Conformity in High School Adolescents: Development and Validation of the Informational and Normative Conformity Scale

Abstract

The present article describes the development and validation of the Informational and Normative Conformity Scale (SKI-N), a brief self-report tool capturing adolescents' general propensity to adopt a conformist attitude, and the underlying motives for doing so. The presentation includes a description of scale construction and an assessment of the psychometric properties. In two independent samples of adolescents (total N = 1,953), the SKI-N factorial structure was investigated, and the reliability and dimensionality, the multi-group measurement invariance, and the construct validity were each verified. The findings showed that the scale structure is bi-factorial, and the tool is reliable, valid, and invariant across gender. Therefore, the SKI-N can be applied in research and/or in psychological and educational practice to provide important information in a broader assessment of students’ psychosocial functioning in the school environment. Moreover, compared to currently available measures, it fills a gap in the tools for measuring conformity in the adolescent population.

Keywords

conformity adolescents peer relationships school environment scale construction psychometric properties factor structure measurement invariance

Introduction

During adolescence, young people experience significant transformations within identity, self-perception, and relationships with parents and peers (Branje et al., 2021). As a result of the ongoing separation-individuation process (Koepke & Denissen, 2012; Meeus et al., 2005), gaining and securing a position within a peer group begins to be fundamental to the identity development and psychosocial functioning of adolescents (see systematic reviews Mitic et al., 2021; Ragelienė, 2016).

The growing importance of peer relationships makes adolescents sensitive to peer influence, which increases and maintains peer similarity (Laursen & Veenstra, 2021). Similarity most often means following the same norms, which raises the chance of being accepted by peers (Costello, 2010), while dissimilarity is likely associated with rejection (Kaufman et al., 2022). Therefore, in adolescence, striving for satisfactory peer relationships and a fear of peer rejection are important factors activating specified personality structures related to the emotional-motivational sphere. Consequently, young people want to adjust to the group, so they observe and follow, sometimes unreflectively, what others do (Coultas & van Leeuwen, 2015), and they may become susceptible to borrowing someone else’s ways of thinking and acting (Kosten et al., 2013; Opozda-Suder et al., 2021). These tendencies correspond to the propensity to adopt a conformist attitude.

Conformity and Two Different Motivations

In the theoretical literature, conformity is defined as an element of personality structure associated with adopting an attitude of submissiveness and similarity in ways of thinking, behaviour, and emotional reactions to those generally accepted in the reference group (Popek, 2008). Additionally, as Cialdini and Trost (1998, p. 162) point out: ‘We conform to others when perceived or real pressure from them causes us to act differently from how we would act if alone’.

There are two different motives for conformist behaviours (Deutsch & Gerard, 1955). One of the motives is the fear of rejection in the event of non-adjustment to the norms and rules applicable in a given group. Thus, fear of rejection motivates normative conformity. It reflects an individual’s propensity to change their attitude under the influence of others because of their desire to be accepted and liked, and their reluctance to experience social disapproval (Campbell & Fairey, 1989; Coultas & van Leeuwen, 2015). The second motive is the uncertainty arising from the lack of information, which underlies informational conformity. This conformity is related to the propensity to perceive others as a valuable source of information. As a result, the person adjusts because they believe that someone else’s knowledge or interpretation of a new and/or unclear situation is better than their own (Sowden et al., 2018; Wice & Davidai, 2021).

Conformity in Psychosocial and Educational Development

It should be emphasised that conformity itself is neither positive nor negative in nature. However, depending on its intensity and the circumstances in which it occurs, it may have both a positive and negative impact on development. On the one hand, in line with the influence-compatibility model (Laursen & Veenstra, 2021), willing conformity could be an important factor for the positive functioning of adolescents in the school environment. As a way of fitting into the norms and rules of the group, it can influence the maintaining of good interpersonal relationships with classmates, protect against rejection and increase the likelihood of experiencing belonging (Coultas & van Leeuwen, 2015; Leary, 2022). Moreover, conformity helps students to improve their adaptation and motivation for learning, supports satisfaction and self-realisation, and fosters cooperation and harmony with classmates, all of which increase educational achievement (Jiang et al., 2015; Xu & Tu, 2022).

