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0UDC 159.9.072.59
doi : 10.23951/2782-2575-2024-1-62-74
M. SNYDER'S SELF-MONITORING SCALE: SHORT VERSION, RELIABILITY, VALIDITY, FACTOR STRUCTURE
1 2 Viktor P. Sheinov , Anton S. Dziavitsyn
1 Republican Institute of Higher School, Minsk, Belarus, ORCID 0000-0002-2191-646X, sheinovl@mail.ru
2 Belarusian State University, Minsk, Belarus, a.dziavitsyn@gmail.com
Abstract. The widespread use of smartphones and social networks has profoundly affected the quality of communication and led to changes in its characteristics, including those measured by the Self-Monitoring Scale. Examining these changes is crucial, especially in the context of the evolving landscape of digital communication. Although the Self-Monitoring Scale was introduced 40 years ago by M. Snyder, it remains a staple of research, demonstrating its enduring applicability. However, there is a growing consensus in the psychological community, both domestically and internationally, that the established methods need to be updated. The reason for this is the potential discrepancy between the responses of today's respondents and those of people from decades past for whom these scales were developed initially. The changing communication context requires re-evaluating these tools to ensure that they remain relevant and reflect current societal dynamics. The aim of the study to reduce the number of questions in the Scale is also important, as large questionnaires cause difficulties in collecting material and (as relevant offline and online studies have shown) lead to poorer quality responses.
Aims of the research: 1) Development of a reliable and valid short version of the Self-Monitoring Scale by M. Snyder; 2) Construction of meaningful models for the Self-Monitoring Scale. The empirical basis of the study was the results of online tests with 1911 respondents from Belarus and Russia, including 1206 women and 605 men. The study was based on the classic test by M. Snyder Self-Monitoring Scale, questionnaires on smartphone addiction (author - V.P. Sheinov), addiction to social networks (authors - V.P. Sheinov, A.S. Dziavitsyn) and the Academic Motivation Scale questionnaire by Vallerand (adapted to the Russian-speaking society by T.O. Gordeeva, O.A. Sychev and E.N. Osin) was also used. Statistical analysis was performed using the SPSS-22 package and the R-based Jamovi version 2.3.21. As a result of this study, a valid and reliable short version of the Self-Monitoring Scale questionnaire was created, consisting of 8 questions on self-monitoring, with better psychometric properties than the original version created by M. Snyder. A rich two-factor model of the Self-Monitoring Scale was developed. The short Self-Monitoring Scale allows you to collect larger samples with better-quality responses.
Keywords: Self-Monitoring Scale, M. Snyder, short version of the Self-Monitoring Scale, reliability, validity, factor structure, psychometric properties, smartphone addiction, social media addiction, academic motivation scales
For citation: Sheinov V.P., Dziavitsyn A.S. M. Snyder's Self-Monitoring Scale: short version, reliability, validity, factor structure. Education & Pedagogy Journal. 2024;1(9):62-74. doi: 10.23951/2782-2575-2024-1-62-74
Introduction
In recent years, the scientific community has realized that revising questionnaires developed some time ago is required. The reason for this is the well-founded concern that the current generation differs considerably from the respondents for whom these instruments were invented many years ago and that their responses might vary substantially from those of their long-time predecessors.
This applies to a large extent to the questionnaires used to diagnose communication behavior, as the amount of time respondents now spend on social networks and communicating via smartphones harms the quality of face-to-face communication. "Too much communication via mobile phones makes people less sensitive to each other, leading to a loss of empathy and a deterioration in emotional connection with others" [1, p. 120].
Smartphone addiction is positively related to lower self-esteem and self-control, as well as the risk of being a victim of cyberbullying [2, p. 235]. A positive correlation was found between social network addiction and anxiety, stress, neuroticism, low self-esteem, cyber victimization, and loneliness [3, p. 607].
Low self-esteem, anxiety, and low levels of self-esteem and self-control are likely precursors to victimization [4, p. 154].
