45 ], or oNLP [ 46 ], extracted from the user’s set of messages, similarly to what was proposed in the Polarity Analysis Framework introduced by the authors [ 14 ]. Therefore, the message pre-processing module is responsible for extracting meta-attributes from the data (usage and message corpus) and building a new base, called meta-base, from the extracted meta-attributes. The list of meta-attributes used in TECLA is summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0212844.t002

Temperament classification module

The temperament classification module infers a temperament from the characteristics (meta-attributes) extracted in the previous module. In principle, this module is based on the application of a specific algorithm and can incorporate any kind of classifier. For the classification of the MBTI model, the system was designed with four classifiers ( Fig 1 ) that receive the same data, but is trained to identify the opposing pairs of attitudes and functions. A classifier is trained and responsible for defining the attitude (Extroversion/Introversion—E/I) and the others the functions (Intuition/Sensation—N/S, Thinking/Feeling—T/F, Judgment/Perception—J/P), all trained in isolation. These classifiers were called decomposing classifiers .

https://doi.org/10.1371/journal.pone.0212844.g001

Each of these classifiers is binary, so the answer is either Extroversion or Introversion, Intuition or Sensation, Thought or Feeling, Judgment or Perception. After training, the response of the four classifiers will define the psychological type, e.g., ISTJ or ENFP (Section 2). Therefore, the psychological type of each user was split into four binary classes. The user may be extroverted or introverted, intuitive or sensory, thinker or sentimental, and judgmental or perceptive, as illustrated in Fig 2 .

https://doi.org/10.1371/journal.pone.0212844.g002

For the classification based on the Keirsey model a sequence of classifiers was constructed. As pointed out in [ 47 ] one of the strategies to work with multiclass classifiers is the combination of classifiers generated in binary subproblems. With this, there is a decomposition of the problem into binary problems. Separating the problem into binary classifiers can reduce the computational complexity involved in solving the total problem with simpler subtasks. In this case, the classifier has the same scheme shown in Fig 1 , however, the first classifier that returns the result “1” will determine the class of the object, as illustrated in Fig 3 .

https://doi.org/10.1371/journal.pone.0212844.g003

Evaluation module

In order to measure the TECLA performance, it was used the accuracy, F-measure, which involves precision and recall, and the area under the ROC curve (AUC). Accuracy is the number of objects correctly classified over the sum of all objects. The F-measure represents the harmonic mean between precision and recall, where precision is the percentage of a class correctly classified and recall is the number of objects correctly classified over the total number of objects that really belong to that class [ 48 , 49 ]

Performance assessment

The goal of this study is to design a temperament predictor that can infer the temperament of a certain individual (social media user) based on what he writes in the social media, instead of applying him a specific temperament test. This is a very interesting and promising approach, because it allows one to know someone’s temperament in spontaneous situations. To assess the performance of TECLA we used a recent, public dataset with over one million tweets.

Data acquisition

The database used comes from the [ 38 ] paper, in which the Twitter users are classified according to the psychological types of Myers-Briggs. The dataset contains 1.2 million tweets from 1,500 users. The number of tweets varies from one user to another. To be part of the database a user needs to have at least 100 tweets and we downloaded at most 2,000 tweets per user. The attributes available and useful are: MBTI; gender; number of followers; number of tweets; number of favorites; and number of listings. Table 3 shows the user distribution for each psychological type of the Myers-Briggs taxonomy. Although considered rare, the intuitive types, especially the INFP and INTJ, were the most common types within the collected database. By contrast, the sensory types (ESFJ, ESTJ, ESFP, ESTP, ISFP, ISTP, ISFJ and ISTJ) accounted for only about 21% of the data.

https://doi.org/10.1371/journal.pone.0212844.t003

The ratio between each element of the E/I, N/S, T/F, and J/P pairs can be seen in Table 4 . There is a clear imbalance between the N/S pair, which may reflect the classification results. However, for this study, no class balancing was performed because it would imply a reduction in the number of users in other pairs.

https://doi.org/10.1371/journal.pone.0212844.t004

To evaluate the Keirsey model, each MBTI type was mapped into its model (Artisan, Guardian, Idealist and Rational). Table 5 describes the number of users by temperament. The Artisan and Guardian classes have the smallest number of users, because of the predominance of intuitive in the database (Idealists and Rational).

https://doi.org/10.1371/journal.pone.0212844.t005

Pre-processing

The attributes provided by the Plank dataset are called behavior attributes, in reference to the behavior of users in the microblog. Table 6 shows the average value of the behavior attributes for each temperament (followers, statuses, favorites, listed and gender). In all temperaments/psychological types the predominant gender was female. In the N/S pair we emphasize the fact that the sensorial ones have, on average, more followers and tweet more frequently, although this is the function with fewer representatives in the database (only 22.53%). The difference between Guardians and Artisans, both sensory, is greater in relation to the number of followers and listed count. On the other hand, among the intuitive there is a greater balance in the way of using the microblog.

https://doi.org/10.1371/journal.pone.0212844.t006

Experimental results

All tests were performed with 10 runs of a k -fold cross-validation ( k = 5). First the results will be presented for the Keirsey model, then the MBTI model. In both cases, it is expected to show the ability of the classifiers to infer each of the classes, that is, if from the input data it is possible to identify an Artisan, Guardian, Idealist or Rational person, or, based on the MBTI model the pairs E/I, N/S, T/F, J/P. In all cases the measures adopted to evaluate the classifiers were the accuracy per class ( percentage of correct classification per class , ACC ), the F-measure ( F ), which is the harmonic mean between Precision and Recall , as discussed previously, and the area under the curve (AUC). The AUC is a summary of the ROC curve (sensitivity versus specificity), and high levels in AUC indicate that, on average, the true positive rate is higher than the false positive rate. The following classifiers were evaluated: AdaBoost; Bagging; J48; Naïve Bayes; Random Forest; and SVM. All classifiers used are from the Python library Scikit-learn 0.19.1 with default settings. We used a workstation with an Intel Core i5-3210M @ 3,10 GHz, 3 MB smart cache, quad-core on hyper-threading, 6 GB RAM memory, 904 GB HD @ 5400 RPM and Windows 8.1 operation system.

Results for the keirsey model.

The following tables show the test results for the Keirsey model: Artisan; Guardian; Idealist; and Rational. The goal is to answer the following question: “ Is it possible to infer the user’s temperament based on his posts ?”. Our tests began with an attribute analysis to understand the best possible configuration. We performed a ranking of the importance of the attributes based on the information gain to perform attribute selection tests and analyze the best results by observing the accuracy and F-measure. Note that our technique separates binary classifiers for each temperament, so the results are divided into ACC, F-measure for class 0 ("No", which means does not have the temperament), F-measure for class 1 ("Yes", which means has the temperament), and AUC of positive result (“Yes”).

Our first analysis refers only to the Twitter attributes and Table 7 below summarizes these results. It is possible to note that, in general, there is a tendency for the classifiers to choose the "No" class, which is the predominant class. Thus, the F-measure for the "Yes" is low. By comparing the ACC and AUC the best result was achieved with the Random Forest using the 5 attributes (total number of tweets posted by the user so far, number of followers, number of followed, number of times the user was listed, and number of times the user was favorited).

https://doi.org/10.1371/journal.pone.0212844.t007

We proceeded testing in scenarios in which the Twitter attributes would not be available, but only the text of tweets. For this case, we have tested three text structuring techniques separately, as mentioned in the pre-processing section: MRC, LIWC and oNLP. Note that the performance had the same behavior of the previous evaluation with a low F-measure for the "Yes" class, indicating the trend in the classifiers for one of the classes. Table 8 presents the best performance with 9 attributes with the Random Forest (87.48%±0.25%) and Bagging (83.23%±0.42%) algorithms. The combination SVM + MRC Features was not successful, because the algorithm could not identify patterns for the class Yes (0.00±0.00%).

https://doi.org/10.1371/journal.pone.0212844.t008

The LIWC showed a better performance, which could be noticed in the AUC measure. By analyzing the results we observed that the Random Forest performance is usually superior; the best accuracy was 87.99%±0.29% with 25 attributes ( Table 9 ). In general, there was no significant change in accuracy and the choice for these attributes was due to the F-measure (Yes). However, there was substantial improvement in the AUC value. Thus, the best performance was obtained by the Random Forest with 25 attributes: 91.14%±0.13% for the F-measure (No); and 70.52%±0.81% for the F-measure (Yes).

https://doi.org/10.1371/journal.pone.0212844.t009

Similarly to the Twitter and MRC results for oNLP, Bagging and Random Forest (also J48) achieved an AUC above 70%, indicating a better identification of “Yes”. By observing the other measures, again the Random Forest algorithm had the best performance with the oNLP 24 attributes ( Table 10 ). Therefore, the average accuracy was 87.60%±0.33%, the average F-measure (No) was 90.95%±0.31%, the average F-measure (Yes) was 69.68%±0.63% and AUC was 86.12%±0.76%.

https://doi.org/10.1371/journal.pone.0212844.t010

Based on its superior performance for all text representation mechanisms, Table 11 details the results of the Random Forest algorithm. By observing the different text representations, the best classification result occurred in the Artisan temperament with 96.46%±0.27% of accuracy for LIWC (25 attributes). These results suggest that the system can be more precise to find what is not Artisan, with all features with an average F-measure of 97.60%±0.24% for Twitter, 98.09%±0.22% for MRC, 98.11%±0.14% for LIWC and 98.08%±0.13% for oNLP. This can also be observed for the Guardian with F-measure (No) of 94.66%±0.30% for Twitter, 95.42%±0.24% for MRC, 95.61%±0.24% for LIWC and 95.51%±0.25% for oNLP. For the idealist the classifier was able to better discriminate the two classes. In the best scenario (LIWC 25 features) the F-measure (No) was 81.47%±0.50% and F-measure (Yes) 74.89%±0.87%. The AUC measure remained constant in all temperament types, around 80%, indicating a low false positive rate.

https://doi.org/10.1371/journal.pone.0212844.t011

Results for the MBTI Model.

The second set of results presented here are for the decomposed classifiers for the MBTI model. Each classifier is responsible for one of the MBTI pairs. In all cases, the same classification algorithm will be run for all classifiers. The goal is to answer the following question: Is it possible to identify the user's psychological attitudes and functions based on what he/she writes in social media ? If it is possible, then a deeper understanding of the virtual persona can be achieved by analyzing social media data. As our previous analysis with the Keirsey model prediction, we also performed an attribute analysis for the MBTI model prediction. Table 12 summarizes the results of the Twitter attributes’ evaluation. Both F-measure (No) and F-measure (Yes) have a value less than 70%, except for Bagging (71.97%±0.34%) and Random Forest (79.29%±0.23%) with 5 attributes, that is, with all the original attributes of the dataset. Both algorithms also achieved high AUC values indicating a good performance for the positive class.

https://doi.org/10.1371/journal.pone.0212844.t012

For the MRC attributes ( Table 13 ), we observed a better performance with 16 attributes. As in the Twitter case attributes, the Bagging and Random Forest algorithms also had a better performance, mainly when we compare the AUC, 81.26±0.46 for Bagging and 87.06±0.25 for the Random Forest. Also, the F-measure, 70.02%±1.15% / 74.46%±0.49% for Bagging and 78.80%±0.62% / 79.13%±0.37% for the Random Forrest. In general, as in the Twitter attributes, the performance of the classifiers was higher when compared with the Keirsey model classification in relation to the F-measure balance.

https://doi.org/10.1371/journal.pone.0212844.t013

The results for the LIWC attributes analysis, presented in Table 14 , show a better performance (AUC) for 24–28 attributes with the best result for 27 attributes associated with the Bagging (82.85±0.43), J48 (77.04±0.43) and Random Forest (87.79±0.56) algorithms. This suggests that the LIWC attributes may better characterize the problem when compared with the previous results also for the Naïve Bayes, AdaBoost and SVM.

https://doi.org/10.1371/journal.pone.0212844.t014

As with LIWC, the oNLP ( Table 15 ) results were satisfactory for Bagging, J48 and Random Forest, mainly for 22 attributes. The highest accuracy was 82.15%±0.14% for the Random Forrest. The Naïve Bayes classifier had the worst accuracy level with only 60.69%±0.13%. AdaBoost and SVM achieved, respectively, 65.02%±0.16% and 64.75%±0.15% of accuracy. Comparing the AUC results, the SVM had the worst performance.

https://doi.org/10.1371/journal.pone.0212844.t015

Table 16 details the Random Forest algorithm results due to its overall superior performance. For the studied database it was possible to predict the E/I pair with a mean average accuracy of 82.05%±0.65% for the oNLP features. The F-measure (No) indicates the first letter in the pair. In the E/I case, the Random Forest with oNLP features had an F-measure for Extroversion of 87.12%±0.44% and 70.38%±1.26% for Introversion. The pair S/N achieved 88.38%±0.68% of accuracy also with oNLP. The F-measure for N (intuition) was 92.66%±0.41% and 72.13%±1.94% for S (Sensation). In T/F the accuracy was 80.57%±0.80% for LIWC with 27 attributes, 84.49%±0.63% of F-measure to F (Feeling) and 74.01%±1.15% of T (Thinking) F-measure. The pair J/P had the lowest accuracy of 78.26%±0.79% (LIWC with 27 attributes). The precision was better in J (Judging) with 81.49%±0.66% of F-measure. Like the Keirsey type prediction, the AUC indicates a good performance of true positive in relation to false positive rate.

https://doi.org/10.1371/journal.pone.0212844.t016

Comparing with results from the literature

Finally, in Table 17 we compare our Keirsey and MBTI results with the literature. By analyzing the results from [ 38 ] our performance was superior for all MBTI pairs. We have also been more effective in the I/E and N/S pairs, however the use of Random Forest combined with other forms of text representation has promoted better performance for T/F and J/P pairs. For the classification results of Keirsey temperaments we compared with previous results obtained in the first steps to build this tool. In this case, we have also achieved an increase in performance. The F-measure in [ 36 ] was obtained from the Precision and Recall.

https://doi.org/10.1371/journal.pone.0212844.t017

Discussion and future trends

The purpose of this paper was threefold: to provide a brief historical review on temperament theories; to make a brief survey of machine-learning research on temperament and psychological type prediction; and to investigate the temperament and psychological types prediction based on data produced by social media users. In this latter contribution, the hypothesis this work tries to validate is if it is possible to predict the virtual persona temperament without using a questionnaire, that is, to use artificial intelligence techniques to understand and classify the profile of users based on what they share and how they behave in social media. The importance of this tool lies in trying to lessen a possible bias provided by questionnaires, when a user knows he is being explicitly evaluated.

From the literature review we seek to extend our previous results [ 14 ], both on text processing techniques and algorithms for building predictive models. With this, we present a set of results based on the combination of different text structuring techniques and classification algorithms. Derived from the proposals presented by [ 37 ] and [ 38 ], on Twitter data, we aim to identify the ability of the models to estimate the temperament typology proposed in [ 27 ].

User analysis was performed using the database provided in [ 38 ], composed of MBTI results and transformed into the Keirsey model, thus performing classification tests for both models. The results pointed to the use of Random Forests with LIWC structuring for the Keirsey model (96.46% of accuracy for Artisan, 92.19% of accuracy for Guardian, 78.68% of accuracy for Idealist, 83.82% of accuracy for Rational), and LIWC or oNLP for the MBTI (82.05% accuracy for E/I pair, 88.38% accuracy for S/N pair, 80.57% accuracy for T/F pair and 78.26% accuracy for J/P pair).

We believe in the importance of understanding the behavior of users on social media, and we also believe that information such as psychological types can help in this regard. This information can serve as input to many profiling systems in various areas. Here, we did an exploratory study aimed at understanding the potential of machine learning techniques for temperament identification. We would like to expand this research to new databases both from Twitter and other social media in order to explore the framework potential. We would also like to present case studies applying TECLA to different groups of users, and thus answer questions such as: What are the profiles of people who talk about the same subject? What is the profile of people who watch a TV show, movie or series? What is the profile of people who consume a specific product or service?

Finally, further research will also assess the computational scalability of TECLA when using High Performance Computing (HPC) platforms. We performed some preliminary experiments in this direction with one of the best scenarios obtained in this paper (i.e., Random Forest for the Keirsey model and Twitter 5 features) using an Intel Xeon Platinum 8160 processor @ 2.10 GHz, each one with 24 physical cores (48 logical) and 33 MB of cache memory, 190 GB of RAM and obtained a significant gain in performance. As social media data arrives continually, a comprehensive set of experiments will be performed to assess the scalability of TECLA in HPC platforms.

Acknowledgments

This work was supported by Mackenzie Presbyterian University, Mackpesquisa, CNPq, and Capes to ACESL as well as FAPESP. Intel also supported this research as an Artificial Intelligence Center of Excellence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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REVIEW article

Temperament and school readiness – a literature review.

• 1 Department of Christian Education, Sts Cyril and Methodius Faculty of Theology, Palacký University, Olomouc, Czechia
• 2 The Center of Evidence-Based Education and Arts Therapies, Faculty of Education, Palacký University, Olomouc, Czechia

This review study was conducted to describe how temperament is related to school readiness. The basic research question was whether there is any relationship between later school success and temperament in children and, if so, what characterizes it. A systematic search of databases and journals identified 27 papers that met the two criteria: temperament and school readiness. The analytical strategy followed the PRISMA method. The research confirmed the direct relationship between temperament and school readiness. There is a statistically significant relationship between temperament and school readiness. Both positive and negative emotionality influence behavior (especially concentration), which is reflected in the approach to learning and school success.