On the other hand, conformity can also block the reflective and individual self-creation of adolescents, who often reproduce patterns imposed by the group (Coultas & van Leeuwen, 2015; Haun & Tomasello, 2011). When young people identify with a group preferring negative norms and values, there is a risk of blocking positive psychosocial development, and consequently a risk of social maladjustment. This is confirmed by research in which conformity was identified as an important factor of the risk of deindividuation (Postmes & Spears, 1998), and contributed to the development of adolescent risky behaviour (Buist et al., 2004; Cakirpaloglu et al., 2021). For example, conformity was found to be a significant predictor of drug use, alcohol consumption and theft (Chassin et al., 2004; Laghi et al., 2019; Litt et al., 2012; Regnerus, 2002). Conformity is also associated with bullying and peer victimisation (Burns et al., 2008; Kaufman et al., 2022; Murphy et al., 2017). Moreover, in educational practice, a high propensity to conform may lead to situations in which adolescents, under group or results pressure (e.g., high-stakes testing), can be less reliant on their own opinions. As a result, they can follow the wrong decisions of the majority, which, in turn, reduces independence of thinking (Beghetto, 2017; Xu & Tu, 2022).

Current Ways of Measuring Conformity and Their Limitations

The interest in conformity in the social sciences is not declining (Coultas & van Leeuwen, 2015), and researchers are still searching for methods of its measurement. They use two approaches to assess individual differences in the level of conformity. The first approach is experimental, and involves creating a situation as comparable as possible to Asch’s (1956) original experiment (Bond & Smith, 1996; Haun & Tomasello, 2011; Kosloff et al., 2017). In this way, researchers try to determine the power of situational group pressure to alter an individual’s expressed beliefs. The second approach, adopted only in a very few studies, is based on the creation of self-report scales assessing conformity as an element of personality structure.

Unfortunately, some of these tools do not directly measure people’s propensity to conform. The Comrey Personality Scale (Comrey & Backer, 1970) includes a 5-item conformity subscale which focuses strictly on respect for the law and acceptance of the social order. Some researchers use the ‘Lie’ subscale from Eysenck’s Personality Questionnaire (Eysenck et al., 1985) to measure conformity (DeYoung et al., 2002). However, this subscale directly assesses only social desirability. In the Schwartz Value Survey (Schwartz, 1992), conformity is treated as a value that people can follow in their lives. The other scales – the Conformity Scales (Brügger et al., 2019; Mehrabian & Stefl, 1995) – although designed to measure conformity directly, are dedicated to the adult population, and marginalise the context of peer relationships. Moreover, these scales do not address motives underlying propensity to conform, and assessment of their psychometric properties is not multifaceted.

Aim of the Study

Recognising the gaps in the assessment of conformity, we developed an original scale to measure adolescents' general propensity to adopt a conformist attitude, and the underlying normative and informational motives. Inclusion of these motives is an innovative feature of our instrument. Additionally, emphasising the significance of conformity as an adjustment mechanism in adolescent psychosocial and educational development, we designed this tool to measure this concept strictly in the adolescent population. Most importantly, to the best of our knowledge, this is the only scale that measures conformity in the context of adolescent peer relationships.

Thus, the general aim of the article is to present the results of the rigorous assessment of an instrument we have named the Informational and Normative Conformity Scale (in Polish: Skala Konformizmu Informacyjnego i Normatywnego – SKI-N). This article presents a description of the construction of the scale and the analyses of the psychometric properties. Moreover, to aid understanding, the entire research procedure is graphically represented in a flow diagram (see Supplemental Material, Figure S1).

Scale Construction

In order to design, develop, and evaluate the psychometric properties of the SKI-N, we adopted the widely accepted criteria (Kaplan, 2018; Kline, 2005) suggested by the Standards for Educational and Psychological Testing (AERA et al., 2014), published jointly by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education.

Phase 1: Item Generation. The conceptual work on the SKI-N was preceded by a detailed literature review in order to identify empirical and definitional indicators of the conformist attitude, including informational and normative conformity. Based on this review, five main indicators were identified: (a) compliance and adjustment to others; (b) unreflective use of well-established patterns; (c) willingness to submit to anyone who seems to be an authority; (d) following the voice of the majority; (e) placing norms or opinions imposed by the group above one’s own (e.g., Cialdini & Goldstein, 2004; Cialdini & Trost, 1998). Two motives of a conformist attitude were also imposed onto the main indicators: (a) a motive related to normative conformity; and (b) a motive related to informational conformity. Furthermore, the content of all statements was set in the context of peer group influence.

Given the above theoretical framework of the scale and using deductive methods, 48 baseline items were prepared, 24 for each conformist motive respectively (to proportionally represent both of the defined dimensions of the measured construct). The items were formulated as affirmative sentences, to which the respondent selected answers on a 7-point scale (from 0 = never to 6 = always). For example: ‘In order to feel liked, I adapt to the group and don’t say what I really think’, and ‘When I am unsure of my own opinion, I value what others think more highly’.