The Self-Monitoring Scale, a construct developed by M. Snyder [5], includes various factors that influence a person's adaptability in social interactions. Snyder assumes that a person's self-monitoring capacity is shaped by the type of information they use to modulate their behavior. Individuals with a high self-monitoring capacity use cues about the situational appropriateness of their behavior, while individuals with a low self-monitoring capacity are more likely to be influenced by their inner states, attitudes, and dispositions. The Self-Monitoring Scale thus measures a person's ability to regulate their behavior and emotional expressions and adapt their appearance to different social contexts based on their perception of social norms.
High self-monitors are characterized by paying attention to the social appropriateness of their actions, being sensitive to the expressive behavior of others, and using these observations to guide their own behavior. These people are adept at directing their expressions to make the desired impression on others. Conversely, low self-monitors exhibit a higher degree of spontaneity and authenticity. They maintain a consistent self-identity in different contexts and show less behavioral variability in response to different social environments.
The Self-Monitoring Scale questionnaire [6] was developed to assess the personal characteristics crucial for social adaptability. The Scale applies to both genders, adults and adolescents, and assesses respondents' tendency to focus on their own feelings, emotions, and states or those of their interlocutors during interactions.
The Self-Monitoring Scale quantifies the extent to which individuals can modulate their behavior to influence the perceptions of others. Self-monitoring
focuses on the two processes of self-regulation and self-observation, which aim to bring one's behavior into line with social norms and expectations.
M. Snyder originally developed the Scale with 25 items, which was later refined to a version with 18 items [6]. This condensed version was favored due to its improved psychometric properties and was the preferred instrument in self-monitoring research for an extended period. However, the results are predominantly from non-Russian cohorts, which led to the question of how the Scale's factor structure might manifest itself in Russian-speaking participants.
Recent trends in research emphasize the tendency to minimize the length of questionnaires. There is evidence that extensive surveys make data collection more complex and can affect the quality of responses. This phenomenon has been observed in traditional [7] and digital [8] formats. With this in mind, this study aims to develop a concise adaptation of the Self-Monitoring Scale tailored to Russian speakers. The objectives include determining the reliability and validity of the short version and describing the Scale's factor structure for the Russian population.
Materials and research methods
Study participants and data collection. The empirical basis of the study was the results of online tests with 1911 subjects from Belarus and Russia (mean age M = 19.4, SD = 5.6, including 1206 women (M = 19.7, SD = 6.0) and 605 men (M = 19.1, SD = 4.7).
Methods: The development was based on the 'Self-Monitoring Scale' by M. Snyder [9, p. 558-559].
In the study, a questionnaire on smartphone addiction (author -V.P. Sheinov) [10], a questionnaire on addiction to social networks (authors: V.P. Sheinov, A.S. Dziavitsyn) [11], the questionnaire "Academic Motivation Scale" by Vallerand (adapted to Russian speakers by T.O. Gordeeva, O.A. Sychev and E.N. Osin) [12] was used.
The statistical analysis was performed using the SPSS-22 package and the R-based package jamovi version 2.3.21. The significance level was assumed to be p = 0.05.
Results and discussion
The homogeneity of the original version of the Self-Monitoring Scale by M. Snyder proved to be relatively low: Cronbach's alpha is 0.619 (see Table 1).
The corresponding SPSS-22 program for improving homogeneity suggests deleting items no. 5 and no. 7.
Table 1
Cronbach 's alpha of the Self-Monitoring Scale after removal of items no. 5 and no. 7 (women and men, N = 1911)
Original version value After items were removed
no. 5 no. 7
Cronbach's alpha 0.619 0.679 0.701
Table 1 shows that the homogeneity of the questionnaire has improved, as evidenced by the increase in Cronbach's alpha from 0.619 to 0.701, i.e., that the recommended indicator (in all manuals) of no less than 0.7 has been achieved.