Introduction

Temperament, as a cluster of mental attributes that are presented in the form of experiencing and reacting to stimuli with an effect on emotional expressions and behavior, has an effect on school results amongst children ( Keogh, 2003 ). For school education, therefore, what is important is how the child is able to manage its temperament and project it into activity, perseverance, and balance in response to stimuli ( McClelland and Wanless, 2012 ).

The aim of this review study was to identify the relationship between the temperament of the child and school readiness presented in the scientific literature and how the research activities were constructed.

The definitions of temperament are not uniform in their conception and differ with different authors. Three basic theories have been put forward in relation to temperament in human life during its historical development: physiological theories Hippocrates or Galen ( Ashton, 2013 ), bio-ecological theories (e.g., Thomas and Chess, 1977 ), and behaviorally oriented theories (e.g., Thomas and Chess, 1977 ). In the context of temperament research, current studies indicate terms that refine temperament and its manifestations, such as executive functions, effortful control, and self-regulation. Two basic research questions were identified in the context of the objective.

1. Are there studies that describe the relationship between temperament and school readiness and subsequent success rates in children?

2. If so, how can this relationship be characterized?

Theoretical Background

Temperament.

Temperament is the focus of scientists’ interest in psychology. Perhaps the most prevalent are theoretical approaches to temperament as defined by Buss and Plomin (1975) , Thomas and Chess (1977) , Rothbart and Derryberry (1981) , Goldsmith and Campos (1982) , and Kagan (1984) .

The Kagan approach ( Kagan, 1984 ) is constructed based on biological factors that he considered congenital and may affect behavior. Goldsmith and Campos (1982) provide a definition of temperament as an individual difference in the ability to experience and express primal emotions. Differences in temperament are observable in the intensity of behavioral expressions, facial expressions, gestures, and movements. The definition, which is constructed on the basis of nine dimensions of behavioral styles – activity level, regularity, approach withdrawal, adaptability, threshold of responsiveness, intensity of reaction, quality of mood, attention span/persistence, and distractibility – was used by Thomas and Chess (as cited in Pharis, 1978 ). The model that was designed by Buss and Plomin (1975) was behavior-genetics oriented. It is assumed that early manifestations of temperamental features are hereditary and adapt evolutionally in a child, as responses to its living conditions, and are also relatively stable. Three core dimensions were identified: emotionality (E), activity (A), and sociability (S). The above-mentioned authors represent the primary sources to which most later studies relate. The approach to temperament by Rothbart ( Rothbart and Derryberry, 1981 ) defines temperament as biologically ingrained individual differences in reactivity and self-regulation in emotional, activation, and attention-based processes. Reactivity refers to levels of biological arousal caused by changes in internal and external stimulation, which are captured as dimensions of negative influence and surgency. Self-regulation applies to processes that modulate reactivity and are reflected in a temperamental dimension that requires effortful control.

Temperament is accompanied by relatively permanent individual differences in reactivity and self-control that can be influenced in the course of the child’s development by maturation and experience ( Rothbart and Bates, 1998 ). Differences in temperament are apparent from early childhood, with some children tending toward negativity and bad moods, while others have difficulties adapting to a new environment and people ( Thomas et al., 1963 ; Putnam and Rothbart, 2006 ).

Children’s temperament has been described as a source of multiple categories of behavioral manifestations. The result is the concept of temperament as a three-component structure, which is represented by Surgency/Extraversion, Negative Affectivity, and Effortful Control ( Rothbart, 1988 ; Rothbart and Bates, 1998 , 2006 ; Rothbart and Putnam, 2002 ). In a more detailed concept, the Surgency/Extraversion category is described as impulsive, exhibiting a high degree of activity and courage and, at the same time, a need for satisfaction.

Negative Affectivity is characterized by manifestations of sadness, frustration, and being difficult to calm down. Effortful Control is characterized by the need for control and ability to concentrate ( Rothbart and Putnam, 2002 ). In relation to school readiness and the subsequent success of children, Negative Affectivity is characterized by the above-mentioned authors as a possible source of problems with controlling emotions and thus as a possible source of problems in children’s behavior.

Executive Functions

Executive functions as a term can be described as a collective name for a complex and diverse set of mental processes, the content and scope of which are differently defined. Most often, higher-order cognitive abilities are described using this term, allowing people to use psychological and physical resources effectively in an unknown or under-structured situation. Executive functioning, cognitive functioning, and affectivity can be considered as three fundamental dimensions of human behavior. Executive functions provide “know-how” on how to handle cognitive and affective processes. There is empirical evidence suggesting a strong relationship between temperamental characteristics and executive functions ( Sudikoff et al., 2015 ). Affrunti and Woodruff-Borden (2015) state that the expression of temperament can be influenced by executive functioning. Temperament also includes behavioral aspects, as well as attention-seeking processes, including maintaining orientation and executive control. These skills form the basis for the development of self-regulation ( Rothbart and Hwang, 2002 ).

Effortful Control

The interaction of effortful control and emotion or stress is characterized by Zelazo et al. (2016) using the expressions “hot” effortful control and “cool” effortful control. These are based on the results of behavioral and neuroimaging research. Both types of effortful control are involved in the problem-solving function and varying degrees of motivation and emotion. For a “hot” approach, important situations involve the predominance of motivation and emotion. The “cool” approach works in affectively neutral contexts ( Zelazo and Carlson, 2012 ).

Self-Regulation

The current theoretical basis emphasizes the importance of self-regulation in relation to school readiness. Self-regulation in a broader sense involves the ability to control emotions ( Blair and Raver, 2015 ). Self-regulation offers an important addition to the conceptualization of school readiness because it addresses children’s ability to attend to information, use it appropriately, and inhibit behavior that interferes with learning. However, like the broader concept of school readiness, theories and perspectives on self-regulation have focused on various priorities ( Pan et al., 2019 ).

The level of reactivity is related to the characteristics of the reactions to changes in stimuli that are reflected on several levels (behavioral, autonomous, and neuroendocrine) and display different periods of observable parameters from latency and an increase and then a peak of intensity until relaxation. Self-control influences these processes and influences reactivity ( Rothbart et al., 2004 ).

School Readiness

School readiness is understood as the state when a child enters school adequately prepared to engage in school activities and benefit from the educational situations so that he/she can experience success regarding his/her potential. Kagan (1990) speaks about readiness for learning, which is a state in which the child, thanks to his/her development, is able to learn the individual subjects. Janus (2007) describe school readiness as a level of maturity of the nervous system which allows the child to process specific “school” stimuli and develop his/her skills and knowledge without mental suffering.

Regarding mental development, school readiness is a child’s state when the child’s skills necessary for meeting his/her cognitive, physical, and social needs on entry to school can be employed ( Mashburn and Pianta, 2006 ; Pianta et al., 2007 ; Janus and Gaskin, 2013 ). The developmental level of the child provides the opportunity to safely reflect the needs of schooling in a wider context in terms of cognitive, social, and emotional functions ( Lemelin et al., 2007 ).

In relation to the above, one can also include maturity and physical health, emotional maturity, and the necessary communication skills ( Kagan, 1992 ; Doherty, 2007 ).

Janus and Offord (2000) named the basic domains that are important in relation to a child’s functioning at school, which can at the same time be used as areas for evaluation or in the event of a need for diagnostics of particular functions. These are physical health and well-being, including the necessary development of fine and coarse motor skills. It is also a domain that includes the social skills of responsibility and respect, approach to education, and readiness to explore new things. Attention also needs to be paid to emotional maturity, which includes pro-social behavior and the ability to function in a group. Being able to deal with anxiety and fear and the ability to manage one’s behavior regarding concentration and activity are associated with emotional maturity. According to these authors, the other domains on the list are the level of language skills and the overall level of cognitive functioning in the areas of literacy, mathematical imagination, and motivation to learn. Communication skills and their adequate development as an essential factor for effective schoolwork can be emphasized.

The research scope of the study is focused on the school readiness of children in relation to their temperament. The given age category of the children and their temperament are considered essential with regard to their readiness for, and subsequent success in, school education, as is stated by other expert studies. Vágnerová (2012) considers preschool age to be a period during which the child should be mentally and physically sufficiently mature to begin school attendance, while Al-Hendawi (2013) argues that temperament is a significant parameter of school adaptation and success. Al-Hendawi (2013) also states that the authors of expert studies view temperament from different perspectives.

The aim of the research was to determine whether there are studies that deal with the relationship between temperament, its dimensions, and school readiness.

For this review study, a design was applied that is based on the PRISMA method ( Moher et al., 2015 ) in the context of the theory of Paré and Kitsiou (2017) . Four stages of the work process were created based on this method.

Stage 1– Strategy

The study, and therefore the search for the primary source texts, focused on the period from 1 January 2000 to 29 February 2020, with the selection including articles in scientific journals in English. The search keywords were represented by the following expressions: School readiness; Temperament; Preschool age; School success; Effortful control; Self control; Mood.

The following elements were used for the search strategy: (school N1 readiness) OR (school N1 success); (school N1 readiness) OR (school N1 success) AND mood; (school N1 readiness) OR (school N1 success) AND Effortful control; (school AND readiness) OR (school AND success); (school AND readiness) OR (school AND success) AND Effortful control; (school AND readiness) OR (school AND success) AND mood; (school N/3 readiness) OR (school N/3 success) AND mood AND preschool.

This time span was chosen because the largest number of texts for further analysis was searched for in the databases during this period. The choice of a shorter time span of the margin did not offer sufficient saturation in searching.

Stage 2 – The Selection of Databases for the Search

The MEDLINE, CINAHL, ERIC, EMBASE, PsycINFO, PsycArticles, Web of Science, Google Scholar, Scopus, and Proquest databases were used for the search. The EBSCO Discovery Service was used. A total of 1092 articles were found.

Abstracts were analyzed for all 1092 articles. On the basis of this analysis, those articles that did not match the specified criteria were gradually eliminated. Figure 1 shows what the procedure for the selection of suitable articles looked like.

Figure 1. Flowchart of Searching.

In the last stage a detailed analysis of 142 articles was performed. In all these articles, the key categories “Temperament”, “Executive functions” “Effortful control”, “Self-regulation”, and “School readiness” were used.

On the basis of the analysis of 142 articles, specific groups based on the topics were created. School readiness was related to different variables with an indirect relationship to temperament – ADHD (25 articles), autism (one article), illness and health problems (19 articles), different age categories (28 articles), a conflict between the parents’ and teachers’ expectations of preschool-age children (five articles), and the topic of preschool children and disability (one article). In addition, there was the theory of mind and executive functions (eight articles), language skills (two articles), and the environment of the family and school (eight articles), parents’ temperament (nine articles), and teacher temperament (nine articles).

The narrow selection included 26 or 27 articles whose topics matched the requirements of the relationship between school readiness and temperament, i.e., both the essential categories – school readiness and temperament – appeared in them simultaneously. Only the 27th article ( Miller and Goldsmith, 2017 ) is rather specific because the authors wanted to create an ideal pupil who would be successful at school.

The articles were analyzed qualitatively using a set of qualitative indicators. The indicators were determined in compliance with the research questions as the basis for the research and a more detailed description of the relationship between the child’s temperament and school readiness. On the basis of these criteria, three qualitative indicators were determined: methods, target group, and research results. These indicators were then divided into the sub-groups shown in Table 1 .

Table 1. Qualitative indicators.

The stated qualitative indicators were determined as the basis for further examination and a more detailed description of the relationship between the child’s temperament and school readiness or success in the selected articles.

Qualitative Indicator – Methods

The focus of the selected studies was divided into three fundamental domains: temperament (A), cognitive abilities (B), and social skills (C) (see Table 2 ). In twelve studies ( Schoen and Nagle, 1994 ; Rudasill and Konold, 2008 ; Rudasill and Rimm-Kaufman, 2009 ; Stacks and Oshio, 2009 ; Zhou et al., 2010 ; Gartstein et al., 2016 ; Collings et al., 2017 ; Miller and Goldsmith, 2017 ; VanSchyndel et al., 2017 ; Bryce et al., 2018 ; Beceren and Özdemir, 2019 ; Johnson et al., 2019 ) the authors directly use the term ‘temperament’, while in 15 ( Bramlett et al., 2000 ; Valiente et al., 2008 , 2010 ; Rimm-Kaufman et al., 2009 ; Iyer et al., 2010 ; Rhoades et al., 2011 ; Silva, 2011 ; Valiente et al., 2011 ; Willoughby et al., 2011 ; Al-Hendawi and Reed, 2012 ; Razza et al., 2012 ; Morris et al., 2013 ; Gaias et al., 2016 ; Sawyer et al., 2019 ; Fung et al., 2020 ) they use the term ‘regulation of emotions’, which they perceive as part of temperament. In all the research focused on school readiness, however, the concept of readiness differed, and it was possible to divide it into two basic categories of social skills ( Bramlett et al., 2000 ; Rimm-Kaufman et al., 2009 ; Rudasill and Rimm-Kaufman, 2009 ; Stacks and Oshio, 2009 ; Valiente et al., 2010 ; Zhou et al., 2010 ; Silva, 2011 ; Valiente et al., 2011 ; Willoughby et al., 2011 ; Al-Hendawi and Reed, 2012 ; Morris et al., 2013 ; Gaias et al., 2016 ; Gartstein et al., 2016 ; VanSchyndel et al., 2017 ; Johnson et al., 2019 ; Beceren and Özdemir, 2019 ) and cognitive skills ( Schoen and Nagle, 1994 ; Rhoades et al., 2011 ; Valiente et al., 2011 ; Razza et al., 2012 ; Morris et al., 2013 ; Collings et al., 2017 ; Miller and Goldsmith, 2017 ; Bryce et al., 2018 ; Johnson et al., 2019 ; Sawyer et al., 2019 ; Rimm-Kaufman et al., 2009 ; Valiente et al., 2010 ; Zhou et al., 2010 ; Willoughby et al., 2011 ; Gaias et al., 2016 ; Gartstein et al., 2016 ; VanSchyndel et al., 2017 ). In the area of cognitive skills, the authors observed reading and mathematical concepts ( Valiente et al., 2010 ; Morris et al., 2013 ; Gaias et al., 2016 ; Johnson et al., 2019 ), language skills ( Schoen and Nagle, 1994 ; Rhoades et al., 2011 ), and in two cases both the skills ( Razza et al., 2012 ; Sawyer et al., 2019 ).

Table 2. Qualitative indicator – target group.

To characterize temperament, different tools were used, in eleven cases the CBQ questionnaire ( Rudasill and Konold, 2008 ; Rudasill and Rimm-Kaufman, 2009 ; Iyer et al., 2010 ; Valiente et al., 2010 ; Zhou et al., 2010 ; Silva, 2011 ; Valiente et al., 2011 ; Morris et al., 2013 ; Gaias et al., 2016 ; Miller and Goldsmith, 2017 ; Bryce et al., 2018 ), which will also be used in our case. In order to assess the level of cognitive and social skills, certified tools were mainly used, in one case ( Johnson et al., 2019 ) a tool that the researchers developed themselves, and in two cases, observation was used ( Rimm-Kaufman et al., 2009 ; Rudasill and Rimm-Kaufman, 2009 ).

The definition of temperament is then adapted for the purpose of the studies. In eight cases, the authors put an emphasis on individual differences in their definitions ( Bramlett et al., 2000 ; Rudasill and Konold, 2008 ; Rudasill and Rimm-Kaufman, 2009 ; Valiente et al., 2010 ; Gartstein et al., 2016 ; Collings et al., 2017 ; Bryce et al., 2018 ; Johnson et al., 2019 ), in eleven cases they emphasized self-control ( Rimm-Kaufman et al., 2009 ; Valiente et al., 2010 , 2011 ; Willoughby et al., 2011 ; Gaias et al., 2016 ; Gartstein et al., 2016 ; Collings et al., 2017 ; Miller and Goldsmith, 2017 ; Bryce et al., 2018 ; Johnson et al., 2019 ; Sawyer et al., 2019 ), and in five cases they stressed the biological basis ( Bramlett et al., 2000 ; Rudasill and Konold, 2008 ; Rudasill and Rimm-Kaufman, 2009 ; Al-Hendawi and Reed, 2012 ; Sawyer et al., 2019 ). Morris et al. (2013) , Beceren and Özdemir (2019) , Johnson et al. (2019) , and Fung et al. (2020) stress the influence of temperament on emotions in their definition and the influence on children’s social skills is emphasized in nine studies ( Schoen and Nagle, 1994 ; Valiente et al., 2008 ; Stacks and Oshio, 2009 ; Iyer et al., 2010 ; Zhou et al., 2010 ; Rhoades et al., 2011 ; Silva, 2011 ; Razza et al., 2012 ; VanSchyndel et al., 2017 ).