Phase 2: Student and Expert Review. In the next step, test items were subjected to a face validity assessment based on focus interviews with six volunteer students (three young women and three young men aged 16–19 years old). Relying on their feedback, the wording of some items was modified. The formulation of the SKI-N’s items was completed by a Polish philologist, who revised the entire material in terms of linguistic correctness. Subsequently, based on nine expert judges’ assessment, the content validity (the extent to which individual items represent the measured trait) was verified and rated very highly. The level of concordance of judges' ratings (Kendall’s W) was moderate (Schmidt, 1997), and ranged from 0.52 to 0.65. As suggested by the expert judges, none of the 48 sentences were rejected. Therefore, the SKI-N composed of all the initially developed items was subjected to an assessment of psychometric properties.

Study 1

In Study 1, we aimed to assess the psychometric properties of the initial SKI-N version by the split-half cross-validation technique (Refaeilzadeh et al., 2009). Therefore, two subsets were created and used separately to conduct exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Based on the EFA, we investigated the scale structure, determined the optimal number of latent factors, and reduced the number of items. To cross-validate the SKI-N structure obtained from the EFA results, we conducted the CFA.

Method

Participants and Procedure

The participants were 968 Polish adolescents (61.1% young women) from 16 to 19 years old (M = 17.16; SD = 1.07). This sample size (N) was determined from the less restrictive of the common rules of thumb: that N ≥ 200 and that the ratio of N to the number of items should be at least 10:1. Therefore, such a size was deemed suitable for the EFA and CFA applications (Kyriazos, 2018).

The students were recruited from 10 public secondary-education schools, selected on the basis of convenience sampling, located in three provinces of southern Poland. The schools were appropriately diversified in terms of size (large and small) and location (suburban and urban areas). Using stratified random sampling, a total of six classes were selected from each school, and the strata were organised according to the age of the students.

The written consent of students and parents was obtained prior to the survey. The study was conducted with a computer assisted web interview (CAWI). The data were collected in the second half of October 2021. Due to the use of CAWI as a data collection technique, the database did not contain any missing data.

Statistical Analyses

The data collected in Study 1 were randomly split in two, using the sample.split function from the R package caTools version 1.18.2 (Tuszynski, 2021). Therefore, train (N = 486) and test (N = 482) subsets were created and used separately to conduct the EFA and the CFA. The modelling was performed using R package lavaan version 0.6–10 (Rosseel, 2012).

Exploratory Factor Analysis (EFA)

Due to the categorical response scale, the EFA was performed using a polychoric correlation matrix, weighted least squares with a mean- and variance-adjusted (WLSMV) estimator and the Oblimin rotation. The number of factors to retain was determined on the basis of Kaiser’s eigenvalue-greater-than-one rule, interpretation of the Cattell’s scree plot and parallel analysis (Schmitt, 2011).

Confirmatory Factor Analysis (CFA)

The CFA – similarly to the EFA – was performed on a polychoric correlation matrix using the WLSMV estimator. As part of the CFA, the bi-factor model was tested. Its use involves testing the assumption underlying the construction of the SKI-N: that there are three orthogonal factors in the latent structure of the scale, one of which is the general factor (the general propensity to adopt a conformist attitude), and the other two are subfactors (specific factors – reflecting normative and informational motives of the conformist attitude). It was assumed that the general factor – the primary construct measured by the SKI-N – loads on all items of the scale, and that each of the two subfactors loads on items assigned to a given subscale respectively.

Model Fit Criteria

The fit of the EFA and CFA models was assessed with three indices: (a) root mean square error of approximation (RMSEA), (b) comparative fit index (CFI), and (c) standardised root mean square residual (SRMR). The Tucker-Lewis index (TLI) was not reported because its value correlates almost perfectly with the CFI value (Garrido et al., 2016). It was assumed that values of RMSEA ≤ 0.05, SRMR < 0.08, CFI > 0.95 indicate a good fit of the model. The models with RMSEA > 0.05 and < 0.08 were considered as acceptably fitted (Schermelleh-Engel et al., 2003; Schreiber, 2017). At the same time, lower values of RMSEA and SRMR, and higher values of CFI, would suggest a better fit of the model to the data. For the RMSEA index, a 95% confidence interval (CI) was estimated and reported. Additionally, despite the fact that the χ² goodness-of-fit test is dependent on sample size and tends to underestimate the fit of complex models (Schermelleh-Engel et al., 2003), its outcomes were also presented.

Results

Exploratory Factor Analysis (EFA) – Train Data Subset

In the EFA, based on the size of the factors' eigenvalues, the Cattell’s scree plot and parallel analysis, we decided to test three-, four- and five-factor models. Tested solutions fitted the data well. The three- and four-factor models had slightly weaker parameters than the five-factor model (Table 1).

Table 1.

Measures of Models Fit to the Data in the EFA (Train Data) and CFA (Test Data).