The discriminatory power of the Self-Monitoring Scale was assessed by examining its correlation with the results of the administered questionnaire. These results are shown in Table 2.
Table 2
Pearson's r and Kendall's t correlation coefficients between the results of M. Snyder's Self-Monitoring Scale and the total score, broken down by gender, for a sample size of 1911 participants
1 2 3 4 5 6 7 8 9 10
r 0.409** 0.459** 0.468** 0.334** 0.296** 0.405** 0.245** 0.418** 0.364** 0.435**
p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
t 0.358** 0.404** 0.412** 0.282** 0.255** 0.344** 0.203** 0.361** 0.314** 0.374**
p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Note. Designations in this and all following tables: * -p < 0.05; ** -p < 0.01.
Table 2 shows the questionnaire items with the lowest discrimination indicators (i.e., r and t below 0.3).
The situation is similar for the male and female subsamples. This shows that items no. 5 and no. 7 of the questionnaire have the lowest discrimination score.
Thus, if we remove these items from the questionnaire, we improve its psychometric properties - both the homogeneity of the questionnaire and the overall discriminatory ability of its items.
Let's check the results using factor analysis.
Factor Analysis of the Self-Monitoring Scale
For the analysis, the results of tests with 1911 respondents were obtained using a short version of Snyder's questionnaire. The factor analysis was performed with the R-based statistical package jamovi, version 2.3.21.
Exploratory Factor Analysis
In the analysis, factor extraction was performed using the principal axis factoring method with oblique rotation (Oblimin).
In this method, the factors are extracted from an original correlation matrix, with the squares of the multiple correlation coefficients on the diagonal serving as initial estimates of the commonalities (factor loadings). These factor loadings are then used to estimate new commonalities to replace the old commonality values on the diagonal. The extraction is continued iteratively until the changes in commonalities from one iteration to the next fulfill the convergence criterion.
The extraction method was preferred to the principal component analysis method for the following reasons:
1. In our case, the data have dichotomous values and, consequently, high measurement errors due to binary sampling. Principal component analysis (PCA) is ineffective for highly discrete values, as the method is preferred for integer or real scales and assumes that all measurements are accurate. The Principal axis factoring (PAF) method, on the other hand, copes better with data that contain measurement errors.
2. Principal component analysis (PCA) assumes that the data is normally distributed and has linear relationships. If these assumptions are violated, as in this case, the Principal axis factoring (PAF) method provides better results.
3. Principal axis factoring (PAF) is better suited to data in which latent variables are present and in which many concepts (such as 'acting,' as used below) cannot be measured directly with the original data.
During the study, the possibility of removing some questionnaire items to increase the homogeneity indicator, measured by Cronbach's alpha coefficient, was investigated. Several models were considered in the analysis, and the best model in terms of statistical indicators was selected, confirming the appropriateness of removing items no.5 and no.7 from the questionnaire.
Removed questions RMSEA
All questions ,0240
No. 5 removed ,0220
No. 7 removed ,0280
No. 5 and no.7 removed ,0215
This resulted in a factor model with the following factor weights (larger values are highlighted in bold):
Variable Factor 1 Factor 2
6 0.550
10 0.465 0.113
8 0.378
4 0.169
9 0.160
3 0.442
2 0.103 0.364
1 0.359
Bartlett's criterion
x2 df P
496 28 < ,001
RMSEA 90% CI
RMSEA Min Max
0.0215 0.00678 0.0346
The values of the RMSEA criterion between 0.01 and 0.05 show a good fit of the tested model to the empirical data.
It makes sense to interpret the resulting factors as follows:
Factor 1 (Other-Directedness):
6. I often behave entirely differently in different situations and when communicating with different people.
10. I am not always what I appear to be.
8. To succeed in business and in relationships with other people, I try to be what is expected of me.
4. Others sometimes think I feel something deeper than I really do.
9. I can be friendly to people I can not stand.
Factor 2 (Acting):
3. I could be a good actor.
2. I could probably play the fool to attract attention or amuse others.
1. The art of imitating other people's habits is difficult for me.
The names of the factors are taken from the article [13], which previously examined the 3-factor structure of the full version of the Snyder questionnaire, which consisted of 18 items.