Qualitative Indicator – Target Group

The numbers of respondents were representative in relation to the research that was analyzed. In longitudinal studies, there were research studies with large numbers of respondents (more than 1000) ( Razza et al., 2012 ; Johnson et al., 2019 ; Sawyer et al., 2019 ), but also one research study involving 31 respondents ( Gartstein et al., 2016 ). For most other research studies, the number of respondents ranged between 100 and 1000 ( Schoen and Nagle, 1994 ; Bramlett et al., 2000 ; Valiente et al., 2008 , 2010 , 2011 ; Rimm-Kaufman et al., 2009 ; Rudasill and Rimm-Kaufman, 2009 ; Iyer et al., 2010 ; Zhou et al., 2010 ; Rhoades et al., 2011 ; Silva, 2011 ; Willoughby et al., 2011 ; Gaias et al., 2016 ; Collings et al., 2017 ; VanSchyndel et al., 2017 ; Bryce et al., 2018 ; Beceren and Özdemir, 2019 ; Fung et al., 2020 ). The exceptions consisted of some studies ( Stacks and Oshio, 2009 ; Al-Hendawi and Reed, 2012 ; Morris et al., 2013 ) in which there were fewer than 100 respondents and one case with 1364 respondents ( Rudasill and Konold, 2008 ). In one study ( Miller and Goldsmith, 2017 ) the respondents were teachers whose task was to create basic categories which they could use to assess a child’s school readiness.

In four cases ( Bramlett et al., 2000 ; Silva, 2011 ; Miller and Goldsmith, 2017 ; Johnson et al., 2019 ) the authors of the study do not state the results regarding gender. In the studies by Schoen and Nagle (1994) , Stacks and Oshio (2009) , Valiente et al. (2010) , and VanSchyndel et al. (2017) the gender ratio between boys and girls was 40% to 60% and in the remaining studies the ratio was around 50% in all cases.

The age span of the respondents was between 0 and 12 years of age. The age of the respondents was associated with the research aim (see Table 2 and the glossary accompanying the table). The information about the respondents was in all cases (except in one case, Gartstein et al., 2016 ), obtained from the responses of teachers or trained researchers and in 14 cases ( Bramlett et al., 2000 ; Rudasill and Konold, 2008 ; Valiente et al., 2008 , 2010 , 2011 ; Rudasill and Rimm-Kaufman, 2009 ; Zhou et al., 2010 ; Rhoades et al., 2011 ; Silva, 2011 ; Al-Hendawi and Reed, 2012 ; Collings et al., 2017 ; VanSchyndel et al., 2017 ; Beceren and Özdemir, 2019 ; Fung et al., 2020 ) also from parents. In three cases, information was also obtained from children ( Iyer et al., 2010 ; Zhou et al., 2010 ; Valiente et al., 2011 ).

Schoen and Nagle (1994) , Miller and Goldsmith (2017) , and Beceren and Özdemir (2019) do not state ethnicity in their studies. Sawyer et al. (2019) state that the research was carried out on a representative sample of the Australian population, similarly to Bramlett et al. (2000) , who state that 98% of their sample was Caucasian. In the case of these two studies, the aim was not to compare the influence of temperament on school success with regard to ethnicity, but primarily a description of the given relationship in a representative sample of the given population. Silva (2011) cites ethnicity, but not the percentual distribution. Rudasill and Konold (2008) , Zhou et al. (2010) , and Fung et al. (2020) presented mono-ethnic samples; in the first case they were Caucasians, the second study involved children from Hong Kong, and in the third article the respondents were from China. In the other studies the percentages of the ethnic groups are presented.

Schoen and Nagle (1994) , Bramlett et al. (2000) , Rudasill and Konold (2008) , Rudasill and Rimm-Kaufman (2009) , Valiente et al. (2010) , Gartstein et al. (2016) , Beceren and Özdemir (2019) , Sawyer et al. (2019) , and Fung et al. (2020) do not state any specifics in relation to their respondents or state that it was a representative sample. Miller and Goldsmith (2017) aimed their research at creating a profile of the most successful child who enters school prepared to the maximum extent. Rimm-Kaufman et al. (2009) reported that their respondents were exclusively children from villages, while in contrast Gaias et al. (2016) chose children from cities. In other cases, the authors studied children who came from a socially or economically endangered environment. They were specifically children who were born to single mothers ( Razza et al., 2012 ), children who were included in the “Head Start” program ( Stacks and Oshio, 2009 ; Rhoades et al., 2011 ; Silva, 2011 ; Willoughby et al., 2011 ; Bryce et al., 2018 ; Johnson et al., 2019 ), and children who were included in the free lunch program ( Silva, 2011 ; Collings et al., 2017 ). Iyer et al. (2010) , Zhou et al. (2010) , Valiente et al. (2011) , Al-Hendawi and Reed (2012) , and Morris et al. (2013) were interested in children who displayed specific requirements for education as a result of increased risk of adverse circumstances (economic disadvantage, developmental delay, or a combination of both).

Qualitative Indicator – Conclusion

In the case of the study by Bryce et al. (2018) , it was not possible to confirm a hypothetical chain process: child’s positive emotionality → emotional engagement in kindergarten → behavioral expressions in kindergarten → educational results in kindergarten. In other cases, the link between temperament and school readiness or subsequent school success was confirmed.

In some cases ( Rudasill and Konold, 2008 ; Valiente et al., 2008 , 2010 , 2011 ; Rudasill and Rimm-Kaufman, 2009 ; Iyer et al., 2010 ; Zhou et al., 2010 ; Rhoades et al., 2011 ; Silva, 2011 ; Al-Hendawi and Reed, 2012 ; Morris et al., 2013 ; Gaias et al., 2016 ; Gartstein et al., 2016 ; Collings et al., 2017 ; Miller and Goldsmith, 2017 ; VanSchyndel et al., 2017 ; Bryce et al., 2018 ; Beceren and Özdemir, 2019 ; Johnson et al., 2019 ; Fung et al., 2020 ) the authors were further interested in whether temperament can be seen as a risk or protective factor. In most cases, it was found that higher Effortful Control has a positive relationship to greater school readiness – the success rate and lower Effortful Control can predict behavioral problems and thus problems at school ( Valiente et al., 2008 , 2010 , 2011 ; Iyer et al., 2010 ; Zhou et al., 2010 ; Morris et al., 2013 ; Gartstein et al., 2016 ; VanSchyndel et al., 2017 ). Rudasill and Rimm-Kaufman (2009) , Silva (2011) , and Gaias et al. (2016) add that the value of Effortful Control can influence the teacher’s relationship with the child and thus the child’s school readiness and also later school success. Al-Hendawi and Reed (2012) found that negative emotionality has a significant effect on adaptivity and schoolwork and can become a predictor of inappropriate behavior. In contrast, Johnson et al. (2019) did not confirm that problems in the area of a child’s temperament can be perceived as a significant predictor of prosocial behavior. There is a statistically significant relationship between temperament and school readiness. Both positive and negative emotionality influence behavior (especially concentration), which is reflected in the approach to learning and school success.

Collings et al. (2017) suggest that there was a positive effect of a previous intervention on temperament, confirmed in the individual items of school performance. Their results for the boys who participated in the intervention program were better in the areas of literacy and mathematics than was the case in boys who did not participate. Bryce et al. (2018) state that positive emotionality significantly influenced behavior in children in kindergarten. Rudasill and Konold (2008) , Rhoades et al. (2011) , Beceren and Özdemir (2019) , and Fung et al. (2020) characterized the child’s maturity in the context of how he/she is able to control his/her temperament so that it can function as a supportive factor in education. Similar conclusions were also reached by Miller and Goldsmith (2017) . In their view, children who were able to regulate their emotions were able to react better in socially appropriate ways and focus their attention, which facilitates learning and provides higher chances of success in school education.

In addition, difficult temperament at an early age can lead to low parental involvement at age three. The role of difficult temperament, poor maternal involvement, and externalizing behavior may be partially responsible for the continuity that has been observed in antisocial behavior over time ( Walters, 2014 ).

The last thing that the authors state is the more detailed characteristics of the relationship identified between temperament and school readiness or school success. Bramlett et al. (2000) admit that there might be differences between what can be termed the home and school temperaments, which can explain the differences between the parents’ and children’s answers. Beceren and Özdemir (2019) stress the importance to social-emotional adjustment of family involvement. Schoen and Nagle (1994) , Al-Hendawi and Reed (2012) , Collings et al. (2017) , and Miller and Goldsmith (2017) state that here there are differences between the temperaments of boys and girls; the last two argue that boys show higher activity. Razza et al. (2012) , Collings et al. (2017) , and also Gartstein et al. (2016) suggest that there is a positive effect of intervention programs on school readiness. These are programs that focus on exerting control over one’s temperament during preschool age. Similarly, Sawyer et al. (2019) state that if there is an increase in the ability to exert self-control at the ages of 2-3 and 6-7, this can have a positive influence on school readiness. The ability to self-regulate is considered an essential factor in school readiness by Rudasill and Konold (2008) , Valiente et al. (2010) , Willoughby et al. (2011) , and VanSchyndel et al. (2017) , and Valiente et al. (2008) , Zhou et al. (2010) , Valiente et al. (2011) , Morris et al. (2013) , and Fung et al. (2020) attribute great importance to effortful control for school readiness. Another important factor that can affect a child’s school readiness is his/her relationships with peers ( Iyer et al., 2010 ) and teachers ( Rudasill and Rimm-Kaufman, 2009 ; Silva, 2011 ; Gaias et al., 2016 ). Rimm-Kaufman et al. (2009) state that the quality of the preschool classroom affects the child’s behavior, and this can then affect school readiness. Miller and Goldsmith (2017) argue that the model of “an ideal child” was created separately for boys and girls who will be successful at school.

The analysis of literary sources showed that in the period under consideration, there are expert studies dealing with the relationship between temperament and school readiness. In total 27 articles were included in the narrowest selection, in which the authors sought and examined this relationship or perceived it as the default setting for further examination.

From selected studies it is clear that when working with the phenomenon of temperament, as a factor that can influence other phenomena from the point of view of psychology, there is a big problem with the definition of temperament. In the introduction to the rewiev study, the individual definitions and views of their authors on temperament are given. The following are terms that are used by other authors instead of temperament. Table 3 lists the concepts of temperament as presented by the authors of selected 27 studies. In the analyzed studies, the authors used either the term temperament or the concept of regulation of emotions directly. Temperament or regulation of emotions were then characterized from different points of view using terms: self-control, individual differences, biological basis and social skills. These concepts of temperament in selected articles confirm the high degree of difference of approaches to the concept of temperament.

Table 3. Qualitative indicator – methods.

Out of 27 relevant studies, 26 confirmed a statistically significant relationship between temperament and school readiness; see Table 4 . In one case ( Bryce et al., 2018 ), the authors did not confirm the relationship between temperament and school readiness, but at the same time they stated that the results support the hypothesis about the indirect influence of positive emotional adjustment in the child on his/her behavior and afterwards on his/her school results. The results of the selected studies indicate that there are differences between boys and girls in the area of temperament, which is then reflected in the level of school readiness; see Table 2 . We should therefore consider this fact in the child-raising/educational process. Another thing that needs to be taken into account in the educational process is the relationship between children’s temperament and the temperament of teachers. This relationship can have an impact on school readiness and success at school. Apart from the confirmation of the relationship between temperament and school readiness, the authors of the studies also dealt with the description of this relationship. The authors agree that the inability to manage one’s emotions has a significant influence on one’s behavior, such as the ability to concentrate or intentional attention, and afterwards one’s readiness for school. If an individual is able to manage his/her emotions, he/she is able to react in a socially appropriate manner and is able to focus, and this can facilitate his/her learning, which is a prerequisite for school success.

Table 4. Qualitative indicator – conclusions.

In 14 out of the 27 cases, there were respondents from a socio-economically disadvantaged environment; see Table 2 . The authors do not confirm the direct influence of a socio-economic disadvantage on school readiness or success but characterize the temperament of these children in relation to searching for appropriate upbringing and educational procedures. They also show the success of these procedures, which does not comply, however, with the theories of temperament, which are based on the fact that temperament is inborn and relatively stable (e.g., Orth and Martin, 1994 ).

In searching for specialized texts focused on the relationship of temperament and school readiness, we repeatedly encountered the concept of the relationship of temperament to cognitive functions. Specifically, temperament is part of effortful control directly related to executive attention ( Rothbart et al., 2007 ). Frick et al. (2018) described the relationship between temperament and cognitive function in their research. Their work focuses on cognitive self-regulation as a set of constructive behaviors that influence cognitive abilities to integrate learning processes. These processes are planned and customized to support the tracking of personal goals in a changing environment. This function already develops when the child is at an early age. When the child is of school age, temperament is associated with cognitive abilities. With regard to the part of the study by Chong et al. (2019) in which they focused on preschool age, the authors report that temperament was less related to cognitive and academic outcomes after parenting and family confusion had been taken into account.

Temperament is considered a predictor of functional attention influenced by individual differences in reactivity and self-regulation in emotion and activity ( Rothbart et al., 2006 ; similarly, Guarnera et al., 2019 ). Outside the topic of research, but as a critical problem area, there appears the relationship of temperament (especially its projection into the attention) and learning difficulties and the connection with the possibility of special intervention ( Commodari, 2012 ). Gan et al. (2016) draw attention to the possible influence of the environment (rural – city) on temperament and subsequently on children’s school readiness. The quality of the teacher-child relationship or direct teacher intervention can have a positive influence on the relation between emotional regulation and cognitive skills ( Commodari, 2013 ; Guarnera et al., 2017 ). The relationship of individual components of temperament and cognitive function in school-age children – especially reading, writing, and mathematics – is evidenced in their study ( Guarnera et al., 2017 ).

Discussion and Possible Application in Practice

By analyzing the selected articles, the basis for creating answers to the key questions was obtained.

1. There is a significant relation between temperament and its major dimensions and school readiness.

2. Temperament and its dimensions can affect school success in both directions, positively and negatively.

Children whose Effortful Control is the dominant feature can be assumed to possess the ability to exert control and self-regulate in the field of behavior ( Olson et al., 2005 ).

If the level of Surgency/Extraversion is higher in the context of the child’s behavior, it can be considered a risk factor that affects hyperactivity. To a lesser extent, it can be an inhibitor of the “research approach” but irrespective of the school’s instructions and rules. The presence of the above options can be a source of problems in children’s behavior and thus have a negative effect on school readiness outcomes ( Fox et al., 2001 ). However, manifestations of children’s behavior, as an important element of school readiness, are always the result of the relationship between temperament and its interaction with the environment. For more on this see Rothbart and Putnam (2002) .

School attendance and the child’s subsequent success in education can be influenced by more factors. The major factors that experts ( Janus, 2007 ; Merrell and Tymms, 2007 ; Vágnerová, 2012 ) list include cognitive functions, motivation, experience, and the child’s temperament. Temperament can influence a child’s functioning during school performance and therefore to some extent either enhance or limit the child’s performance. In mathematics, reading, or other school activities which require the child to calm down, concentrate on the task, and resist stimuli from the surroundings, temperament can be a very important factor ( Collings et al., 2017 ; Ato et al., 2020 ). Therefore, it can have a negative influence on the performance of a child who is functioning cognitively quite well, but is unable to concentrate, calm down, and detach him- or herself from disturbing stimuli from outside. On the other hand, it can enhance a child’s performance, which might be weaker from the school evaluation perspective. They can, to an adequate extent, reduce their physical activity, calm down, concentrate, and carry out a task to its end.

Al-Hendawi and Reed (2012) argue that the negative emotionality associated with a low level of ability to control expressions of temperament can be a source of problems in social situations in class. In their study, these authors point out the possibility of the overstimulation of children with stimuli from the outside, with a negative effect on their engagement in schoolwork and the quality of their results. Dependency in the teacher-child relationship has a strong correlation with school adjustment difficulties, including poorer academic performance, more negative attitudes to school, and less positive engagement with the school environment ( Birch and Ladd, 1997 ).

In terms of temperament and its introduction into the school environment, there is one potentially conflicting area ( Keogh and Prokopcová, 2007 ). These are situations where the child’s temperament and the temperament of the teacher do not meet in a mutually satisfactory constellation, but are mismatched with each other, creating clashes and having a negative effect on their mutual functioning.

The quality of first-grade classroom environments is based on three domains: emotional support, classroom organization, and instructional support. A high-quality classroom environment may ameliorate the academic and social risks associated with having a difficult temperament ( Curby et al., 2011 ).

Some teachers are active and react quickly, while some are slower and react upon consideration. These differences are reflected in the activities which take place in the classroom, especially in the pace of teaching and in the form of personal interactions and emotional charge. If there is a child in the group with a significantly different temperament to that of the teacher, this difference may be a source of misunderstandings and consequently of failure and demotivation in the child. The child will experience more stressful situations when entering school. Apart from the encounter with the teacher’s temperament, there is also the encounter with the temperaments of the child’s classmates. If it is important to deal with temperament and success at school, it is not on the basis of a construct, but on the actual situation in each classroom and the need to work effectively with these factors. In conclusion, it should be noted regarding the school or class environment that they appear explicitly in only two articles as one of the parameters linked to the temperament of children. In the first case, Al-Hendawi and Reed (2012) are inclined to the concept of the school environment in terms of the creation and functioning of social relations. They work with relationships between children and children and the teacher. In the second article, Bramlett et al. (2000) used the term ‘school environment’ for the social environment and focused on the area of problematic behavior, which is related to the reduced ability of the child to control his or her temperament.

The preschool period of the child is a very important period in which the basics of socio-emotional competence are laid. Their influence on future success in education and in the development of socialization is indisputable. Teachers can use specific programs – such as Head Start or their own active approach – to help children successfully develop self-regulatory behavioral control skills and thus help prepare them for school success ( McBryde et al., 2004 ; McClelland and Wanless, 2012 ; Brophy-Herb et al., 2018 ; Booth et al., 2019 ). In conclusion, the authors cited above agree on temperament as an innate individual reactivity to stimuli that can affect the school success rate of children.