MODEL	Chi-square Test of Model Fit		RMSEA	RMSEA95% CI	SRMR	CFI
MODEL	χ²	df	RMSEA	RMSEA95% CI	SRMR	CFI
EFA
Three-factor	2203.93^**	987	0.050	[0.048–0.053]	0.034	0.964
Four-factor	1843.88^**	942	0.044	[0.041–0.047]	0.030	0.974
Five-factor	1605.87^**	898	0.040	[0.037–0.043]	0.027	0.979
CFA
One-factor	728.64^**	90	0.121	[0.113–0.130]	0.055	0.949
Two-factor	305.94^**	89	0.071	[0.063–0.080]	0.034	0.983
Bi-factor	225.50^**	74	0.065	[0.056–0.075]	0.027	0.988

Note. ^** statistically significant (p < .001).

The analysis of the factor loadings indicated that, for the five-factor model, all items belonging to the fifth factor had values lower than 0.50. Therefore, this factor was uninterpretable, and the five-factor solution had to be rejected. For the same reason (loading values of the fourth factor lower than 0.50) the four-factor solution was rejected. The results of the three-factor solution revealed that 33 items should be removed (see Supplemental Material, Table S1). The reasons for their rejection were related to: a complexity coefficient > 1.1 (Burton & Mazerolle, 2011); loading values lower than 0.50 or a cross-loading issue (i.e., when an item has a loading above 0.50 on one factor and a substantive loading (> 0.30) on another factor) (McDonald, 1999; Reise et al., 2010); the fact that two items (I24 and I32), assigned by design to the informational motive (factor #2), were strongly loaded by the factor referring to the normative motive (factor #1). As a result, factor #3 was no longer present. Finally, we selected 15 items that strongly represent either of two factors, which was our goal. The loading values for these items ranged from 0.67 to 0.89.

Confirmatory Factor Analysis (CFA) – Test Data Subset

In the CFA, in addition to the assumed bi-factor model, both one- and two-factor models were tested as comparative models. The bi-factor CFA model was the best-fitted to the data (Table 1). This result is in line with the assumptions underlying the construction of the SKI-N.

Although acceptable, the RMSEA value of the bi-factor CFA model proved unsatisfactory (greater than 0.05). Therefore, striving to select only non-redundant items strongly belonging to the general factor and only one subfactor, the loading values and the modification indices were analysed. Based on the lowest loading values (λ < 0.25) for a given subfactor (see Supplemental Material, Table S2), we decided to remove four items (N48, N12, N1 and I10). Additionally, based on the values of modification indices > 5.0, two items (N19 and I16) were deleted (see Supplemental Material, Table S3). The decision as to which item of a given pair (N19∼∼N20 and I14∼∼I16) should be removed was resolved through team discussion, and the item that had better wording was selected. According to these results, the final version of the SKI-N, consisting of 9 items, was prepared.

Study 2

In Study 2, we focused on assessing the psychometric properties of the final SKI-N version. The complex assessment was based on the results of the following analyses: CFA, reliability and dimensionality, multi-group measurement invariance, and construct validity.

Method

Participants and Procedure

We applied the same research procedure as was performed in Study 1, but classes participating in Study 1 were excluded. The sample comprised 985 Polish adolescents from 16 to 19 years (M = 17.07; SD = 1.53; 61.0% young women). The minimum sample size (N) was determined from the more restrictive of the common rules of thumb: that is, that the ratio of N to the number of model parameters (q) should be at least 5:1. Based on the CFA results from Study 1, we knew that the bi-factor model would be the most complex of all tested solutions. For the 9-item SKI-N version, the model consisted of 72 parameters, thus the N:q ratio in this study exceeded 13:1. The data were collected at the end of November and the beginning of December 2021.

Measures

Informational and Normative Conformity Scale (SKI-N; designed by Opozda-Suder)

The total score of 9-item SKI-N indicates the level of the general propensity to adopt a conformist attitude. The subscales relate to two different motivations to conform: the KN subscale represents the motivation to meet the normative expectations of others (five items) and the KI subscale corresponds to the motivation to adopt others' opinions as a valid source of information (four items). Table S4 (Supplemental Material) includes items from the final version of the SKI-N (Polish with proposed English translation).

Moreover, the following self-report tools were used to assess the construct validity of the SKI-N: Generalised Self-Efficacy Scale (GSES; Schwarzer & Jerusalem, 1995); Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965); Multidimensional Self-Esteem Inventory (MSEI; O’Brien & Epstein, 1988) – ‘Likeability’ and ‘Personal Power’ subscales; Need to Belong Scale (NTBS; Leary et al., 2013); The De Jong Gierveld Loneliness Scale (DJGLS; de Jong-Gierveld & Kamphuls, 1985); The Creative Behaviour Questionnaire (CBQ III; Bernacka, 2009) – ‘Conformity-Nonconformity’ subscale.