In the 1980s, factor analysis showed that the Self-Monitoring Scale measured several components of self-monitoring in communication [10]. At the same time, there were debates: does the Scale measure a single construct or a complex of several interrelated phenomena? [14].
The factor analysis of the Self-Monitoring Scale, which consists of 18 questions, revealed three factors: Acting, Extroversion, and Other-directedness. Acting involves the ability to talk and entertain; Other-directedness is the willingness to change one's behavior according to other people's demands; extroversion is the tendency to be sociable. Other-directedness correlates positively with shyness and neuroticism and negatively with self-esteem. Extraversion correlates negatively with shyness and positively with self-esteem and sociability. Therefore, two of the three factors on the Scale are inversely related to other personality dimensions. These three factors help explain some discrepancies found in previous studies on self-control scales. For future research, it is suggested that the ratings of the individual factors are more appropriate than the ratings of the entire Scale [13].
This shortened questionnaire with 8 items was derived from a short version with 10 items and, therefore, does not include the third factor mentioned above. The highest possible psychometric indicators could be achieved by shortening the questionnaire (in addition to improving usability) - the best heterogeneity and distinctiveness of the tasks.
Confirmatory factor analysis
To confirm the obtained factor models, a confirmatory factor analysis was conducted.
Model readings:_
x2 df P
44,6 19 < 0.001
RMSEA 90% CI
RMSEA Lower Upper
0.0268 0.0166 0.0371
The values of the specified criteria confirm a good fit of the tested model to the empirical data.
Checking the validity of the short version of the questionnaire
Let's check if there are correlations between the Self-Monitoring Scale and personality traits that are of great importance today - smartphone addiction and addiction to social networks. This is shown in the research results below.
A number of foreign publications show positive correlations between the Self-Monitoring Scale, smartphone addiction [15-18], and social network addiction [19-23]. The correlations between the short version of the Self-Monitoring Scale, smartphone addiction, and social network addiction are shown in the following tables, which were calculated for all the samples we considered.
Table 3
Correlation of the Self-Monitoring Scale with smartphone addiction and addiction to social networks (women and men, N = 826)
Correlation Smartphone addiction Social networks addiction
Pearson Value 0.172** 0.105**
Significance 0.000 0.003
Kendall Value 0.152** 0.103**
Significance 0.000 0.000
Women generally suffer more from smartphone addiction and addiction to social networks. Therefore, we test construct validity using a general sample of women and men and separately for each gender.
Table 4
Correlation of the Self-Monitoring Scale with smartphone addiction and addiction to social networks (women, N = 530)
Correlation Smartphone addiction Social networks addiction
Pearson Value 0.140** 0.076
Significance 0.001 0.082
Kendall Value 0.134** 0.079*
Significance 0.000 0.012
Table 5 Correlation of the Self-Monitoring Scale with smartphone addiction and addiction to social networks (men, N = 296)
Correlation Smartphone addiction Social networks addiction
Pearson Value 0.153** 0.055
Significance 0.009 0.348
Kendall Value 0.119** 0.081*
Significance 0.005 0.046
Tables 4 and 5 show that the relationship between the Self-Monitoring Scale and smartphone addiction and social network addiction is consistently positive, while the relationships with smartphone addiction are linear and the
relationships with social media addiction are non-linear. The latter is due to the fact that Pearson correlations depict linear relationships, and Kendall correlations depict non-linear relationships.
This proves the validity of the Self-Monitoring Scale questionnaire in relation to smartphone addiction and social network addiction.
The results of foreign studies have shown that the Self-Monitoring Scale is also positively related to motivation [24-28].
