Day-to-day associations between school-based ethnic discrimination and school disengagement: Investigating the role of psychological need frustration as mediating pathways
Creators: name: Branje, Susan
name: Civitillo, Sauro
name: Vietze, Jana
name: Thijs, Jochem
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Social and Behavioral Sciences

description: Study Information
1. Title (required)

Day-to-day associations between school-based ethnic discrimination and school disengagement: Investigating the role of psychological need frustration as mediating pathways 

2. Authors (required)

Omitted for peer review process

3. Description (optional)

Racism and discrimination are integral aspects of the lives of racial-ethnic minority adolescents around the world. The majority of them report encountering discriminatory experiences on a daily basis (Yip, 2018) across various public spaces (Ortega‐Williams et al., 2022) including within the school ecology (Steketee et al., 2021). These experiences might have an impact on school disengagement, defined as withdrawal from involvement in the learning process and school activities (Skinner et al., 2019). School disengagement represents an indicator of academic underachievement with lasting negative consequences for students, regardless of their background (Fredricks et al., 2004). However, the bulk of research focusing on ethnic discrimination and school disengagement relies on cross-sectional data; how daily experiences of ethnic discrimination at school are associated with more immediate changes in school disengagement is less clear. This lack of knowledge is particularly significant during late adolescence, a developmental period marked by an increased awareness of racism and concurrent decline in school engagement (Hughes et al., 2016). 
This study focuses on school-based racial-ethnic discrimination, broadly defined as detrimental treatment due to racial-ethnic group membership from peers and teachers experienced at school, a key context for adolescents’ development (Wong et al., 2003). Previous experience sampling methodology studies (e.g., daily diaries) found that experiencing everyday discrimination has detrimental, short-term consequences on psychological adjustment, including depressive symptoms, anxiety, and positive and negative mood (Bámaca et al., 2022; Douglass et al., 2016; Seaton &amp; Douglass, 2014; Seaton &amp; Iida, 2019; Wang &amp; Yip, 2020). Less is known about the negative effects of school-based racial-ethnic discrimination on day-to-day academic outcomes.


Although changes in academic outcomes are most often captured at the between-person level (e.g., changes in the mathematics grade mean of the sample) and on a monthly or yearly basis, academic experiences (e.g., motivation, engagement) are likely to vary from day-to-day at the within-person level (Martin et al., 2015; Patall et al.; 2018). The salience of individual experiences within a given day, such as encountering discrimination at school, and the way these events are perceived and processed through the student’s perspective and orientation, may account for the variability in school disengagement. Nevertheless, it is plausible that a student who is repeatedly exposed to discrimination experiences is singularly disengaged for weeks or even throughout the school year. The goal of the current study is to investigate the extent to which daily school-based racial-ethnic discrimination has short-term consequences (i.e., same-day and next-day) on school disengagement at the within-person level, and over a two-week period at the between-person level. Moreover, we will target theoretically plausible psychological mechanisms that might explain the link between school-based ethnic discrimination and school disengagement: frustration of the basic psychological needs for relatedness, competence, and autonomy. 

4. Hypotheses (required)

Hypothesis 1a: We expect that on days when students report school-based racial-ethnic discrimination, regardless of the discriminatory form and source, they would also report a greater increase in behavioral, emotional, and cognitive disengagement the same day and the next day. Hypothesis 1b: We expect this effect to be stronger for emotional and cognitive disengagement than for behavioral engagement. Hypothesis 1c: We hypothesize that students who report school-based racial-ethnic discrimination, regardless of the discriminatory form and source, would also report a greater increase in behavioral, emotional, and cognitive disengagement across the study period. Hypothesis 1d: We expect this effect to be stronger for behavioral disengagement than on emotional and cognitive engagement.