Statistical Analyses

Confirmatory Factor Analysis (CFA)

Similarly to Study 1, CFA was conducted on a polychoric correlation matrix using the WLSMV estimator. The CFA aimed to confirm the bi-factor solution obtained in Study 1. One- and two-factor solutions were also tested as comparative models. The modelling was performed using R package lavaan version 0.6–10 (Rosseel, 2012).

Reliability and Dimensionality Analyses

The Cronbach’s alpha coefficient, a classic measure of internal consistency, was calculated using raw score. Furthermore, based on factor loadings of the bi-factor CFA model, omega (ω) coefficients (McDonald, 1999) were estimated: omega total (ω_T), omega hierarchical (ω_H), and omega hierarchical for subscales (ω_HS). The ω_T coefficient gives a reliability estimate of the overall variance of the total score that is due to both the general factor and subfactors (Kalkbrenner, 2021). The ω_H coefficient represents the strength of a general factor and estimates the proportion of total score variance explained by the general factor alone. In turn, omega hierarchical for subscales (ω_HS) refers to their reliability and reflects the percentage of variance in a subscale score that is explained solely by a given subfactor (after controlling for the variance accounted for by the general factor) (Reise, Bonifay, et al., 2013).

Additionally, ω_H and ω_HS were used to assess whether the subfactors contain enough information beyond the general factor to be interpreted as independent dimensions. The ω_H coefficient > 0.75 combined with ω_HS < 0.50 indicate that the primary source of information about the latent variable is the general factor (not subfactors) (Gignac & Kretzschmar, 2017). The explained common variance (ECV) coefficient (Reise, Scheines, et al., 2013) was also considered. The ECV is the percentage of the model’s common variance attributed to a given factor. An ECV of the general factor > 0.75 indicates the presence of one general dimension in the model and the low importance of the subfactors (Reise, Bonifay, et al., 2013).

In order to verify whether the results of the subscales (KN and KI) show a sufficient degree of distinctiveness, that is, ‘unique information for score interpretation not captured by the total score’ (Paap et al., 2021, p. 570), the value-added ratio (VAR; Haberman, 2008) was calculated. As recommended by Feinberg and Jurich (2017), when VAR ≤ 0.90, the subscore is deemed not useful because the observed subscores explain less variance in the true subscores than the observed total score. Reliability and dimensionality analyses were performed using R packages Bifactor Indices Calculator (Dueber, 2017) and subscore version 3.3 (Dai et al., 2022).

Additionally, we used item response theory (IRT) to plot a test information curve and explore the SKI-N local reliability (measurement precision) across the latent trait continuum (various levels of theta). The curve was derived from the amount of the Fisher information: that is, the reciprocal of the square root of the posterior standard deviation of the estimated score (posterior mean) (Asparouhov & Muthén, 2016). The reliability was calculated using the formula proposed by Reise (Reise & Haviland, 2005).

Multi-group Measurement Invariance Analyses

The next step in the analysis involved the verification of the SKI-N measurement invariance across gender. Based on the theta parameterisation, three sequential levels of measurement invariance – configural, scalar, and strict – were tested, respectively. The metric level was omitted due to ordered polytomous categorical variables used and the adaptation of the bi-factor CFA model, in which a factor indicator loads on more than one factor (Muthén & Muthén, 2019).

In order to check whether the constraints imposed at each level significantly impair the model’s fit to the data in relation to the less restricted model, the difference (Δ) of the values of χ², RMSEA, and CFI were calculated. It was restrictively assumed that measurement invariance would be confirmed when ΔRMSEA ≤ 0.007 and ΔCFI ≥ −0.002 (Meade et al., 2008). Additionally, a nonsignificant result of the DIFFTEST was expected for the Δχ². However, this particular criterion alone was not decisive in the non-invariance detection, since χ² in larger samples is overly sensitive to small, unimportant deviations from a ‘good’ model (Chen, 2007).

When full measurement invariance was not achieved at a given level, partial measurement invariance was examined. This testing involved relaxing some equality constraints on the measurement parameters in one of the groups (Millsap & Kwok, 2004). We used information from modification indices to identify potentially non-invariant items. The modelling was performed using Mplus 8.3 software (Muthén & Muthén, 2019).