The following Tables 6-8 show the correlations of the short version of the Self-Monitoring Scale with motivation. As there is no information on the prevalence of the corresponding traits in men and women, the correlations were calculated for both groups.
Table 6
Correlation of the Self-Monitoring Scale with Motivation (women and men,
N = 826)
Correlation Introjected motivation External motivation
Pearson Value 0.149** 0.197**
Significance 0.000 0.000
Kendall Value 0.116** 0.149**
Significance 0.000 0.000
Table 7 Correlation of the Self-Monitoring Scale with Motivation (women N = 530)
Correlation Introjected motivation External motivation
Pearson Value 0.125** 0.171**
Significance 0.004 0.000
Kendall Value 0.111** 0.141**
Significance 0.001 0.000
Table 8 Correlation of the Self-Monitoring Scale with Motivation (men, N = 296)
Correlation Introjected motivation External motivation
Pearson Value 0.137* 0.232**
Significance 0.018 0.000
Kendall Value 0.105* 0.178**
Significance 0.015 0.000
The validity of the Self-Monitoring Scale questionnaire was therefore also demonstrated with regard to its relationship to motivation.
The validity of the Self-Monitoring Scale questionnaire was therefore demonstrated overall, taking into account all the variables considered.
Reliability of the short version of the Self-Monitoring Scale questionnaire
The reliability of the Self-Monitoring Scale questionnaire was tested using the following criteria: 1) internal consistency (homogeneity), 2) discriminatory power, and 3) repeated testing (retest).
The Cronbach's alpha coefficient quantifies the homogeneity of the questionnaires. This amounted to 0.701, i.e., it reached the recommended indicator of at least 0.7.
This procedure also increased the discrimination points of the questionnaire as a whole, as the removed items no. 5 and no. 7 had the lowest indicators, according to both Pearson and Kendall. After their removal, only items whose discriminatory power was well above 0.3 remained in the questionnaire.
Thus, the psychometric indicators of the version of the Self-Monitoring Scale questionnaire reduced to 8 items proved to be better than the original version. Please note that the aim of creating a shorter version of the Self-Monitoring Scale questionnaire is to obtain a more practical instrument and improve its psychometric properties: the internal consistency (homogeneity) of the questionnaire and the discriminatory power of all its items.
The reliability of the short version of the Self-Monitoring Scale questionnaire was verified by repeated testing at 4-week intervals. Since we had the contacts of the respondents who had answered the questions in the original version of the questionnaire, we asked them to answer the questionnaire again in its shortened version. 223 respondents took part in the repeat survey. The correlation between the first and second tests is 0.877. This result indicates a good retest reliability of the questionnaire, as an indicator of more than 0.7 serves as proof of reliability for this criterion.
The reliability and validity of the short version of the Self-Monitoring Scale questionnaire is also extremely high, with a correlation coefficient of
0.957.(p < 0.001) between the overall indicators of the original questionnaire and the short version of the social self-monitoring questionnaire.
Conclusion
The results of our study confirm the effectiveness of the revised, concise version of the Self-Monitoring Scale questionnaire. This streamlined version maintains the basic standards of validity and reliability and outperforms the original in terms of psychometric properties. The practical value of this study lies in the introduction of a more user-friendly Self-Monitoring Scale instrument. This advance facilitates research efforts by providing an efficient and accessible means of measuring self-monitoring, thereby improving the ease and effectiveness of data collection in the psychological assessment of the quality of communication.
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Information about the authors:
Victor P. Sheinov, Doctor of Sociological Sciences, Professor; Professor of the Department of Psychology and Pedagogical Excellence. Republican Institute of Higher School (st. Moskovskaya, 15, 220001, Minsk, Belarus). ORCID 0000-0002-2191-646X E-mail: sheinov1@mail.ru
Anton S. Dziavitsyn, Senior Lecturer at the Department of Web Technologies and Computer Modeling, Belarusian State University (Independence Ave., 4, 220030, Minsk, Belarus).