The analysis of the articles also showed that even if the temperament is innate, it can be affected by appropriate interventions, so that it can be used in a positive direction in school success. Methodologically, this study will be used to process a similar study that will focus on the areas of children with visual handicaps.

The focus on texts written in English can thus be a weakness. It is possible that this topic might be covered in other languages, but the results of such studies are not presented here. The authors are aware of possible terminological differences that can occur in the texts, as was the case, for example, with the term ‘temperament’, for which some authors used the term ‘mood’.

Author Contributions

PP and MP contributed to the design and implementation of the review, to the analysis of the results, and to the writing of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by grant: Palacký University Olomouc: IGA CMTF_2019_006.

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.

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Keywords : school readiness, temperament, self-control, preschool age, school success, effortful control

Citation: Potmesilova P and Potmesil M (2021) Temperament and School Readiness – A Literature Review. Front. Psychol. 12:599411. doi: 10.3389/fpsyg.2021.599411

Received: 27 August 2020; Accepted: 22 April 2021; Published: 20 May 2021.

Reviewed by:

Copyright © 2021 Potmesilova and Potmesil. 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: Milon Potmesil, [email protected]

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Modeling human temperament and character on the basis of combined theoretical approaches

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Although there are several models on the structure of human temperament, character and personality, the majority follow a single approach, providing a unilateral and overly theoretical construct which is unsuitable for clinical application. The current study aimed to develop a complex and comprehensive model of temperament and character by empirically combining relevant existing theories.

The study included 734 healthy general population subjects aged 40.80 ± 11.48 years, who completed the TEMPS-A, TCI and NEO-PI-3 questionnaires. Data were analyzed in a multistep approach using Exploratory Factor analysis and forward stepwise linear regression.

The results yielded two highest order factors (Self and Self–Environment Interaction), six middle order factors (Emotional Self, Cognitive Self, Social Emotionality, Emotional and Cognitive Control, Ethical Emotionality and Behavior, Social Emotionality and Behavior) and 12 factors at the bottom (Ego Resiliency, Ego Strength, Intrapersonal Emotion, Personal Space Cognition, Interpersonal Cognition, Emotional Creativity, Externalized Interpersonal Emotion, Internalized Interpersonal Emotion, Emotional Motivation, Self-Discipline, Ethical Values and Ethical Behavior).

Conclusions

The current study developed a complex hierarchical model of temperament and character on the basis of empirical data from several temperament theories. An important feature of the new temperamental model is the frequent admixture of emotional and cognitive processes within the same module. This model expands the field to include elements probably corresponding to meta-cognition mechanisms and complex interactions between affective and cognitive control, which may provide useful in understanding and treating affective disorders as well.

Introduction

The concept of temperament is one of the most ancient in the history of the study of human behavior. It originally referred to those aspects of personality that are innate, rather than learned, whereas character was considered to be what we made of ourselves intentionally. Later it was considered that temperament is the emotional core of personality that is moderately stable throughout life, whereas character reflects a person’s goals and values as they develop over the lifespan [ 1 ]. The oldest theoretical approach included an early biological model which suggested that bodily humors were responsible for human mental health, disease and behavior. It seems that early versions of this theory might had existed in ancient Egypt or Mesopotamia, but the theory was developed in full by the school of Cos and specifically by Polybos, a pupil and son-in-law to Hippocrates (fourth century B.C.)

Temperament and personality theories influenced philosophical thinking and played a predominant role in the shaping of the anthropological and humanitarian sciences. Prominent scholars like Ernst Platner (1744–1818), Immanuel Kant (1724–1804), Friedrich Schiller (1759–1805), Friedrich Wilhelm Nietzsche (1844–1900), Rudolf Steiner (1861–1925), Ernst Kretschmer (1888–1964), and Erich Fromm (1900–1980), Emil Kraepelin (1909–1915), Alfred Adler (1879–1937), Eduard Spränger (1882–1963), William James (1842–1910), and Ernst Kretschmer (1888–1964) all developed theories of human internal psychological functioning and behavior but they were not based on strict evidence. Hans Eysenck [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], Jeffrey Gray [ 12 ], Jerome Kagan [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] Robert Cloninger [ 22 , 23 , 24 ] and Hagop Akiskal [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ] all developed empirical theories of temperament and character traits and dimensions. Hans Eysenck (1916–1997) was the first to analyze personality differences using an empirical/statistical method. He proposed that the basic factors were Neuroticism (tendency to experience negative emotions), Extraversion (tendency to enjoy positive events) and Psychotisism (cognitive style). Eysenck’s theory and all the theories that derived from it, concern approach/reward, inhibition/punishment, and aggression/flight [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Currently the theories of temperament and character are represented by three major questionnaires, the NEO-PI-3, the TCI and the TEMPS-A. However these are not the only ones which exist [ 32 ].

The NEO Personality Inventory (NEO-PI) was developed by Costa and McCrae. Its original version which was published in 1978, was the Neuroticism-Extroversion-Openness Inventory (NEO-I). That version was measuring only three of the Big Five personality traits [ 33 ]. It was revised in 1985 to include all five traits and subsequently was renamed the NEO Personality Inventory (NEO-PI) and it was further refined to NEO-PI-R [ 34 ]. The latest version is the NEO-PI-3, which was published in 2005 [ 35 ]. The NEO-PI-3 includes 240-items corresponding to the Big Five personality traits (Extraversion (E), Agreeableness (A), Conscientiousness (C), Neuroticism (N), and Openness to Experience (O)) and subordinate dimensions (facets). It is suitable for use with adolescents and adults (12 years or older). All items are answered on a five-point scale, ranging from “strongly disagree” to “strongly agree”.

Robert Cloninger’s psychobiological model of temperament and character is a dimensional approach to personality assessment. It led to the development of the Temperament and Character Inventory (TCI) which measures both normal and abnormal personality traits in the two major components of personality, temperament and character [ 22 , 23 , 24 , 36 ]. It proposes the existence of four dimensions of temperament and three dimensions of character. Each reflects normally distributed quantitative traits, present in varying degrees in everyone. Temperament dimensions (novelty seeking (NS), harm avoidance (HA), reward dependence (RD), and persistence (PS), stand for styles that are moderately stable throughout life and concern automatic basic emotional responses such as anger, fear, and disgust. Character dimensions (self-directedness (SD), cooperativeness (CO), and self-transcendence (ST)), stand for individual differences in goals, values, and self-conscious emotions like shame, guilt, and empathy, and mature in a stepwise fashion.

Hagop Akiskal’s theory focused on the affective components of temperament and their relationship to mood disorders and creativity [ 25 , 26 , 27 , 28 ]. This theory resulted in an operationalized definition of the five affective temperaments (depressive, hyperthymic, irritable, cyclothymic and anxious) proposed as the proximal behavioral phenotypes in the pre-morbid course of affective disorders [ 29 , 30 , 31 ]. The original criteria of the five temperaments derived from theoretical considerations and clinical observation [ 31 ]. Later the Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS) was developed as a semi-structured TEMPS-I, administered in interview format [ 37 , 38 ] and as a self-rating auto-questionnaire, the TEMPS-A [ 39 ] with 109 (for men) or 110 (for women) items.

The TEMPS is different from the Temperament and Character Inventory (TCI) [ 40 ] and the Five Factor Model (NEO-PI-R) [ 41 ] in that it frames questions in the language of affectivity, it is rooted in an evolutionary biologic perspective [ 26 ] and its clinical validity has been recently supported on a genetic basis [ 42 ].

The aim of the current study was to investigate the hierarchical latent structure of temperament and character as they are assessed with the combined use of the TEMPS-A, the NEO-PI-3 and the TCI and to test whether the hierarchical model which will derive is similar or different to previously described such models.

Materials and methods

Volunteers gathered from around the country gathered data from their region in the frame of standardizing the three instruments. The study included subjects from the general population who satisfied the following inclusion criteria:

Age 18–70 years.

Lack of any physical disorder (according to self-report).

Lack of any psychotic disorder (according to self-report and clinical impression of the examiner, after using a short interview).

The study sample included 734 subjects from the general Greek population (436 females; 59.4% and 298 males; 40.6%). Mean age was 40.80 ± 11.48 years (range 25–67 years). The mean age for females was 39.43 ± 10.87 years (range 25–65 years) while the mean age for males was 42.82 ± 12.06 years (range 25–67 years).

All subjects provided written informed consent and the protocol was approved by the Ethics committee of the Faculty of Medicine, Aristotle University of Thessaloniki, Greece.

The protocol included the gathering of sociodemographic data and the application of the TEMPS-A [ 25 , 43 , 44 , 45 ], the TCI [ 36 , 46 , 47 , 48 , 49 ] and the NEO-PI-3 R [ 34 , 35 ]. On the basis of this dataset all of them were officially validated in the Greek language [ 50 , 51 , 52 ] and their psychometric properties can be found in the related publications. It is also important to note that the collection of the data has been completed by 2008, before the current economic crisis began, and originally was used for the validation of these instruments.

Statistical analysis

The statistical analysis included the following:

The creation of descriptive statistics tables concerning age, gender and occupational status distribution in the sample.

Exploratory Factor Analysis (EFA; with varimax normalized rotation) for the identification of latent structures of variables. The sample size is adequate since the maximum number of variables used in this analysis was 63 (sample size was > 10 times the number of variables). The eigenvalue > 1 was used as the criterion to select factors. In order to attribute a variable to a specific factor the loading was taken into consideration. In case there was a single loading above 0.5 this was considered to be the sole significant. For variables with lower loadings the pattern of loading was taken into consideration. This was done in two separate EFAs:

The first EFA analysis included all TEMPS temperament subscales and all NEO-PI-3 domains (N, E, O, A and C) and TCI temperament and character traits (high order traits; HA, NS, RD, PS, SD, CO and ST).

The second EFA analysis included all TEMPS temperament subscales and all NEO-PI-3 facet scales (N1-6, E1-6, O1-6, A1-6 and C1-6) and TCI temperament and character facets (lower order traits; HA1-4, NS1-4, RD1-4, PS, SD1-5, CO1-5 and ST1-3).

Both EFA analyses led to the recognition of first-order factors. Following this, the factor scores were calculated for each subject by multiplying factor loadings with values of each variable and summing the results. The factor scores underwent EFA again to investigate for the presence of second-order factors. The same procedure was performed for a third time to identify third-order factors. The above analysis was performed twice, separately one time for each EFA analysis mentioned above.

Forward Stepwise Linear Regression Analysis (FSLRA) to test for the ability to calculate the score of one subscale on the basis of the scores of subscales from the other instruments. The components used in this analysis were all TEMPS temperament subscales and all NEO-PI-3 domains (N, E, O, A and C) and TCI temperament and character traits (high order traits; HA, NS, RD, PS, SD, CO and ST).

Pearson Correlation coefficients were also calculated to investigate the relationship among subscales. The same components as in FSLRA were used to calculate correlations.

Data contained in this study are available from the authors on request.

Demographic results and description of the study sample

Age and gender distribution in the study sample, compared to the general Greek population according to the 2009 census is shown in Additional file 1 : Table S1. The distribution of occupation in the study sample (data were available for 533 subjects) is shown in Additional file 1 : Table S2. The results indicate that the study sample is representative of the country’s active population with some over-representation of younger ages in terms of the country’s population of the year 2008, when the data were collected.

Exploratory factor analysis results

The first EFA analysis returned four first-order factors and explained 68% of observed variance. The first factor included TEMPS depressive, cyclothymic, irritable and anxious temperaments, NEO-PI-3 N, and TCI HA and SD (the last with an opposite sign to the rest). The second factor included NEO-PI-3 E and O and TCI NS and RD. The third included TEMPS-hyperthymic temperament, NEO-PI-3 C and TCI ST. The fourth included TEMPS irritability, and with an opposite sign NEO-PI-3 A and TCI CO (Additional file 1 : Table S3). The EFA with factor scores revealed the presence of two second-order factors; the first includes factor 1 and 3 and the second includes 2 and 4. It explained 50% of observed variance (Additional file 1 : Table S4).

The second EFA analysis returned twelve factors and explained 63% of total variance, which is similar to the first EFA. The results are shown in Additional file 1 : Table S5. The first factor included all TEMPS temperaments, NEO-PI-3 N1-4, N6, E6, and TCI HA1-4, SD1-5. The second included TEMPS-hyperthymic temperament, NEO-PI-3 C1-5, E3-4 and TCI Perseveration. The third included NEO-PI-3 A4, N5, C6, and TCI NS2-4, and SD5, the fourth included NEO-PI-3 E1, A1-6, C3, and TCI CO4, the fifth TEMPS-hyperthymic temperament, NEO-PI-3 O6 and TCI ST1-3, the sixth included NEO-PI-3 O1-3, O5-6 and E5, the seventh included TCI RD3, SD5 and CO1-3, the eighth included NEO-PI-3 E2, E6, RD2, the ninth included NEO-PI-3 O4 and TCI NS1, RD1, the tenth included TCI RD3, SD2, the eleventh was a residual factor (with highest loadings for NEO-PI-3-A1, Ε3 and Ν4) with very low loadings while the twelfth included TCI C5. The EFA with factor scores revealed the presence of five second-order factors and explained 40% of variance (Additional file 1 : Table S6). An additional EFA on factor scores returned two third-order factors and explained 40% of observed variance (Additional file 1 : Table S7).

Results of the EFA analyses and the hierarchical model which emerges are shown in Figs.  1 and 2 .

Two-dimensional visual representation of the hierarchical model of temperament

Three-dimensional visual representation of the hierarchical model of temperament

The correlation matrix is shown in Additional file 1 : Table S8.

Forward stepwise linear regression analysis results

The FSLRA results are shown in Additional file 1 : Table S9. Highest explained percentage of variability (R 2 ) was 52% while the lowest was 7%. Individual TEMPS subscales were predicted by NEO-PI-3 and TCI subscales, with explained variability 33–44% and 37–49%, respectively. The NEO-PI-3 subscales were predicted by TEMPS and TCI subscales with explained variability 4–52% and 22–54%, respectively. The TCI subscales were predicted by TEMPS and NEO-PI-3 subscales with explained variability 10–54% and 7–51%, respectively.

The results of the current study suggest that all TEMPS temperament subscales and all NEO-PI-3 domains (N, E, O, A and C) and TCI temperament and character traits (high order traits; HA, NS, RD, PS, SD, CO and ST) can be grouped into four factors explaining 68% of observed variance (first EFA analysis). A second EFA analysis showed that the TEMPS temperament subscales and all NEO-PI-3 facet scales (N1-6, E1-6, O1-6, A1-6 and C1-6) and TCI temperament and character facets (lower order traits; HA1-4, NS1-4, RD1-4, PS, SD1-5, CO1-5 and ST1-3) are grouped in twelve factors explaining 63% of total variance. These latter factors can be further grouped into five second-order factors explaining 40% of variance. Therefore, as shown in Figs.  1 and 2 , the above analyses can provide data to support the hierarchical positioning of all the above subscales into two levels. The four factors of the first EFA analysis largely identify with the five second-order factors of the second EFA analysis. The next step of the analysis returned two second-order factors for the first EFA analysis which explained 50% of observed variance. Similarly, concerning the factor scores of the second EFA analysis, further factor analysis returned also two third-order factors and explained 40% of observed variance. The second-order factors of the first EFA analysis were identical with the third-order factors of the second EFA, so it was easy to place them in the hierarchical diagram in Figs.  1 and 2 .

Notably, the low ability to predict the scores of one questionnaire on the basis of the scores of the others suggests that the three questionnaires assess different and complementary aspects of temperament. This is not in contrast with the finding that there seems to be little difference among different personality questionnaires concerning their predictive validity of real-life psychological constructs [ 32 ] since these real-life constructs are too broad and of unknown reliability.

Interpretation of the findings and development of the model

The development of the model was based on the second EFA since it is more detailed and also essentially includes conclusions from the first EFA.

A. The 12 basic temperament modules

A1 ego resiliency (er).

Starting from the bottom, the first factor includes all TEMPS temperaments; the NEO-PI-3 facets which correspond to anxiety, angry hostility, cognitive aspects of depression (guilt, hopelessness and loneliness), self-consciousness (shame and embarrassment, sensitive to ridicule and prone to feelings of inferiority), vulnerability to stress and the presence of positive emotions (being cheerful and optimistic); the TCI facets corresponding to worry and pessimism with difficulty getting over humiliating and embarrassing experiences, rumination, fear of uncertainty (rarely takes risks, difficulty adapting to changes in routine), shyness with strangers, fatigability vs vigor, responsibility vs blaming, purposefulness vs lack of goal direction (can delay gratification to achieve goals vs reactiveness and empty lives), resourcefulness vs inertia (being productive, proactive, competent and innovative), self-acceptance vs self-striving (being able to accept both strengths and limitations, good self-esteem vs unrealistic fantasies) and congruent second nature vs bad habits (organized life vs. self-defeating traits and weak will).

It could be interpreted as corresponding to ‘Ego Resiliency’ that is to the ability to cope with stress and to stay healthy and functional under pressure and under demanding conditions.