Construct Validity

The construct validity of the SKI-N was investigated by testing its associations with related constructs. We chose conceptually accurate variables, which, like conformity, are crucial for the psychosocial functioning of adolescents. Based on the literature on conformity and related topics, we expected that conformity measured by the SKI-N would positively correlate with need to belong (Bică, 2022; Williams et al., 2000) and with loneliness (Mehrabian & Stefl, 1995), but negatively with self-efficacy and self-esteem (Enjaian et al., 2017; Tainaka et al., 2014), with creative (non-conformist) style and approach to problem-solving (Beghetto, 2017; Xu & Tu, 2022), and with likeability (popularity) and personal power (leadership skills) (Leary, 2005; Robinson et al., 2015). Correlations between the SKI-N result and the results from other tools (calculated based on mean scores for observed variable), were assessed using the r-Pearson correlation coefficient with 95% confidence interval (CI). Correlation coefficients were calculated using R package apaTables version 2.0.8 (Stanley, 2021).

Results

Confirmatory Factor Analysis (CFA)

Among all tested solutions the one-factor model was rejected as a poor approximation of the data. The two-factor model was found to have better fit than the one-factor model. However, the values of fit indices in the bi-factor solution clearly indicated that this model is the best-fitted to the data. This means that the structure of the SKI-N is best represented by a single, general latent factor and two (orthogonal) subfactors. The results for the analysed solutions are shown in Table 2 and Figure 1.

Table 2.

Model Fit Indices for Three Solutions in the CFA.

MODEL	Chi-square Test of Model Fit		RMSEA	RMSEA95% CI	SRMR	CFI
MODEL	χ²	df	RMSEA	RMSEA95% CI	SRMR	CFI
One-factor	652.509**	27	0.153	[0.143–0.164]	0.042	0.928
Two-factor	323.365**	132	0.081	[0.070–0.092]	0.033	0.981
Bi-factor	34.771*	18	0.031	[0.015–0.046]	0.013	0.998

Note. * statistically significant (p < .01); ** statistically significant (p < .001).

Figure 1.

Schematic of the Two-factor and Bi-factor CFA Models. Note. GEN = general factor; KN = normative motive of conformity, KI = informational motive of conformity.

Reliability and Dimensionality Analyses

The SKI-N should be considered a reliable tool. The Cronbach’s alpha was 0.88 for the total score, 0.82 for subscale KN and 0.82 for subscale KI. Furthermore, ω_T = 0.90 can be considered high (Kalkbrenner, 2021). The ω_H coefficient was 0.79, which indicates that almost 80% of the total score variance is explained by the general factor alone. The ω_HS coefficient for the KN and KI subscales was 0.14 and 0.20, respectively. Therefore, the values of ω_H and ω_HS taken together suggest that the subfactors cannot be interpreted as independent dimensions.

The ECV for the general factor was 0.76 (for the KN and KI subfactors it was 0.11 and 0.12, respectively). This value indicates that the scale structure is predominantly reflected by the general dimension, despite using a multidimensional item pool. Low ECV values for both subfactors show that they explain a trivial amount of variance. Moreover, a low VAR of 0.90 suggests reporting the total score instead of the subscales scores.

Additionally, the test information curve demonstrated that the SKI-N allows precise measurement of the general propensity to adopt a conformist attitude across a wide range of the continuum (see Supplemental Material, Figure S2). Between the theta values of −2.2 and 4.5, the latent trait is measured with a reliability above 0.80, and above 0.90 for theta from −1.0 to 3.7.

Multi-group Measurement Invariance Analyses

The verification of the M-G MI started with the analysis of baseline models, separate for young women (G1) and young men (G2). In both groups, the bi-factor CFA model was well fitted to the data (G1: RMSEA = 0.043 [95% CI: 0.024–0.062], SRMR = 0.012, CFI = 0.997; G2: RMSEA = 0.038 [95% CI: 0.00–0.064], SRMR = 0.014, CFI = 0.997). Therefore, the analysis of the M-G MI proceeded. The fit indices of the M-G MI models are shown in Table 3.

Table 3.

Model Fit Indices for the Multi-group Measurement Invariance (Across Gender).

MODEL	Chi-Square Test^a				RMSEA	Δ RMSEA	CFI	Δ CFI
MODEL	χ²	df	Δ χ²	Δ df	RMSEA	Δ RMSEA	CFI	Δ CFI
Configural^b	65.663	36	−	−	0.041	−	0.997	−
Scalar^c	132.559	94	20.303*	15	0.029	−0.012	0.996	−0.001
Strict^d	187.927	105	37.469	12	0.040	0.011	0.991	−0.005
Partial Strict^e	156.539	103	21.046	10	0.031	0.002	0.994	−0.002

Note. Δ = the difference of the values of χ², RMSEA and CFI in relation to the less restricted model; * statistically significant (p < .05).

^aThe Δχ² and Δdf calculated with the DIFFTEST procedure from the Mplus package.

^bFactor loadings and thresholds freely estimated across groups, residual and factor variances fixed at 1.0 in both groups, and factor means fixed at zero in both groups as well.