E-mail: a.dziavitsyn@gmail.com
Е<1исаИоп & Pedagogy 4оыгпа1. 2024. 1 (9)
ОПРОСНИК САМОМОНИТОРИНГА ОБЩЕНИЯ М. СНАЙДЕРА: КОРОТКАЯ ВЕРСИЯ, НАДЕЖНОСТЬ, ВАЛИДНОСТЬ, ФАКТОРНАЯ СТРУКТУРА
1 2 Виктор Павлович Шейнов , Антон Сергеевич Девицын
1 Республиканский институт высшей школы, Минск, Беларусь, ОЯСЮ 0000-0002-2191-646Х, sheinov1@mail.ru
2 Белорусский государственный университет, Минск, Беларусь, a.dziavitsyn@gmail.com
Аннотация. Активное использование смартфонов и социальных сетей существенно влияет на качество общения. Меняются его характеристики, в частности, такой его признак, как самомониторинг общения, изучение которого весьма актуально. При этом в исследованиях активно используется созданная 40 лет назад М. Снайдером «Шкала самомониторинга общения». В отношении давно разработанных методик в психологической науке (у нас и за рубежом) сложилось убеждение, что их нужно перепроверить, поскольку ответы респондентов в настоящее время могут значительно отличаться от ответов их давних предшественников, на ответах которых много лет назад создавались эти опросники. Актуальна и установка на сокращение опросников, поскольку большие опросники вызывают трудности в сборе материалов и (как показали соответствующие исследования в офлайне и в онлайне) дают худшее качество получаемых ответов. Цели данного исследования: 1) разработка надежной и валидной короткой версии опросника «Самомониторинг общения»; 2) построение состоятельной факторной модели самомониторинга общения. Эмпирической основой исследования послужили результаты онлайн-тестирования 1911 испытуемых из Беларуси и России, в том числе 1206 женщин и 605 мужчин. В основу разработки положен классический тест М. Снайдера «Самомониторинг общения», использованы опросники зависимости от смартфона (автор - В.П. Шейнов), зависимости от социальных сетей (авторы - В.П. Шейнов, А.С. Девицын), опросник «Шкалы академической мотивации» Валлеранда (в адаптации к русскоязычному социуму Т.О. Гордеевой, О.А. Сычева и Е.Н. Осина). Статистический анализ проведен с помощью программ пакета SPSS-22 и пакета jamovi версии 2.3.21 на базе R. В результате данного исследования сконструирована состоящая из 8 вопросов валидная и надежная короткая версия опросника «Самомониторинг общения», обладающая лучшими психометрическими характеристиками, нежели его исходная версия М. Снайдера. Построена состоятельная двухфакторная модели самомониторинга общения. Короткая версия опросника «Самомониторинг общения» позволяет собирать выборки большего объема при лучшем качестве ответов.
Ключевые слова: самомониторинг общения, опросник М. Снайдера, короткая версия опросника, надежность, валидность, факторная структура, психометрические характеристики, зависимость от
смартфона, зависимость от социальных сетей, Шкалы академической мотивации
Для цитирования: Sheinov V.P., Dziavitsyn A.S. M. Snyder's Self-Monitoring Scale: short version, reliability, validity, factor structure // Education & Pedagogy Journal. 2024. Вып. 1 (9). Р. 63-75. doi: 10.23951/2782-25752024-1-63-75
Информация об авторах:
Шейнов Виктор Павлович, доктор социологических наук, профессор; профессор кафедры психологии и педагогического мастерства, Республиканский институт высшей школы (ул. Московская, 15, Минск, Беларусь, 220001). ORCID 0000-0002-2191- 646X E-mail: sheinov1@mail.ru
Девицын Антон Сергеевич, старший преподаватель кафедры веб-технологий и компьютерного моделирования, Белорусский государственный университет (пр. Независимости, 4, Минск, Беларусь, 220030). E-mail: a.dziavitsyn@gmail.com
Submitted December 14, 2023