Hypothesis 2: We expect school-based racial and ethnic discrimination to be associated with higher frustration of competence, relatedness, and autonomy, (same day/next day) which in turn leads to increased levels of behavioural, emotional, and cognitive school disengagement. We predict that need frustration would fully meditate the associations between school-based racial and ethnic discrimination and all dimensions of school engagement on both, the within-person level (on the same day) and the between-person level (across the study period). 

Hypothesis 3: We expect that there would be larger indirect effects when teachers are the perpetrator of discrimination compared to peers (H3a). Regarding the forms of discrimination (overt vs. covert), we will investigate associations in a more explorative fashion to provide insight into the different manifestations of school-based racial and ethnic discrimination commonly occurring on a daily level in the school ecology (H3b).

Design Plan

5. Study type (required)

Observational study.

6. Blinding (required for intervention studies)

No blinding is involved in this study.

7. Is there any additional blinding in this study?

No.

8. Study design (required)

Within-between-participants design.


Sampling Plan

9. Existing data (required)

Registration prior to the creation of data. 

10. Explanation of existing data (optional)

N/A

11. ESM data collection procedure (required)
11.1. Study duration (number of days).

The ESM period will last for 10 days.

11.2. Type of sampling scheme:

Participants will fill out once a day (i.e., 29 questions) every day from Monday to Friday between 4.00 pm and 10.00 pm over a 2-week period. 

11.3. Total number and type of items (open-ended or closed) and estimated questionnaire duration.

Eleven items will be measuring school-based ethnic discrimination (three items for open discrimination by peers: e.g., ‘Today in school another student called me names or teased me because of my ethnic origin’; Four items for subtle discrimination by peers: e.g., ‘Today in school another student ignored me because of my ethnic origin’; and four items for teacher-based discrimination: e.g., ‘Today in school the teacher picked me less often because of my ethnic origin’). The response scale for all discrimination-related items ranged from: 0 (did not happen), 1 (happened once), to 2 (happened two or more times).

Nine items will be measuring daily school disengagement: Three items for behavioural disengagement items will be adapted from a scale by Amemiya, Fine, and Wang (2020, e.g., ‘Today I did not follow the rules’). For emotional disengagement, three items from a scale by Wang et al. (2017) will be adapted (e.g., ‘Today in school I felt worried’). Cognitive disengagement will be measured with three items taken from Wang et al. (2017): ‘e.g., Today when I didn’t understand a task, I gave up right away’). The response scale ranged from 1 (not at all) to 5 (completely).


The 9-item version of the need frustration scale/questionnaire will be administered (Neubauer et al., 2022). Three items will be measuring frustration of the psychological needs for autonomy (e.g., “Today in school I had to do a lot of things I did not want to do”), competence (e.g., “Today in school I had the feeling that I am not good at any thing”), and relatedness (e.g., “I had the feeling that other people do not really like me”). This scale showed good psychometric properties in a prior daily diary study conducted with young adolescents.

An overview of the proposed scales (in English and in Dutch) measuring the main variables of the current project is reported on the OSF project page.

11.4. Time-out specifications.

To prompt diary completion, participants will be notified to fill out daily entries (i.e., 29 questions) via a smartphone application every day at 4.00 pm. If a response is not recorded after the signal, a reminder will be sent in the next time frame (i.e., 6 pm, and 9 pm), for a total of 2 daily reminders. Participants have time until 10.00 pm to fill out daily entries. 

11.5. Additional passive monitoring.

N/A

11.6. Incentivization and increasing participant engagement.

Participants will receive a 25 Euro voucher, as compensation for their time spent participating in the study. An additional voucher to the value of 15 Euro will be given to participants who complete more than 70% of the assessments (1 baseline measurement, and eight daily surveys).

11.7. Please indicate any additional details that are relevant to the ESM data collection procedure.

Participants will complete a baseline survey in which information regarding age, gender, family’s country of origin, cultural self-identification, generation status, school grade, school type (vocational or academic track), and perceived school ethnic composition will be collected. The baseline measurements will also retrospectively assess school-based racial and ethnic discrimination, basic psychological needs, and school disengagement in the previous 30 days. Finally, participants will get familiar with the smartphone application and how to respond to the items.