^cFactor loadings and thresholds equal across groups, residual and factor variances fixed at 1.0 in one group and free in the second group, and factor means fixed at zero in one group and free in the other group.

^dJust as in the scalar model, additionally, residual and factor variances fixed at 1.0 across the groups.

^eJust as in the strict model, additionally, residual variances of items N28 and I14 free in one group.

The configural invariance model provided a good fit to the data. Consequently, in the next step, we carried out testing of scalar invariance. The scalar model also fitted the data well. Compared to the configural model, the deterioration of the fit of the scalar model met the assumptions for the adopted limit values.

The strict invariance model failed to fit the data well and showed a significant decrement in the fit of this model, compared with the scalar model. Therefore, a partial strict invariance model was tested. Based on the modification indices, we sequentially freed the residual variances for items I14 and N28. This partial strict model, specified in such a way, fitted the data well – an insignificant DIFFTEST result was obtained (p = 0.076). The Δ values for the fit indices of this model and scalar model fell within the acceptable range. This confirmed that partial strict invariance of the SKI-N emerges across gender: that is, young men have larger residual variances than young women in two of the nine items.

Construct Validity

The construct validity assessment involved measuring the relations of the SKI-N with other instruments that tested related constructs. The r-Pearson correlation coefficients were calculated between the observed results of the scale (and its subscales) and the other tools used (see Table S5, Supplemental Material).

The SKI-N results positively correlated with the NTBS (need to belong) and the DJGLS (loneliness). Although the correlation coefficients were weak (0.31 and 0.32, respectively) they were statistically significant (p < 0.01). Moreover, the SKI-N was significantly and negatively correlated with the GSES (self-efficacy), the RSES (self-esteem), the CBQ III (creative style of problem-solving), and two subscales of the MSEI (self-esteem components: ‘Likeability’ and ‘Personal Power’). The correlation coefficients ranged from −0.21 to −0.44 (p < 0.01). Similar correlation coefficient results were obtained for each SKI-N subscale.

Discussion

The aim of this research was to develop and rigorously validate the Informational and Normative Conformity Scale (SKI-N). The conception of this scale is based on the assumption that conformity is a mechanism of development in adolescence that regulates relations within the peer group.

The results of our two studies, based on large, independent adolescent samples, demonstrated that the SKI-N structure is bi-factorial. This finding means that the general factor of the SKI-N represents the level of general propensity to adopt a conformist attitude, which (as expected) is itself composed of two subfactors reflecting informational and normative motives.

The obtained Cronbach’s alpha coefficient and omega coefficients supported the conclusion that the reliability of the SKI-N and its subscales is very good. The test information curve showed that the scale is particularly precise for average and high scores and the measurement is less precise only for adolescents with a very low level of conformity. Furthermore, the omega hierarchical, ECV and VAR values indicated that the general factor is strong and two subfactors are trivial. Therefore, when interpreting the results of the scale for a given individual, only the total score should be used.

However, although the subfactors had low independence from the general factor, the subscores can still be used as sums or means – for example, in regression analyses, as two independent but correlated variables (Dueber & Toland, 2021). Such a possibility is particularly useful when someone is interested in what type of motive more strongly predicts the other variables. In these analyses, however, attention should be paid to the collinearity and potential redundancy of both subdimensions (DeMars, 2013).

Furthermore, our results revealed full scalar and partial strict measurement invariance across gender. The strict invariance is partial because residual variances for two of nine items are not invariant across groups (larger for young men than for young women). Therefore, caution is required when performing a gender comparison of observed (but not latent) mean scores. However, because the proportion of non-invariant parameters is relatively small, these parameters do not heavily affect the result of the group comparisons. Moreover, full strict invariance in statistical practice is rarely achieved (Vandenberg & Lance, 2000; Wu et al., 2007).

We also considered the construct validity of the SKI-N to be satisfactory. In accordance with our expectations the SKI-N manifested significant correlations with all tools measuring related constructs, that is, need to belong, loneliness, self-efficacy, self-esteem, creative (non-conformist) style and approach to problem-solving, likeability (popularity), personal power (leadership skills). These results could be explained by the fact that conformity may serve as a form of psychological defence that plays a role in maintaining positivity about the self. In other words, the results confirm that conformity is a strategy to supply a universal need for belonging and acceptance (Bică, 2022; Enjaian et al., 2017) and satisfy the desire to be accurate in one’s opinions and beliefs (Arndt et al., 2002).

Limitations and Future Research

We are aware that the research has some limitations. Primarily, because the study design was cross-sectional and the outcomes were not examined over time, further research should verify the longitudinal measurement invariance of the SKI-N (including test-retest reliability). To establish the measurement stability of the scale to short-term fluctuations, which are not the result of changes in the level of conformity, tests should be re-administered periodically after short intervals (for example, after one, two, and four weeks). Moreover, it is also necessary to verify the multi-group measurement invariance across all phases of adolescence.