A2 Ego Strength (ES)

The second factor includes the Hyperthymic temperament of the TEMPS; the NEO-PI-3 facets which correspond to competence (being capable, sensible, prudent, effective), order (being neat, tidy, well organized), dutifulness (being conscientious with ethical and moral principles), achievement striving (with high aspiration levels, hard-working and possibly workaholic), self-discipline (begin tasks and completes them in spite of drawbacks), assertiveness (being dominant, forceful and socially ascendant, leadership traits), activity (with high energy, fast-paced lives); TCI facet corresponding to being industrious, hard-working, persistent and stable in spite frustration and fatigue.

This factor could be interpreted as corresponding to ‘ego strength’ since it corresponds to the ability to carry activities with vitality, competence, discipline and endurance.

A3 Intrapersonal Emotion (IE)

The seventh factor includes the TCI facets corresponding to attachment (intimacy vs privacy, expression of experiences and feelings, warm and lasting social attachment, sensitivity to rejection and slights), congruent second nature vs bad habits (organized life vs. self-defeating traits and weak will), social acceptance vs social intolerance, empathy vs social disinterest (being able to get ‘in other peoples’ shoes’, conscious understanding of others and respect) and helpfulness vs unhelpfulness (helpful supportive encouraging reassuring vs self-centered egoistic, selfish).

It could be interpreted as corresponding to the person’s emotional attitude towards others, it reflects an intrapersonal, deeper and core functioning, attitude and needs, and a structure which can be called ‘Intrapersonal Emotion’ (IntraE).

A4 Personal Space Cognition (PSC)

The fifth factor includes TEMPS-Hyperthymic temperament, the NEO-PI-3 facet corresponding to readiness to reexamine social political and religious values and TCI facets corresponding to self-forgetfulness vs self-consciousness (that is the ability to absorb within self, isolate from the surroundings and be creative vs always practical, conventional, unimaginative), transpersonal identification (the position of self to the environment and the universe) and spiritual acceptance vs. rational materialism (that is supernatural beliefs vs. objective empiricism). The presence of Hyperthymic temperament in this factor probably reflects activity level rather than emotion.

Overall this factor could be interpreted as corresponding to a cognitive attitude of the person towards the environment, memories and circumstances. Although it has to do also with other people, this is not the central element of this module. The central element seems to be the world as a whole, in the way the person perceives its own personal space. Thus a label ‘Personal Space Cognition’ (PSC) would be appropriate for this module.

A5 Interpersonal Cognition (IC)

The 11th factor includes NEO-PI-3 facets corresponding to trust (that is others are honest and well-intentioned vs. cynical and skeptical), assertiveness (dominant, forceful and socially ascendant, leadership tendency) and self-consciousness (shame and embarrassment, sensitive to ridicule and prone to feelings of inferiority.

It could be interpreted as corresponding to the cognitive attitude towards others and labeled as ‘Interpersonal Cognition’ (IC).

A6 Emotional Creativity (EC)

The sixth factor includes NEO-PI-3 facets corresponding to fantasy (creativity through daydreaming), esthetics (general interest in fine arts), feelings (emotionality, intense experience of feelings, craving for excitement), openness to new and unconventional ideas and values (readiness to reexamine social political and religious values).

It could be interpreted as corresponding to a predominantly emotional component supporting creativity and thus it could be labeled as ‘Emotional Creativity’.

A7 Externalized Interpersonal Emotion (EIE)

The eighth factor includes the NEO-PI-3 facets corresponding gregariousness (enjoys the company of others vs loneliness), positive emotions (being cheerful and optimistic) and the TCI facet corresponding to warmth vs cold aloofness.

Altogether this structure could be interpreted as corresponding to emotional social life with others and towards socializing and interpersonal relationships. A proper label could be ‘Interpersonal Emotion’ (InterE).

A8 Internalized Interpersonal Emotion (IIE)

The tenth factor includes the TCI facets attachment: intimacy vs. privacy (expression of experiences and feelings, warm and lasting social attachment, sensitivity to rejection and slights) and purposefulness vs. lack of goal direction (delay gratification to achieve goals vs. reactiveness and empty lives).

It could be interpreted as corresponding to ‘emotional social life with others-attachment’ and labeled as Internalized Interpersonal Emotion (IIE).

A9 Emotional Motivation (EM)

The ninth factor includes the NEO-PI-3 facet action (willingness to try new activities) and the TCI facets exploratory excitability vs. stoic rigidity (Sensation seeking) and sentimentality (easily moved by emotions, high emotional expression).

This factor could be interpreted as corresponding to an emotional motivation towards activity and could be labeled as ‘Emotional Motivation’ (EM).

A10 Self-Discipline (SD)

The third factor includes the NEO-PI-3 facets compliance (defers to others, tend to forgive and forget, inhibits anger), impulsiveness (control of craving and urges) and deliberation (tends to think carefully before acting) and the TCI facets impulsiveness vs reflection, extravagance vs reserve (gallant, flamboyant, unrestrained), disorderliness vs regimentation (easily express anger; prefer activities without strict rules) and congruent second nature vs bad habits (strong will, focused, reliable, goal-directed not self-defeating).

This factor could be interpreted as corresponding to impulse control and self-discipline and labeled as ‘Self-Discipline’ (SD).

A11 Ethical Values (EV)

The 12th factor could be interpreted as corresponding to ‘Ethical values’ and includes the TCI facet Integrated conscience vs self-serving advantage (being honest, sincere, with ethical principles).

Ethical Behavior (EB)

A12 The fourth factor includes the NEO-PI-3 facets warmth (interpersonal intimacy, being affectionate and friendly), trust (believing that others are honest and well-intentioned vs being cynical and skeptical), straightforwardness (being frank, sincere and ingenuous vs manipulative, flattering, deceptive), altruism (concern about the welfare of others, generosity, assists others), compliance (defers to others, tends to forgive and forget, inhibits anger), modesty (being humble and modest without low self-esteem), tender Mindedness (sympathy and concern for others vs being hardheaded and unmoved by appeals to pity), dutifulness (having sense of duty and moral obligations), and the TCI facet of compassion vs revengefulness (getting over insults and unfair treatment to be constructive in relationships).

This could be interpreted as corresponding to ‘Ethical Behavior’ (EB).

B. The six Higher Level Modules

B1 emotional self (emos).

Ego Resiliency (ER), Ego Strength (ES) and Intrapersonal Emotion (IntraE) modules group together and correspond to an emotional component of self, that is the emotional processes concerning the inner experience and the needs of oneself in order to be able to stay psychologically balanced, to cope with demands and be competent, active and productive but also to keep tuned with others in an subconscious and spontaneous, unforced and casual way. Thus it could be labeled as reflecting the emotional component of self.

B2 Cognitive Self (CogS)

The Personal Space Cognition (PSC) and the Interpersonal Cognition (IC) modules group together and correspond to a cognitive component of self that is the cognitive processes involved in the understanding of the environment and others.

B3 Social Emotionality (SE)

The Emotional Creativity (EC), the Externalized Interpersonal Emotion (EIE) and the Internalized Interpersonal Emotion (IIE) group together and correspond to mechanisms of emotional processes towards social environment and social life, that is the emotional mechanisms involved in the understanding of the social life and social environment and include among others attachment/detachment, esthetics, ideas and values, environment and others. Thus it could be labeled as ‘Social Emotionality’ (SE).

B4 Emotional and Cognitive Control (ECC)

Emotional Motivation (EM) and part of Self-Discipline (SD) group together and correspond to the emotional together with cognitive mechanisms involved in the filtering and manifestation of behavior that determine the interaction between the individual the environment and others.

B5 Ethical Emotionality and Behavior (EEB)

Ethical Behavior (EB), Ethical Values (EV) and parts of Self-Discipline (SD) and Internalized Interpersonal Emotion (IIE) group together and correspond to a complex control on activity, that is the emotional together with cognitive mechanisms involved in the filtering and manifestation of behavior that determine the interaction between the individual the environment and others, with ethical values at the center.

It can be labeled Ethical Emotionality and Behavior (EEB).

The third and fourth factors from the first EFA do not correspond exactly to the second and fourth of the second EFA. The solution to this could be that there is an important loop which bridges S and SEI through SE and ECC with the presence of an intermediate module:

B6 Social Emotionality and Behavior (SEB)

There is no complete correspondence among the factors identified by the first and the second EFA. The main conclusion from the inspection of the differences between them is that the second factor of the first EFA includes parts of the ‘Social Emotionality’ (SE) and f Emotional and Cognitive Control (ECC). For this reason it was considered appropriate to include in the model this factor as a module reflecting ‘Social Emotionality and Behavior’ (SEB), since it includes elements of Openness, Novelty Seeking, Reward Dependence and Extraversion.

C. The two super-modules at the top

C1 self (s).

The two groups ‘Emotional Self’ (EmoS) and ‘Cognitive Self’ (CogS) converge to create a top supergroup reflecting aspects of the inner experience and mechanisms of ‘self’ including both emotional and cognitive aspects of ‘Self’ (S).

C2 Self–Environment Interactions (SEI)

‘Social Emotionality and Behavior’ (SEB) groups together with ‘Ethical Emotionality and Behavior’ (EEB) to create a top supergroup reflecting aspects of ‘Self–Environment Interactions’ (SEI).

Theoretical and clinical implications of the model

The literature suggests that aspects of human personality extending beyond temperament usually include attitudes, beliefs, goals, and values. These elements seem to develop out of evolutionarily conserved temperament systems. Personality also includes higher-level cognitive functioning relatively unique to human beings (including language, abstract thought, meta-cognition, etc.). In terms of clinical utility, temperament is mostly studied in relationship to bipolar disorder [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ].

The model developed with the current study suggests that the basic psychological structure in humans comprises two separate super-modules placed at the top of a hierarchical structure. One reflects the perceived components of ‘self’ and the second reflects the interaction of these components with the internal representation of the environment in interaction with its properties which have been internalized and embedded in the character of the person. Both components are ‘internal’ by definition, since they reflect subjective experiences and processing of internalized descriptions and reconstructions of the environment with emphasis on the social environment. An important element is the frequent admixture of emotional and cognitive processes in the same module. Although one of these processes seems to dominate the respective module, very often a component of the other process also exists.

It is very interesting that Social Emotionality (SE) and Social Emotionality and Behavior (SEB) seem to bridge Self (S) and Self–Environment Interaction (SEI). SE contributes to S but also to SEB which in turn contributes to SEI. This ‘bridge’ denotes that the two components of ‘Self’ are kept functionally together by social emotion and the corresponding behavior corresponding mainly to parts of ‘openness’, ‘extraversion’ and ‘reward dependence’ but also with parts of ‘novelty seeking’ and ‘ (Fig.  2 ). In this sense cognitive processes within the S super module probably correspond to inherent pre-existing cognitive templates (biases) while the cognitive processes within the self–environment interaction) probably correspond to meta-cognition.

So far, existing models reflect processes within emotions and within cognition separately, but almost never an interaction of these two modes of psychological function. The early theories focused on activity and affective functioning which were considered to be developmentally stable. Later attention and self-control were added [ 65 ]. These later psychological functions emerged later both in evolution and also during individual development and they are probably shaped also by the environment [ 66 , 67 ]. It is highly likely that the brain circuitry which serves human psychological function is extremely complex with extensive feedback, as well as with simultaneous parallel and serial processing which makes linear analysis and solutions inadequate and relatively naïve [ 68 , 69 , 70 ]. In this sense, the arrows used in Figs.  1 and 2 should be considering only as marking the progress from lower to higher levels of modules rather than that of direction in the flow of information which should be considered to be largely bi-directional.

There are three dominant models of temperament and personality today and there exist significant theoretical and also essential differences between these three theoretical approaches and consequently the respected questionnaires. McCrae and Costa proposed the five-factor model (Big Five) [ 71 ] which includes neuroticism, extroversion agreeableness, openness and conscientiousness and constitutes a further development of Eysenck’s theory. The older concept of ‘psychoticism’ was substituted by agreeableness and conscientiousness while openness has some degree of overlap with extroversion [ 72 ]. Their work is largely based on the classical psycholexical study by Gordon Allport and Henry Odbert [ 73 , 74 ].

The work of Robert Cloninger is characterized by an attempt to intimately connect temperamental characteristics with individual differences in genetics, neurotransmitter systems, and behavioral conditioning. He described novelty seeking (anger), harm avoidance (fear), reward dependence (attachment) and persistence (ambition) [ 22 , 36 ]. His research suggests that temperament components can be assessed as early as preschool age [ 23 ] and remain moderately stable throughout a person’s lifespan except for changes from behavioral conditioning [ 75 ]. A main strength of Cloninger’s Temperament and Character model [ 36 ] is that each temperament dimension was identified and characterized as a relatively ‘pure’ and independently inherited trait that can be ascribed to the basic emotions of fear, anger, attachment and ambition or determination. Fear and anger are the most basic emotions, regulating, respectively, inhibition and initiation of behavior. In the current model harm avoidance is perceived as part of ego-resiliency, that is part of the mechanisms of self to regulate feelings of danger while novelty seeking as part of mechanisms controlling activity. Reward dependence is conceptualized as a mechanism which bridges the two others, while persistence is part of ‘ego strength’.

Hagop Akiskal has conceived temperament as the affective predisposition or reactivity, based on the original descriptions by Kraepelin (1921) of fundamental states (manic or hyperthymic, irritable, cyclothymic, anxious and depressive. These are close to the classic descriptions of Kraepelin, Kretschmer and Schneider [ 25 ]. The model of Hagop Akiskal [ 25 , 43 , 44 , 45 ] concerns exclusively the affective temperament modules and has been conceived while evaluating and observing mood patterns in clinical practice. Empirical research has confirmed the hypothesized four-dimensional factor structure of affective temperament and is in agreement with those previously proposed on clinical populations. Temperament traits according to this model also correspond to fear and anger and it is not surprising that all these temperament traits are included mainly within the ‘self’ module and more particularly within the ‘ego resiliency’ group while the Hyperthymic temperament is also part of the ‘ego-strength’. While Hyperthymic trait is exclusively within the ‘self’ module, Irritability contributes to the ‘self–environment’ module by participating in mechanisms controlling activity, but this contribution is rather weak. The place of these traits within the current model confirms that Akiskal’s model captures the basic affective style and mood pattern as well as identifies individuals with high risk for mood disorders [ 76 , 77 , 78 ], suicidality but also various types of psychopathology (REF: Pompili M, Rihmer Z, Akiskal H, et al. Temperaments mediate suicide risk and psychopathology among patients with bipolar disorders. Compr Psychiatry 2012;53(3): 280–5). Some studies are in accord with the ego-resiliency vs. ego strength sub-organization of affective temperaments proposed in the current model [ 44 , 79 ].

While personality refers to goals, coping styles, defensive styles, motives, self-views, life stories, and identities [ 80 ], basic personality traits (e.g., extraversion or neuroticism) are essentially parts of temperament [ 81 ]. Apart from these, there are three major systems of learning and memory which play a major role in the shaping of human behavior: associative conditioning of habits and skills, declarative learning of facts, and autonoetic learning of a personal lifetime narrative (autobiography) [ 82 , 83 , 84 ].

The current model expands the field to include elements probably corresponding to meta-cognition mechanisms and complex interactions between affective and cognitive control on activity. It was developed exclusively after research on mentally healthy persons so it has no direct relevance to psychopathology. Future research on patient populations might provide with valuable insight concerning the areas of dysfunction in the structure of this model.

According to this model (Fig.  2 ), the Self (S) comprised mainly emotional (EmoS) and thought mechanisms (CogS) which seem to be relatively distinct, highly intrinsic and independent from the environment. There seems to be a significant possibility they reflect the most genetically determined traits. On the other hand, emotional functions dominate the self–environment interaction (SEI) as well as the bridging between the two super-modules, that is Self (S) and Self–Environment Interaction (SEI). The influence of ethical values (EV and EB) seems to constitute a distinct element probably influenced significantly by forces outside the person but still they are internalized. Then it is emotional function related to social tendencies (SE) which stems out of the Self and receives the influence of control mechanisms (ECC) leading to the development of a block of social emotion and behavior (SEB) which in turn is fused with ethical values (EEB) to create the SEI. Control mechanisms (ECC) seem to constitute from two distinct modules, one emotional which has to do with emotional motivation (EM) and on cognitive which probably reflects some kind of meta-cognition (SD).

The gross structure of this model suggests that at the core of psychological function are the internal emotional and cognitive processes which through social emotionality and meta-cognition determine the externalized behavior which is further shaped by internalized social factors in the form of ethical values. It is interesting that both meta-cognitive modules (ECC and EEB) are not purely cognitive but they include a strong emotional component (EM and IIE).

The presence of two super-modules at the top is in accord with previous studies, which reported similar structure but with different functions. The first such study named these super-modules as ‘alpha’ and ‘beta’ since their psychological meaning was unclear. ‘Emotional stability’ was recognized in one of them as the analog of ER, while in the other module traits of extraversion and creativity were identified probably reflecting EIE, EC and EM among others but in a very different hierarchical structure [ 85 ]. It is interesting that Digman et al. interpreting the alpha factor (which among others included emotional stability) and shares some elements with the S super-module as a ‘Social factor’ by theorizing that emotional stability and health are the direct consequence of social environment. These authors interpreted the beta factor with shares elements with the SEI as ‘personal growth and self-fulfillment related to self-actualization’ which is generally not in contrast to the findings of the current study [ 85 ]. Other authors interpreted alpha as ‘stability’ and beta as ‘plasticity’ [ 86 ] or ‘ego control’ and ‘ego resiliency’ [ 26 , 87 ]. Most models suggest the presence of a module of extraversion/positive emotionality, orienting sensitivity, and affiliativeness, and of a second model reflecting negative affect versus effortful control content [ 88 ]. In general these models recognize the presence of a function of ‘effortful control’ which is similar to the EEB module of the current module while ‘Orienting Sensitivity’ could share features with SEB, but the distinction of positive vs negative affectivity modules is not in accord with our findings. The module corresponding to ‘affiliativeness’ is probably IIE and it is located at the lowest level instead of the top [ 89 , 90 , 91 ].

Also the literature concerning the three major theories taken together in the current paper suggests that the four-temperament model of Akiskal [ 26 ], the cube model of Cloninger [ 24 ], the five-factor model represented by the NEO-PI [ 92 ], the seven-factor model of Tellegen [ 93 ] and Cattell’s 16 factor model [ 94 ] may in fact represent different levels of an hierarchical structure of normal and pathological personality with a two-superfactor solution at the top [ 26 , 87 ], a limited number of temperaments in the middle (named under many labels, but significantly overlapping) [ 95 ] and many characters [ 10 , 11 , 12 , 13 , 14 , 15 ] at the bottom. According to most conceptualizations, ‘Temperament’ corresponds to the ‘higher’ levels, while ‘personality’ and ‘character’ to the ‘lower’ [ 96 ]. In another approach, fear and anger could be used in a bidimensional model to describe affective temperament traits [ 97 , 98 ].

An important characteristic of the current model is that it does not accept this hierarchical separation of ‘temperament’ vs. ‘character’ and locates both of them across all hierarchical levels and modules.

Significant outcomes of the current study

The basic psychological structure in humans comprised two separate super-modules (self and its interaction with environmental representation).

The two super-modules are ‘bridged’ by social emotion.

Meta-cognition seems to be a significant element of temperament and this poses important conceptual questions.

A defining finding was the frequent admixture of emotional and cognitive processes in the same module and even in meta-cognition.

An important characteristic of the current model is that it does not accept the hierarchical separation of ‘temperament’ vs. ‘character’ and locates both of them across all hierarchical levels and modules.

Strengths of the current study

The current study utilized a large sample of adequate size of normal individuals more or less representative of the healthy and active population of the country and it is equivalent in size and quality to the study samples of previous similar studies.

The use of the three major questionnaires of temperament and character make this model development unique in the literature.

The utilization of labeling and definitions with a slight psychodynamic orientation in comparison to previous models which were based on the previously defined nomenclature adds an additional unique feature in this model. This made possible the easier interpretation of the functioning of specific modules which had been proven more difficult in previous attempts.

Limitations of the current study

Several limitations of the study should be mentioned. First of all, there are limitations related to the application of linear methods. The method the current model was derived (orthogonal EFA) produces modules which do not correlate to each other. Thus at each level the modules are independent. However, the connection between levels is presumed to be reciprocal although the exact power of each direction remains elusive. Thus the arrows in Figs.  1 and 2 should not be considered as representing the direction of the flow of information and effect but rather they point to the corresponding higher-level module. This reflects a weakness of linear methods and might not correspond to reality, which probably reflects non-linear dynamical systems [ 49 ].

Second, the study has limitations related to the interpretation of the structure. In the literature there is a fundamental question whether modules exist at all or it would be rather more suitable to approach psychological function as a complete indivisible pattern (trait vs. profile) [ 65 , 99 ]. The question is further complicated by the fact that the modules identified in the current model (and in all models) correspond to functions not to anatomical circuits, neurotransmitters or genes. Attempts to correlate temperament isolated traits and genes were promising but so far unsuccessful probably because of complex genetic mechanisms and environmental influence [ 64 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 ].

Third, the study has limitations with respect to the questionnaires used. The scales included in the current study reflect aspects of temperament, character and personality but they do not reflect all theoretical or empirical approaches. There is the possibility the model was biased towards the theories underlying these questionnaires rather than true psychological structure per se.

Future models should utilize non-linear approaches possibly with the use of network analysis and the training of neural networks. Still all these attempts will always be limited by the fact that input will be restricted to inner experience as it is perceived and described consciously by the individual.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Xenia Gonda is recipient of the Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences and was supported by ÚNKP-19-4-SE-19 of the New National Excellence Program of the Ministry of Human Capacities, Hungary.

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KNF and XG conceived the study and wrote the protocol. KNF participated in gathering and analyzing the data. KNF and XG participated in interpreting the data and writing the manuscript. All authors read and approved the final manuscript.

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Additional file 1: table s1..

Composition of the study sample in terms of gender and age in comparison to the general population according to the Greek National Statistics Service for 2009. Table S2. Occupation characteristics of the study sample. Table S3. Factor analysis of the all TEMPS temperament subscales and all NEO-PI-3 domains (N, E, O, A and C) and TCI temperament and character traits (high order traits; HA, NS, RD, PS, SD, CO and ST). Table S4. Factor analysis of factor scores and the results concerning second-order factors of the initial analysis with all TEMPS temperament subscales and all NEO-PI-3 domains (N, E, O, A and C) and TCI temperament and character traits (high order traits; HA, NS, RD, PS, SD, CO and ST). Table S5. Factor analysis with TEMPS temperament subscales and all NEO-PI-3 facet scales (N1-6, E1-6, O1-6, A1-6 and C1-6) and TCI temperament and character facets (lower order traits; HA1-4, NS1-4, RD1-4, PS, SD1-5, CO1-5 and ST1-3). Table S6. Second-order factors of the initial analysis with TEMPS temperament subscales and all NEO-PI-3 facet scales (N1-6, E1-6, O1-6, A1-6 and C1-6) and TCI temperament and character facets (lower order traits; HA1-4, NS1-4, RD1-4, PS, SD1-5, CO1-5 and ST1-3). Table S7. Third-order factors of the initial analysis with TEMPS temperament subscales and all NEO-PI-3 facet scales (N1-6, E1-6, O1-6, A1-6 and C1-6) and TCI temperament and character facets (lower order traits; HA1-4, NS1-4, RD1-4, PS, SD1-5, CO1-5 and ST1-3). Table S8. Pearson Correlation coefficients. Significant were those with R >0.07 at p<0.05 (in bold italics underlined). Table S9. Forward Stepwise Linear Regression Analysis results in order to calculate the score of one subscale on the basis of the subscales of another questionnaire. Overall the results are poor with 7-52% of variability explained.

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Fountoulakis, K.N., Gonda, X. Modeling human temperament and character on the basis of combined theoretical approaches. Ann Gen Psychiatry 18 , 21 (2019). https://doi.org/10.1186/s12991-019-0247-1

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Temperament and Academic Achievement in Children: A Meta-Analysis

This study aimed to systematize the diverse and rather controversial findings of empirical research on the relationship between the temperament and academic achievement of school children, as well as to determine the average effect size between these variables. We included 57 original studies of published and unpublished research conducted in 12 countries between 1985 and 2019, with cumulative sample size of 79,913 (varying from 6333 to 14,126 for links between particular temperament dimensions and specific domains of achievement). A random-effects and mixed-effects model was fitted to the data for the central tendency of the temperament–achievement relation and for analyzing moderators, respectively. The high heterogeneity of studies was tackled by selected specific moderators, namely, education level, transition status, family’s socio-economic level, and sources of report on achievement and temperament. The main findings of this meta-analysis affirmed the positive association of effortful control (EC) and inverse relationship of negative affectivity (NA) with a child’s academic performance, together with no apparent trend of surgency (SU) in this relationship; additionally, the sources of report significantly moderated the link between temperament and academic achievement.

1. Introduction

1.1. interface between temperament and academic achievement.

A comprehensive explanation of variance in academic achievement encompasses cognitive and non-cognitive variables [ 1 , 2 , 3 ]. The latter refers to the wide realm of personality-related attributes, including temperament. Existing meta-analytic reviews have focused on only one temperamental category [ 4 , 5 ], while secondary analyses included only a few temperament-related characteristics as non-cognitive predictors of academic achievement [ 6 , 7 ]. Several studies have reported that many facets of temperament contribute jointly to school performance within an educational context [ 8 , 9 , 10 , 11 ]. To the best of our knowledge, the effect of various temperamental categories on children’s academic achievement has not yet been summarized.

Temperament is the earliest emerging characteristic within an individual and is relatively stable over the school years. It predisposes the child to interact with the environment in a particular way, and it is consistent across situations. Temperament was conceptualized in many ways such as: (1) a child’s behavioral style [ 12 ], (2) individual differences in reactivity and self-regulation [ 13 ], (3) inhibited or uninhibited response to novelty [ 14 ], and (4) patterns of emotionality, activity, and sociability [ 15 ]. Considering the codependency of children’s cognitions and emotions [ 16 ], it can be assumed that children’s temperaments affect their learning and its outcomes; moreover, as per Keogh [ 17 ], it accounts for a child’s ability to use what they know. Thus, it may suggest the important role of temperament—among all non-cognitive determinants—in academic achievement, which refers to “a level of proficiency in scholastic work in general or in a specific skill, such as arithmetic or reading” [ 18 ] (p. 5).

Temperamental attributes in the educational context have been explored for almost a century. There have been supportive opinions regarding its involvement in students’ academic success [ 19 , 20 , 21 , 22 , 23 ] as well as skeptical ones [ 24 , 25 ]. However, this particular research was conducted with college and university students. Systematic empirical studies on temperament in the school context started in the early 1980s. Keogh et al. [ 26 ] extensively supported the hypothesis that teachers’ academic decisions were influenced by their perceptions of pupils’ temperament, especially in cases of limited cognitive or physical development. They investigated how characteristics of children’s temperament (e.g., task orientation, flexibility, and reactivity) influenced teachers’ perception of students as “more teachable” or “less teachable”. Similarly, Lerner and Lerner [ 27 ] found that temperamental fit with the educational demands led to higher academic achievement. Furthermore, Martin and Holbrook [ 28 ] conducted a study that clearly showed how prediction of achievement by temperament (activity, distractibility, and persistence) exceeded that by intelligence. Meanwhile, Maziade et al. [ 29 ] found some evidence in support of temperament’s relationship with achievement, and Talwar et al. [ 30 ] reported indirect effects between temperament and academic skills. These studies were based on the interactional model of temperament, which was developed in the New York Longitudinal Study (NYLS) by Thomas and Chess with colleagues [ 12 ].

In recent times, most studies have been based on the developmental approach to temperament. It was introduced and elaborated by Rothbart and her colleagues [ 13 , 31 , 32 ]. According to it, temperament characteristics consistently fall into three dimensions. Effortful control (EC) is featured by regulatory skills in motor and cognitive domains, manifested mainly through inhibitory control, attention focus and shifting, and perceptual sensitivity. Negative affectivity (NA) is largely defined by sadness, fear, anger, frustration, and poor soothability. Surgency (SU) is mostly described by high activity, impulsivity, and sociability, and a low level of shyness. From the perspective of scholastic success, the least questionable is EC. Studies have highlighted its predominantly positive interface with a broad range of academic performance variables [ 33 , 34 , 35 , 36 ]. However, there were contradictory findings too. For instance, no relationship was found between EC and reading or mathematical achievements among preschoolers [ 37 ]. The direction of interface between NA and academic achievement was also found to be sensitive to multiple aspects. Negative associations of NA with school readiness were reported for preschool-age children [ 36 , 38 ]; for elementary school pupils, only anger—not sadness—produced an inverse relationship with achievement [ 39 ]. Teacher-rated NA of adolescents was associated with higher math grades, while self-rated NA had no link with them [ 40 ]. Studies reported two-fold associations of SU with school achievements, for instance, positive links with pre-academic skills [ 38 ] and reading achievement in the first grade [ 41 ], together with zero correlations with reading skills among school-age children [ 42 ].

Thus, there is a significant research body with rather inconsistent findings on the interface between a child’s temperament and school achievement. Therefore, there is a need to systematize the existing evidence-based findings to understand the central tendency of the relationship between temperament and academic achievement. We found extensive theoretical and empirical support for several factors, considered as moderators, for this relationship. These were as follows: educational level and transitional status, low socio-economic status (SES), and the sources of report about temperament and academic achievement.

1.2. Moderators

1.2.1. educational level and transitional status.

Children progressively climb three educational levels—pre-primary, primary, and secondary—defined by a change in structure of learning environment. There is some evidence that the magnitude of the relationship between temperament and achievement varies depending on educational level. Implications from Maziade et al.’s [ 29 ] study highlighted different patterns of this interface at age 7, compared to age 12. That is, more significant relationships were recorded at the older age; moreover, negative correlation was found between persistence and achievement at age 7 and positive correlation at age 12. In contrast, Al-Hendawi [ 43 ] reported decreasing tendencies of associations between temperament and achievement from childhood to adolescence. This finding is theoretically supported by Chess and Thomas [ 44 ], who suggested that the structure and even the nature of temperament is subjected to the growing influence of environmental forces throughout the school years.

During schooling years, pupils undergo two major transitions—from pre-primary to primary level and from primary to secondary level. Transitioning to an advanced educational setting is relatively short-term but has a very turbulent pace. Temperament operates on a full scale at the points of transition from one educational level to another. This has been the focus of many studies, such as those on the transition from preschool to primary school [ 45 , 46 ], primary to elementary school [ 34 , 47 ], and elementary to high school [ 48 , 49 , 50 ]. Several studies highlighted that adaptation to a new educational level is associated with the fall of academic grades [ 51 , 52 ]. The threat of deviation from an established academic pathway is especially evident at the transition to secondary school [ 50 , 53 ] when the pupil has to navigate the demands of multiple subject-teachers.

1.2.2. Socio-Economic Status

A child’s temperament ties with academic achievement may vary by their family socio-economic status. Numerous studies have documented the unfavorable contribution of low SES to the links between children’s temperament and their academic success.

The NYLS—the first systematic study of temperament—suggested the importance of family SES for the child’s learning outcomes [ 12 ]. Currently, the impact of SES has been explored most extensively among pre-primary and primary school children. Existing empirical evidence suggested that EC is linked with the entire range of variables of academic success [ 33 , 35 , 54 , 55 ]. For instance, Razza et al. [ 56 ] found that the lack of impulsivity at age 5 was a strong predictor of emerging math and literacy skills at age 7 in the poorest, but not in the nearly poor children’s group. This tendency was not confirmed in the extended longitudinal study covering the period of elementary school—early attentional regulation predicted school achievement across both levels of poverty [ 45 ]. The authors tentatively explained this finding by the increased exposure to testing situations during primary school years, unlike the rare experiences in demand-eliciting situations in the families of low SES during early childhood. Similarly, McClelland et al. [ 57 ] found that pupils from low-income families, compared to those from middle-income families, enter the primary school with lower abilities to regulate their attention and behavior, which influence later under-performance at school. Other existing connections suggested that family SES was weakly related to their children’s EC [ 54 ] and NA [ 58 ]; in both cases, the relationship was more significant for literacy but not math skills.

Few studies have longitudinally examined the interaction of SES, temperament, and achievement during subsequent school years. It was affirmed that strong EC and low NA and SU buffered against learning difficulties that stemmed from low socio-economic background in the span from primary to secondary school [ 50 ]. Another longitudinal study derived the conclusion that temperament was also a significant predictor of achievement, at the end of school years, even after controlling SES [ 59 ]. Thus, from a longitudinal perspective, certain children’s temperament categories can counterbalance the risk posed by low family SES.

1.2.3. Sources of Report on Temperament and Achievement

The current understanding on what a child’s temperament means for their achievement was accumulated predominantly using teachers’ and parents’ reports. Day-to-day interactions with children enable adults to assess their more pervasive, consistent traits. However, teachers and parents have disparate views of children’s individual differences [ 49 , 60 ]. While both value adaptability and learning-related persistence [ 61 ], they exhibit distinct opinions on a child’s emotionality and regulation [ 62 , 63 ], particularly on negative emotionality [ 64 ]. The NYLS findings suggested that parents could aggravate a child’s negative mood and adaptational troubles if they had rigid parenting standards and high expectations for school achievement [ 44 ]. In the school context, a teachers’ impression about students interfered with the grading of their performance [ 42 , 65 ]; additionally, these impressions were assumed to explain why children’s temperament was related to their achievement [ 40 ].

There are various sources of report on school achievement as well. It is commonly evaluated by the teacher-assigned grades (e.g., grade point average (GPA), rating scales) and by standardized testing. GPA is considered a more valid indicator of a student’s achievement because it generalizes the quality of many assignments over time [ 66 ], and it predicts students’ future academic achievement more accurately compared to other assessment methods [ 67 ]. It is also considered to be more sensitive to the individual differences of children [ 9 , 68 ] and to the subjectivity of teachers’ opinions [ 43 ]. Compared to GPA, standardized testing is assumed to be a more objective and a useful measure of students’ achievement [ 43 , 69 ]; however, its ability to adequately capture acquired knowledge and skills is limited [ 70 , 71 ]. Studies have shown that certain individual characteristics of children are more sensitive to a particular assessment method of achievement [ 72 , 73 ]. Therefore, both the teacher-assigned grades and standardized testing reflect different aspects of academic performance.