It would also be useful to enhance the accuracy and utility of the scale by determining the cut-off points of its score. This issue is important because it is not possible to determine a priori the desirable level of conformity for an adolescent. However, we assume the possibility of determining two optimal cut-off points of the SKI-N, such that properly selected analyses should indicate the scale values that provide an assessment of beneficial and detrimental levels of conformity. Scores between determined cut-off points would identify a beneficial level of conformity that is of adaptive significance. Contrarily, scores lower than the first cut-off point would indicate a detrimental level of conformity, which leads to a lack of integration with the group and peer rejection. Scores higher than the second cut-off point would also indicate a detrimental level of conformity, associated with the risk of losing individual identity in favour of group identity. For such scores above this value, there is also a risk of non-adaptive behaviours and problems in the school environment.

The above limitations suggest several ways of supplementing the assessment of the psychometric properties of the SKI-N. Moreover, future studies are needed to implement the scale, particularly using an accelerated longitudinal design (Galbraith et al., 2017), beginning with tests among youth in early adolescence up to late adolescence. Thereby, investigators could study how the level of the general propensity to adopt a conformist attitude changes as the importance of peer relationships increases. This would also allow for a partial verification of the previous research results demonstrating an inverted, U-shaped developmental trend of susceptibility to conformity. This shape reflects the level of conformity, which increases before peaking in early adolescence, and then declines again (see short research review by Zhang et al., 2017).

We also see the need to undertake research from the perspective of intracultural and intercultural comparisons, because theories of conformity emphasise the importance of cultural values in shaping people’s responses to social pressure (Bond, 2004). In the intracultural context, the goal is to investigate individual differences in the level of conformity, depending on selected sociodemographic and economic variables, within the given society (Sibilsky et al., 2021). On the other hand, the intercultural context concerns the study of patterns of conformity in various ethnic groups or countries, which may differ in their particular levels of collectivism. Such analyses seem especially relevant as previous cross-cultural comparisons have yielded a mixed set of findings (see meta-analysis by Bond & Smith, 1996).

Despite the indicated limitations, the SKI-N can be easily used in research and/or in psychological and educational practice to provide important information in a larger evaluation of students’ psychosocial functioning in the school environment. This is because, as mentioned in the Introduction, conformity in adolescence is significantly associated with the quality of relationships with classmates and the effectiveness of the learning process. Moreover, school is an important environment that should be conducive to satisfying the basic developmental needs of adolescents. On the other hand, when the fulfilment of these needs is threatened, the propensity to adopt a conformist attitude may occur (Arndt et al., 2002; Griskevicius et al., 2006). Therefore, the SKI-N can be helpful in identifying the relationship between conformity and individuals' basic needs (e.g., need to belong and for acceptance, safety needs, self-esteem needs), which, if unmet, are possible drivers of conformity.

Knowledge of the level of conformity combined with other variables relevant to the psychosocial functioning of adolescents can be used to determine important areas of student development that need support or change in the school setting. However, recommendations on how to work with students with different levels of conformity, respecting the individual’s basic needs, are of interest for future research.

Conclusion

The current research is an important scientific contribution to extending the possibility to measure conformity in the adolescent population in the school environment. The obtained results showed that SKI-N has sound psychometric properties. The scale is reliable, valid, and invariant across gender and, as a brief instrument, it may be effective in research among youth who are often reluctant to engage in lengthy assessments.

Supplemental Material

Supplemental material for Conformity in High School Adolescents: Development and Validation of the Informational and Normative Conformity Scale

Supplemental material for Conformity in High School Adolescents: Development and Validation of the Informational and Normative Conformity Scale by Sylwia Opozda-Suder, Paweł Grygiel, and Kinga Karteczka-Świętek in Journal of Psychoeducational Assessment

Footnotes

Author's Note

The de-identified databases of this research and all statistical models’ syntaxes are available from the corresponding author on request. The original SKI-N Polish questionnaire is also available on request from the corresponding author for non-commercial use.

Ethical Approval

The Research Ethics Committee of the Faculty of Philosophy at the Jagiellonian University approved the protocol of this research on October 12, 2021 (No. 221.0032.3.2021).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was conducted as a research project financed by the National Science Centre, Poland (No. 2019/03/X/HS6/00273). The professional English-language editing of the article was funded under the program Grants Supporting Publications ‘Excellence Initiative – Research University’ at the Jagiellonian University in Krakow (No. 221.6120.57.2022). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

ORCID iDs

Sylwia Opozda-Suder

Pawel Grygiel

Kinga Karteczka-Świętek

Supplemental Material

Supplemental material for this article is available online.

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