1.3. Present Study

Our decision to conduct a meta-analysis on the relationship between children’s temperament and their academic achievement was guided by the necessity for clear implications from previous studies [ 57 ]. Excluding a few reviews [ 9 , 43 , 74 ], there has been no meta-analysis on this issue. Meanwhile, some other non-cognitive correlates of academic outcomes—physical activity [ 75 ], creativity [ 76 ], personality [ 77 , 78 ], subjective well-being [ 79 ], self-concept [ 80 ], early life non-cognitive skills [ 2 ], and so on—have already been meta-analyzed.

Currently, a vast majority of empirical data on pupils’ temperament has been based on Rothbart’s model [ 31 , 32 , 81 ]. This model groups inherent psycho-biological characteristics into three dimensions (EC, NA, and SU), and captures the full range of child behaviors across all educational levels. It particularly emphasizes on children’s regulatory function, which is very important within the school context. There were also studies based on other theoretical approaches (the interactional and the criterial). We included them into our meta-analysis as well. In this case, temperament categories were considered as semantic equivalents of EC, NA, and SU, following the grouping by Else-Quest et al. [ 82 ] (p. 57).

Thus, our focus lay on the relationship of EC, NA, and SU with the overall, math, and reading achievement of children. To generalize the accumulated data on these links, we set two goals: first, to investigate the effect size between them; second, to examine the impact of potential moderators on the aforementioned relationship. Therefore, we expected to clarify the magnitude of the mainly positive and tentatively negative effects of EC and NA, respectively, on the school performance, with no clarity about the direction of the SU contribution. Those factors whose influence on the temperament–achievement relationship was already affirmed in a majority of existing studies, were used to tackle the plausible heterogeneity of studies. We believe that this meta-analysis will help to contextualize and specify the relationship between pupils’ temperament and their academic achievement.

2. Materials and Methods

This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 83 ]. The protocol for this meta-analysis was not registered.

2.1. Eligibility Criteria

To be included in the analysis, a study had to meet the following criteria: (1) an original empirical research on the relationship between temperament and academic achievement; (2) conducted with school-age children; (3) reliable instruments and/or procedures; 4) only one empirical report was taken if the same sample results were presented in several sources.

2.2. Information Sources and Search Strategy

The literature search was conducted in December 2019 without limiting the search date. Three strategies were used. First, we searched for the studies in the following databases: Web of Science (Clarivate Analytics), ScienceDirect, and the package of EBSCO Publishing Databases (including PsycINFO, PsycARTICLES, ERIC, MEDLINE, SocINDEX, Teacher Reference Center, and OpenDissertations). We combined the term “temperament” with terms such as “academic achievement”, “academic performance”, “grade point average”, and “GPA” using the Boolean operators AND and OR. We searched the combinations of terms in titles, abstracts, and/or subject terms, depending on the capabilities of the databases. Second, we looked for unpublished studies. For this, we disseminated information on our meta-analysis on the Temperament Consortium website ( https://www.b-di.com , accessed on 9 November 2019), which unites temperament professionals around the world. We also contacted the Australian Temperament Project ( https://www.melbournechildrens.com , accessed on 9 November 2019) with a request to share unpublished research data on temperament and academic achievement. Third, we conducted a backward literature search in the references of studies already included.

2.3. Data Collection Process and Coding

2.3.1. effect measure and main variables.

The effect size was a bivariate correlation coefficient between temperament and academic achievement. All temperament variables presented in the studies were assigned in terms of meaning to one of the three dimensions—EC, NA, or SU. The teacher assessments and the results of the standardized achievement tests were considered as academic achievement indicators. These variables were assigned to mathematics, reading, and overall (i.e., several curriculum subjects). When a few correlation coefficients between temperament and achievement were presented in the same study, they were averaged, transforming each coefficient into Fisher’s z and converting back to r after averaging [ 84 , 85 ]. Averaging was necessary when the study reported correlations between (a) several indicators of the same temperament dimension and academic achievement, (b) temperament and several indicators of academic achievement, (c) temperament and academic achievement in the same sample at different times (longitudinal studies), and (d) temperament and academic achievement from more than one informant (e.g., teachers and parents). Averaging was not applied when correlation coefficients were provided on how a broad dimension (e.g., EC) and its components (e.g., inhibitory control, attention focusing) were linked with the other broad domain (e.g., reading) and its components (e.g., letter–word identification, reading fluency). To further the analysis, preference was given to the broad variables. Thus, only one correlation coefficient was taken from one sample, showing a relationship between the same dimension of temperament and the same domain of academic achievement.

2.3.2. Other Variables

Selected studies included children from different educational levels. Some studies covered several educational levels at the same time. The educational level was coded according to when academic achievement was assessed ( pre-primary = 1, primary = 2, secondary = 3). If the educational level was not specified in the study, we relied on country-specific information [ 86 ] according to the age and/or grade of the participants. We did not include studies that combined children from two educational levels, namely, both pre-primary and primary [ 87 , 88 , 89 , 90 , 91 ] and both primary and secondary [ 30 , 92 , 93 , 94 , 95 ]. We classified these studies into transition (= 1) or non-transition (= 0).

We categorized all the studies by SES risk ( non-risk = 0, risk = 1). If the authors did not classify their sample by socio-economic origin, we assigned a sample to the risk/non-risk group based on the sample description. For example, if most participants were indicated as receiving free meals, we identified such a sample as SES risk; if most participants belonged to the middle class, we assigned such a sample to the non-risk group.

The sources of information about temperament were parents (= 1), teachers (= 2), or self-report (= 3). When information was obtained from several sources (e.g., both parents and teachers), the source was coded as multiple (= 4). There was only one study [ 34 ] in which temperament was assessed using laboratory procedure; so, we did not include it in this moderator analysis. Sources of information on academic achievement were teacher assessments (= 1) or standardized achievement test scores (= 2). When information was obtained from several sources (e.g., both grades and standardized tests), the source was coded as multiple (= 3).

Prior to coding, a coding protocol was developed. Both authors tested it independently on 20% of randomly selected studies. After discussing the issues raised, the coding system was improved, and the rest of the study was coded by one of the authors. Data analysis was initiated only after full consensus was reached.

2.4. Synthesis Methods

To answer questions about the central tendency of relation between temperament and academic achievement, the average effect size was calculated using the Fisher r -to- z transformed correlation coefficient as the outcome measure. A random-effects model was fitted to the data [ 96 , 97 , 98 ]. The studies were weighted using their sampling variance and the estimated amount of heterogeneity [ 99 ]. The relationships between the three dimensions of temperament (EC, NA, and SU) and the three domains of academic achievement (overall, math, and reading) were calculated separately. The benchmark values of 0.10, 0.20, and 0.30 for small, medium, and large effect size, respectively, were chosen [ 100 , 101 ]. The amount of effect size heterogeneity (τ 2 ) was estimated using the restricted maximum-likelihood estimator [ 102 ] also taking the Q -test for heterogeneity [ 103 ] and the I 2 statistic [ 104 ]. The Q -test revealed the fact of heterogeneity (when p < 0.05) [ 105 ] while I 2 values of 25%, 50%, and 75% meant low, medium, and high inconsistency, respectively [ 104 ].

We also inspected the presence of outliers and/or influential studies. Studies with a studentized residual larger than the 100 × (1 − 0.05/(2 × k ))th percentile of a standard normal distribution were considered potential outliers. Studies with a Cook’s distance larger than the median plus six times the interquartile range of the Cook’s distances were influential [ 106 ]. The rank correlation test [ 107 ] and the regression test [ 108 ], using the standard error of the observed outcomes as predictor, were used to check for funnel plot asymmetry. In both cases, statistical significance ( p < 0.05) indicated evidence of publication bias.

To explain the heterogeneity across studies, we conducted the analysis of each moderator. A mixed-effects model was fitted to the data using the restricted maximum-likelihood estimator [ 97 , 109 ]. All moderators were categorical, and the moderator was considered significant, if the effect size differed statistically significantly ( p < 0.05) across groups [ 110 , 111 ].

The statistical analysis was carried out using the metafor package (version 2.1.0) [ 99 ], the multcomp package (version 1.4-13) [ 112 ], and the dmetar package (version 0.0.9000) [ 113 ] in R (version 3.6.2) [ 114 ].

3.1. Study Selection

The results of study search and selection process are presented in a flow diagram ( Figure 1 ) and described below.

Flow diagram.

The initial search yielded 454 records. Identical records within and between database were excluded before screening and 176 records remained. We reviewed the titles and abstracts of these records and those that did not meet the first two eligibility criteria (e.g., lack of temperament and/or academic achievement variables, study sample age beyond our set limits, review articles, other than correlational design) were excluded from further analysis. We thoroughly assessed remained 62 reports and excluded 14 that did not meet the third and fourth eligibility criteria. Namely, several studies presented results of the same sample, such as publications from the Study of Early Child Care and Youth Development at the National Institute of Child Health and Human Development or the Finnish Study of Temperament and School. To avoid duplication of the sample across many publications from the same project, we selected an article that covered more relevant variables and/or a larger sample. One more reason for exclusion was the low reliability of temperament measures (e.g., Cronbach’s value of the scales < 0.60). We carefully reviewed the bibliographies of the selected reports and such backward search added nine more studies. The result of the study selection included 57 original studies on the relationship between temperament and academic achievement.

3.2. Description of Studies

Fifty-seven original studies were selected for the meta-analysis: 48 published articles (84.2%), 7 doctoral dissertations (12.3%), and 2 with unpublished research data (3.5%). These studies included research on the relationship between temperament and academic achievement in 12 countries from 1985 to 2019. The 47 samples represented one level of education: 6 pre-primaries (10.5%), 28 primaries (49.1%), and 13 secondaries (22.8%), while 10 samples (17.5%) were transitional (i.e., from two educational levels). Forty-two samples (73.7%) were selected from diverse SES backgrounds, and fifteen samples (26.3%) were assigned to the SES risk group. In 18 samples, the sources of information on temperament were the parents (31.6%), in 17 samples—the teachers (29.8%), 9 samples—self-report (15.8%), 12 samples—multiple sources (21.1%), and in one study, the temperament was assessed by a laboratory procedure (1.8%). In 25 samples (43.9%), the information on achievement was provided by the teachers, in 19 samples standardized tests (33.3%) were used, and in 13 samples the achievement data relied on multiple sources (22.8%). Detailed descriptive information on each study is provided in Table 1 .

Descriptive information of studies included in the meta-analysis.

1 Country abbreviation are given in accordance with the International Organization for Standardization [ 144 ]. EC = effortful control; NA = negative affectivity; SU = surgency; O = overall academic achievement; M = math achievement; R = reading achievement.

3.3. Average Effect Size

Nine average effect sizes, expressed in Fisher r -to- z transformed correlation coefficients, were obtained to describe the relationship between the three dimensions of temperament (EC, NA, and SU) and the three domains of academic achievement (overall, math, and reading) ( Table 2 ). Neither the rank correlation (Kendal’s tau), nor the regression test (Egger’s test), indicated any funnel plot asymmetry ( p > 0.05). Below, the results are presented in a more detailed and sequential manner, according to the relationship of each of the three dimensions of temperament with the three domains of academic achievement. A complete list of studies with individual effect sizes can be found in Appendix A .

Results of average effect size and test of heterogeneity.

k = number of studies; N = sample size; ES r = average effect size; CI = confidence interval; SE = standard error; z = test for significance of ES r ; τ 2 = estimated amount of total heterogeneity; Q = test for heterogeneity; df = degrees of freedom; I 2 = total variability (%). * p < 0.05. ** p < 0.01. *** p < 0.001.

Correlations between EC and academic achievement ranged from −0.10 to 0.78 (overall), −0.12 to 0.55 (math), and −0.08 to 0.63 (reading). Most of the estimates (96–98%) were positive. There was no indication of outliers across all three models; however, one study appeared to be overly influential for the EC and overall achievement model [ 116 ].

Similarly, correlations of NA with academic achievement ranged from −0.37 to 0.09 (overall), −0.45 to 0.25 (math), −0.39 to 0.00 (reading), with most estimates (90–95%) being negative. Moreover, no indication of outliers was present for these models, but one study [ 130 ] was considered as overly influential in the NA and overall achievement model. In addition, one study [ 119 ] had a relatively larger weight compared to the rest of the studies in the NA and math achievement model.

Correlations between SU and overall achievement ranged from −0.30 to 0.44, and one study [ 116 ] may be a potential outlier as well as overly influential for this model. Relationships between SU and math achievement ranged from −0.30 to 13, and this model had no outliers. However, one study [ 123 ] could be considered as overly influential. Lastly, correlation coefficients between SU and reading achievement ranged from −0.25 to 0.44. One study [ 93 ] may be a potential outlier in this model; furthermore, two studies [ 87 , 120 ] could be overly influential. In all three models, slightly more estimates (52–60%) were negative.

In sum, the highest average effect sizes—from medium to large—were obtained between the EC and overall, math, and reading achievement. Small negative average effect sizes were found between the NA and all the three academic achievement variables while effect sizes between academic achievement and SU was received close to zero and were statistically non-significant. Analysis of heterogeneity showed that all the average effect sizes were significantly heterogenous with low to high inconsistency. This meant that average effect sizes could be affected by potential moderators.

3.4. Analysis of Moderators

A moderator analysis was performed to verify whether the average effect size between temperament and achievement varied between groups by educational level, transition status, SES risk, and source of information on temperament and achievement ( Table 3 ).

Moderator analysis: educational level, transition status, SES risk, and source of information on temperament and academic achievement.

1 The reference category (intercept). For convenience, real values of effect size and confidence interval are presented, whereas analysis for heterogeneity results are presented with intercept. Asterisks next to effect sizes indicate the statistical significance of the temperament–achievement relationship for each category of moderator. EC = effortful control; NA = negative affectivity; SU = surgency; k = number of studies; ES r = average effect size; CI = confidence interval; Q = test of moderators (omnibus). * p < 0.05. ** p < 0.01. *** p < 0.001.

Analyses revealed that educational level (pre-primary, primary, secondary), transition status (transition, non-transition), and SES risk (risk, no risk) were not statistically significant moderators. In all these cases, Q value was low and not significant as well as the categories of these moderators did not differ statistically significantly from their reference categories (intercepts). That is, the temperament–achievement relationships were similar—statistically significantly or not—between different categories of educational level, transition status, and SES risk.

Conversely, sources of information on both temperament (parents, teachers, self-report, multiple) and academic achievement (teacher assessments, test scores, multiple) were statistically significant moderators. The results showed statistically significant differences among effect sizes of EC in relation to math achievement ( Q (3) = 8.86, p < 0.05); effect size was significantly higher when information was provided by teachers compared to parents (intercept) ( p < 0.01). Significant differences were also observed in effect sizes between NA and math achievement ( Q (3) = 11.92, p < 0.01) as well as reading achievement ( Q (3) = 14.58, p < 0.01). Specifically, when parents (intercept) reported on the child’s temperament, the effect size between NA and math was statistically significantly lower, compared to reports from teachers ( p < 0.05), self ( p < 0.05), and multiple sources ( p < 0.05). A very similar result was found for the effect size between NA and reading; a statistically significantly lower effect size was found when the source of information was parents (intercept) compared to when the sources were teachers ( p < 0.001) and multiple ( p < 0.05). The results revealed statistically significant differences among effect sizes of EC and overall achievement depending on the source of the information on academic achievement ( Q (2) = 12.48, p < 0.01). Effect size was significantly higher when achievement was assessed by the teachers (intercept) compared to test scores ( p < 0.001).

In sum, neither the education level nor the transition status and the SES risk had a statistically significant influence on the relationship between temperament and academic achievement. Only the source of information (on temperament and academic achievement) appeared to have a statistically significant influence on relation between temperament and academic achievement variables.

4. Discussion

To the best of our knowledge, our meta-analysis was the first attempt to synthesize the empirical findings on the relationship between three temperament dimensions and academic achievement in school children. It appeared that the three temperament dimensions—EC, NA, and SU—were positively, negatively, and unclearly, linked with school achievement, respectively. EC and NA interacted with achievement in a very narrow range of significant effect size, regardless of the academic subject. In other words, the average of the relationships between EC and math was 0.24, between EC and reading was 0.25, between NA and math was −0.13, and between NA and reading was −0.14. The very close effect sizes in each dimension, together with a lack of outliers, suggested that the existing research confirmed a definite contribution of EC and NA to achievement. Each result seemed to be a part of a larger study on this particular relationship, despite differences in sample size or educational level.

Regarding positive EC links with children’s achievement, this was expected. Compared with findings from the very few published secondary analyses, our conclusion specified this relationship and challenged the notion that it was dependent on the nature of the academic subject. Specifically, a previous meta-analysis on a particular facet of EC—inhibitory control—in preschoolers reported it to be stronger associated with math-related skills, compared with literacy-related ones [ 4 ]. Another meta-analytic investigation that also dealt with children’s individual differences in relation to their achievement [ 78 ] suggested that in primary education, the strongest predictor of achievement was a child’s conscientiousness, which was believed to develop from the child’s EC [ 145 ].

There was no documented evidence summarizing the relationship of NA and achievement from individual studies; however, there are some findings on NA-related characteristics. For instance, negative arousal was found to be inversely linked to academic achievement [ 146 ]. Extensive research highlighted that negative emotionality hindered a child’s capacity to develop the skills needed for success in school [ 147 , 148 , 149 ]. Additionally, there were sporadic findings of no associations between NA and academic outcomes [ 150 ] and split opinions by caregivers and teachers on this issue [ 142 ]. Thus, we claim our testimony of definite inverse links of NA with academic achievement to be an incremental finding of our meta-analysis.

The evidence on SU collected so far appeared to be so conflicted that the overall size of the relationship between SU and scholastic achievement yielded an insignificant outcome. The effects were distributed over a very wide range from −0.30 to 0.44, with several outliers and influential studies. Even from a theoretical perspective, the multifaceted composition of SU could be related to a mixed contribution to academic achievement. On the one hand, it comprises an activity level. Some studies claimed its positive link to academic outcomes [ 151 , 152 ] or at least to math grades [ 11 ]. On the other hand, SU also encompasses impulsivity, which is typically negatively linked to achievements [ 45 , 153 ]. Therefore, it is possible that one facet of SU “neutralized” the other during aggregation of primary findings.

The moderator analysis helped to specify the discovered central tendencies within the educational context. Educational level and transition status were not statistically significant as moderators, thereby supporting the testimony of other authors [ 154 , 155 ] that the temperament–achievement connection is stable over the school years. Additionally, SES was also not significant moderator. Its hypothesized moderator capacity was possibly weakened by the inconsistency of SES measurement among different studies: (1) based on the family income [ 56 ], (2) a composite indicator for poor districts in a particular state [ 87 ], or 3) an averaged index of socioeconomic risk [ 50 ].

Sources of information on temperament and achievement appeared to influence the relationship in question. It was affirmed that children’s EC and NA were more strongly related to achievement, when temperament was reported by teachers than parents. Additionally, the relationship between EC and performance was stronger in the case of teacher-assigned grades than standardized testing. This meta-analytic message suggests that teachers’ perceptions of child temperament could affect their pedagogical decisions, which was consistent with other reports. For instance, it was found [ 156 ] that teachers tended to overestimate the achievement level of their class and were barely accurate in the assessment of negative emotions of the students. Deater-Deckard et al. [ 42 ] provided evidence that perceived polarities of pupils’ temperament were related to the mismatch between the assigned grades and children’s abilities. On the other hand, there are reports [ 157 ] supporting the accuracy of teachers’ assessments.

4.1. Implications

Our aggregated results clearly indicated the definite advantage of EC and the obvious disadvantage of NA for achievement outcomes. A child with high EC learns in a more autonomous way and is more self-reliant in the classroom with a minimum need for monitoring. A child with high NA has a different approach to school assignments and a very limited capacity to overcome their mood extremes. Frustration, irritability, and anger could jeopardize the realization of their full learning potential. Thus, children with high NA need to be taught to recognize and cope with these primary reactions, to embrace their strengths, rather than persisting in negative states.

These findings require a mindful reflection. Teacher-assigned grades seemed to have an objective and subjective component—the evaluation of accumulated knowledge and the assessment of temperament, respectively. The contribution of a non-cognitive factor to academic achievement poses the threat of educational labelling, especially for negative emotionality. From this perspective, the very concept of possibly more or less “teachable” children or “ideal” students could be questionable. Every child is teachable in their own way and pace.

Moreover, ignoring a child’s temperament may lead to their frustration and a defensive stance towards learning [ 158 ] and cause misinterpretation of feelings such as frustration as disobedience [ 65 , 159 ]. Several intervention programs [ 160 , 161 , 162 ] and scientific projects [ 64 , 163 ] have already demonstrated the effectiveness of tailoring education to pupils’ temperament. Their incremental value can be seen through the various benefits for parents, children and teachers as follows: (1) It enabled parents to understand the basics of child’s tentatively differentiated behavior in home and school. (2) It allowed pupils to identify their unique learning style, and implement their strengths and move beyond their limitations. (3) It paved the way for teachers to engage in evidence-based teaching, by being a more effective teacher, who is sensitive to individual differences, rather than working harder. (4) Additionally, it enhances teachers’ awareness of the effect of their own temperament on their professional decisions and expectations.

These benefits may serve as guidelines for the development of educational policies. Temperamental attributes lay the groundwork for personalized learning and provide a framework to identify their own individuality in the classroom. The growing diversity in education requires constant renewal and review of effects of the patterns of temperamental characteristics in transforming the teachings into learnings. These issues should become an integral part of teachers’ trainings.

4.2. Limitations and Guidelines for Future Studies

There are certain constraints regarding the generalizability of our results. A different conceptual framework of temperament or choice of moderators would possibly yield different and challenging results. The adoption of a tri-partite constellation of temperament, which generated most of the existing research, required us to classify the attributes into three broad groups. It confined our sensitivity to a genuine understanding of authors. We relied on the capability of meta-analyses to provide a synthesized solution, i.e., a broader picture, by looking “at the forest instead of trees”. Certain distinct characteristics in one particular dimension could act contradictorily within educational settings. Therefore, a meta-analysis on the broad categories of temperament combined with their narrower attributes would be useful in the future.

In most cases, the moderator analysis was conducted with an asymmetrical number of studies in the comparable groups. The synthesized picture of the research fields documented the frequency for the investigation of some less common samples. Although there are no strict guidelines on the number of groups to be compared within a meta-analysis, the results of such a comparison should be interpreted with some caution. For instance, this meta-analysis was limited due to teachers’ and parents’ reports dominance over self-reports and laboratory assessments of temperament; furthermore, primary educational level was investigated more than the secondary level. According to our observation, contemporary research involves a wider range of data sources and information from a variety of contexts. Thus, future original studies can be expected to include a bigger variety of data sources, a wider age-range of participants, and assessment of temperament in real-life situations.

5. Conclusions

The main findings of this meta-analysis confirmed the various contributions of distinct temperament dimensions—the affirmative, unfavorable, and indefinite effect of EC, NA, and SU, respectively—to a child’s academic performance. Contrary to expectations, some of the selected moderators—educational level, transition status, and family’s SES—did not reduce the heterogeneity of studies on the link between achievement and particular dimensions of temperament. Meanwhile, the sources of the report appeared to be relevant. Specifically, the relationship between children’s temperament and their achievement was significantly stronger when the teachers provided data on child’s temperament and when teachers assigned the grades.

Acknowledgments

The authors thank Robin L. Hegvik, Heather A. Henderson, and Lisa G. Smithers for sharing unpublished data of their research. The authors are also grateful to the late William B. Carey for his valuable advice in the early stages of our study.

List of studies with individual effect sizes: Effortful control and academic achievement.

EC = effortful control; NA = negative affectivity; SU = surgency; N = sample size; # ES = number of effect sizes, from which one effect size was obtained; ES r = row effect size; y i = transformed effect size; v i = sampling variance; w i = weight (the values are given in percent).

List of studies with individual effect sizes: Negative affectivity and academic achievement.

List of studies with individual effect sizes: Surgency and academic achievement.

EC = effortful control; NA = negative affectivity; SU = surgency; N = sample size; # ES = number of effect sizes, from which one effect size was obtained; ES r = row effect size; y i = transformed effect size; v i = sampling variance; w i = weight (the values are given in percent.

Author Contributions

Conceptualization, D.N. and T.L.; methodology, D.N. and T.L.; formal analysis, T.L.; data curation, T.L.; writing—original draft preparation, D.N. and T.L.; writing—review and editing, D.N. Both authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Master Guide

I. prerequisites.

• Be a baptized member, in regular standing, of the Seventh-day Adventist Church.
• Have a written recommendation from your local church board.
• Be at least 16 years of age to start the Master Guide curriculum and at least 18 years of age at completion.
• Be an active staff member of an Adventurer or Pathfinder Club.
• Adventurer Ministries
• Pathfinder Ministries

Additional Prerequisite Notes

• The Master Guide curriculum must be completed in a minimum of one year and a maximum of three years.
• Those completing the Master Guide curriculum do so under the supervision of the Conference/Mission Youth Ministries Director or his/her designated Master Guide.

II. Spiritual Development

• Read or listen to the book “Steps to Christ” and submit a one page response focusing on the benefits of the reading.
• Complete the devotional guide “Encounter Series I, Christ the Way”, or complete another year long Bible reading plan that covers the four Gospels.
• Read or listen to the book “Desire of Ages”.
• Keep a devotional journal for a minimum of four weeks, summarizing what you learned in your devotional time and outlining how you are growing in your faith.
• Complete the “Steps to Discipleship Personal Spirituality Curriculum”
• Be involved in one or more evangelistic or community based outreach programs.
• Prepare a one page point form (bullet point) summary on each of the 28 fundamental beliefs.
• The Experience of Salvation
• Growing in Christ
• The Remnant and its Mission
• Spiritual Gifts and Ministries
• The Gift of Prophecy
• The Sabbath
• Christ’s Ministry in the Heavenly Sanctuary
• The Second Coming of Christ
• Death and Resurrection
• Earn the Adventist Church Heritage Award.
• “The Pathfinder Story”
• “We are The Pathfinders Strong: The First Fifty Years” by Willie Oliver with Patricia Humphrey.
• Another book that is approved by your supervising Conference/Mission Youth Ministries Director.
• “Anticipating the Advent” by George Knight.
• “Tell It to the World” by Mervyn Maxwell
• “Light Bearers to the Remnant” by Richard W. Schwarz and Floyd Greenleaf
• “The Church Heritage Manual” produced by the General Conference Youth Department.
• Complete a two page (minimum) research paper about a standard temperament analysis program and complete the affiliated temperament inventory.

III. Skills Development

• How to be a Christian leader
• Vision, mission, and motivation
• Risk Management for Adventurer and Pathfinder Ministries
• Communication theory, listening skills,
• Practical communication practices
• Understanding and teaching to learning styles
• How to prepare effective creative worship
• Understanding and using creativity
• Principles of youth and children’s evangelism
• How to lead a child to Christ
• Understanding your spiritual gifts
• Christian Storytelling
• Camping Skills I-IV
• Drilling & Marching (if this Honor is not applicable in your country’s culture, suggest an alternative to your supervisor)
• Earn two additional honors or awards of your choice, not previously earned.
• Hold a current Red Cross First Aid and Safety certificate or its equivalent.
• Supervise participants through either an Adventurer or Pathfinder Class level or teach a Sabbath School class for at least one year to a group of children ages 6 to 15.

IV. Child Development

• Read or listen to “Education” and submit a one page response focusing on the benefits of your reading.
• Read or listen to either “Child Guidance” or “Messages to Young People” and submit a one page response focusing on the benefits of your reading.
• Attend three seminars dealing with child development or educational theory relating to the age of your primary ministry group.
• Observe for a period of two hours a group of Adventurers or Pathfinders and write a reflection on their interaction with each other and with the staff.

V. Leadership Development

• “Positive Church in a Negative World” by John Arrias.
• “Take The Risk” by Ben Carson.
• “Ellen White on Leadership” by Cindy Tutsch.
• “Help! I’m being Followed” by Clinton Valleyn.
• Other book recommended by your supervising Conference/Mission Youth Ministries Director.
• Develop and conduct three creative worships for children and/or teenagers.
• Participate in a leadership role with your local church children’s/youth group in a Conference sponsored event.
• Teach three Adventurer Awards or two Pathfinder Honors.
• Assist in planning and leading a field trip for a group of Adventurers, Pathfinders, or Sabbath school class for children ages 6 to 15.
• Be an active Adventurer, Pathfinder, or Sabbath school staff member for at least one year and attend at least 75% of all staff meetings.
• Write goals that you would like to accomplish in your ministry.
• Identify three current roles in your life, at least one of which is spiritually oriented, and list three goals or objectives for each.

VI. Fitness Lifestyle Development

• The physical components of the AY Silver Award. If you already have the AY Silver Award, then move on to the AY Gold Award.
• A school physical fitness program.
• A personal physical fitness program based on a fitness book of your choice or a workout program in consultation with your local supervising Master Guide or authorized instructor.

VII. Documentation

• Compile a portfolio documenting all of your work related to completing the Master Guide curriculum.
• Seminars should be of at least 90 minutes in length. All the seminar facilitators must be approved by the Conference youth director or the church pastor.
• It would be advantageous for a Master Guide to have working knowledge of both Adventurers and Pathfinders ministries.
• Suggested themes, questions and subjects are given in the “Master Guide Curriculum Manual.”
• The “Steps to Discipleship Personal Spirituality Curriculum” can be found and download at the General Conference website: www.gcyouthministries.org

This is an official website of the Seventh-day Adventist Church. Learn More about Adventists .

What Leaders Must Know About Temperaments

T he direction Ellen G. White gives to pastors in Gospel Workers , page 338, can also be applied to leaders in other phases of church ministry: “The pastor meets with an endless variety of temperaments; and it is his duty to become acquainted with the members of the families that listen to his teachings, in order to determine what means will best influence them in the right direction.”

Considering the Sabbath School to be the church family at study, it is easy to apply the following counsel to Sabbath School leaders: “Marked diversities of disposition and character frequently exist in the same family [Sabbath School], for it is in the order of God that persons of varied temperament should associate together. When this is the case, each member of the household [Sabbath School] should sacredly regard the feel­ings and respect the right of the others . . . Harmony may be secured, and the blending of the varied temperaments may be a benefit to each” (Ellen G. White, Child Guidance , p. 205).

Define the Terms 

Because the words temperament , character , and personality are often used interchangeably, let’s define our terms:

Temperament from the Latin temperare , meaning “to mingle”—23 chromosomes from mom blended with 23 chromosomes from dad produce you. According to the Random House Dictionary , temperament is “the combination of inborn traits, the unique constitution of an individual that affects the manner of thinking, feeling, and acting.”

Character is taken directly from the Latin and refers to an instrument for branding. The basic temperament is modified (branded) by personal experiences, training, education, principles, motivations, basic attitudes, beliefs, and the influence of supernatural powers. While we cannot choose our inborn traits, we have almost limitless choices as to what we will do with the temperament package.

Personality is derived from the Latin persona , meaning a mask worn by an actor. Personality can be used to mask undesirable but cherished traits, to erect a pleasant facade, to hide an unpleasant or weak character.

So we can see that temperament is the only one of the three attributes that God puts into each person’s hereditary package.

Listen and Observe 

Of course the first task of all Sabbath School leaders is to become aware of their own strengths and weaknesses and how these impact the relationships with those on their ministry team as well as those the entire team serves. 

As principal or coordinator of the school staff, the superintendent is responsible for ensuring that those on the team understand and apply this information. So let’s do a brief review of the four temperament types that I hope will pique your interest enough to lead you to deeper study.

Long before the Greek philosopher, Hippocrates, mistakenly labeled the temperaments after body fluids, the inspired writer of Proverbs described four types of people in Proverbs 30:11-14. I have developed a temperament study based on music and will apply musical interpretation terms to each type for this study on temperaments:

Verse 11 - doloroso (sad, mournful) Verse 12 - secco (dry)  Verse 13 - animato (animated)  Verse 14 - grandioso (grand) 

Next, what visual clues about these four types can you see in the art of Matthew Moore, a freelance artist in North Highlands, Califomia?

The value of the music illustration is that people learn to continue to think of a person as part of the musical composi­tion of their life even when confused with the weaknesses of that person.

Expressions 

So how could the temperaments play out in Sabbath School on Sabbath morning? Of course, the negatives would be seen and heard only because the persons had not submitted themselves to the power of the Holy Spirit.

Animato : These good storytellers make friends easily but may waste time talking and exaggerating. They may be easily angered but apologize quickly.

Grandioso : These goal-oriented people seek practical solutions and exude confidence. However, they can be rude and tactless. When their quick temper is ignited, they do not apologize quickly and tend to hold grudges.

Doloroso : These creative and compassionate Sabbath School members are schedule oriented and self-sacrificing. They can also be critical and suspicious and hear selectively, being unforgiving and sulking over disagreements.

Secco : These agreeable good listeners are easygoing. And although they are excellent at mediating problems, they have a tendency to avoid responsibility and to judge other people.

Even though most people reflect the tendencies of two temperaments, this short list of the many traits of each underscores that there is not a perfect single temperament or combination.

Useful temperament analysis tools that I have found include the materials prepared by Robert J. Cruise and W. Peter Blitchington, which are available through the Andrews University Press, 1-800-467-6369.

Current pricing: Understanding Your Temperament , the guidebook, $3.99; test booklet, $1.25; and four reusable scoring templates, $3.99. Use these tools to determine what percentage of all four temperaments you possess.

The Statistics 

Len McMillan, Ph.D., is director of' family life education at the church-owned Pacific Health Education Center in Bakersfield, California. He and his wife scored the temperament inventories of more than 9,000 people in the United States, Canada, and South Africa. From that survey (reported in the August 1997 issue of Sabbath School Leadership ), they concluded that the Adventist Church tends to attract or retain particular temperament blends.

Members with the outgoing but dominating grandioso tendencies and the perfectionistic yet creative doloroso temperaments dominated the survey. The easygoing secco types, having a quiet will of iron, make a less spectacular showing. The charismatic but disorganized animato types took last place.

One Analysis . The McMillans concluded that animatos, like the apostle Peter, are often the first to respond to evangelistic efforts. Perhaps, however, they incur the wrath of grandioso and doloroso members who despair of their chattiness during lesson study and their disorganization during times of sowing and reaping. So, as you can see, an understanding of temperaments is essential throughout the church family, from superintendents to teachers, to members in the pews and classrooms.

To win and hold minority temperament types, David Farmer’s book, Power Witnessing: How to Witness to Different Personalities , may provide useful insights and direction.

And, of course, the classic studies on this subject are works by Ellen G. White: the two-volume Mind, Character, and Personality and the nine-volume Testimonies for the Church .

© 2014 General Conference of Seventh-day Adventists

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