For the duration of the study, participants will complete diaries via smartphones through the M-Path experience sampling application (KU Leuven, Belgium). The M-Path application allows to check whether indeed daily entries are completed each day at the predetermined moment of the day, providing a time-date stamp on the moment of completion. 


Two recruitment strategies will be implemented. First, recruitment of participants will occur in all types of secondary schools (i.e., vocational, and academic track) in different parts of the Netherlands. Second, recruitment of participants will occur in youth clubs (e.g., sport organizations, religious associations). 

 
12. Sample size (number of participants) (required)

We aim to include a total of 200 participants in our sample. The aim is to sample primarily adolescents of Turkish and Arab descent (16 to 18 years old), attending all types of secondary schools in the Netherlands. 

13. Rationale for sample size: Temporal design and number of participants (if applicable)

Sample size will be not determined in an a-priori fashion because power analysis in a multilevel setting would require knowledge of (or good estimates for) a multitude of (co-)variances on both the within-and the between-person level (Bolger, Stadler, &amp; Laurenceau, 2012). Regarding Level-1 sample size, we will follow participants for 10 days. A 2-week period is the most common sampling period in studies assessing the consequences of daily ethnic discrimination during adolescence (Civitillo &amp; Jugert, 2023). A 2-week period is not only common but also sufficient to capture the experiences of interest (i.e., daily discrimination and daily school disengagement). This period also accounts for attrition and the potential burden associated with an excessive number of observations. With regard to Level-2 sample size, we will aim to recruit 200 participants.

14. Stopping rule (if applicable)

We will stop when we reach the number of participants N = 200, or when funding ends on October 1st, 2024. If we do not reach at least 150 participants, we will ask for an extension of the funding to obtain as many participants as possible up to 150. 
Variables

15. Measured non-ESM/time invariant variables (if applicable)

Ethnic self-identification. Next to family’s country of origin, we will assess participants’ cultural self-identification with one open item (Leszczensky &amp; Santiago 2015), namely ‘To which culture do you feel primarily connected? For example: Your parents are Turkish, but you are currently in The Netherlands. Do you feel mostly Turkish, or Dutch, or both? ’. 
 
Generational Status. Participants will be asked to report their country of birth, as well as that of their parents. We will then create two groups of students: (1) first generation (i.e., the participant and parents were born outside of the Netherlands) and (2) second generation (i.e., the participant was born in the Netherlands, but at least one parent was born outside of the Netherlands).
 
Family Cultural Capital. As an indicator of objectified family cultural capital, we will use one item adapted from Sieben and Lechner (2019), addressing the number of printed books at home (1 = 10 books or less to 5 = more than 200 books).

16. Measured ESM/time-variant variables (required)
 
 See point 11.3

17. Open-ended questions (if applicable)

N/A

18. Indices (if applicable)

N/A

19. Manipulated variables (if applicable)


 N/A 
Prior knowledge of the data (if applicable)


20. List the publications, conference presentations (papers, posters), and working papers (in prep, unpublished, preprints) you have worked on that are based on the data set. Describe which variables you have previously analyzed and which information you used in these analyses. Limit yourself to variables that are relevant to the current study. If the dataset is longitudinal, include information about what wave of data was previously analyzed

N/A

21. What prior knowledge do you have at the time of pre-registration about trends in the data set you will be working with?

N/A


Analysis Plan

22. Statistical models (required)


The reliability of the scales measuring ethnic discrimination and school engagement will be examined by running multilevel confirmatory factor analysis (MCFA; Muthén, 1994). MCFA allows the calculation of factor loadings for the within-subjects variation in a set of items that have been repeatedly measured. This preliminary step is crucial to verify whether the scales are reliably enough to capture within- and between-person variability (Shrout &amp; Lane, 2012). 

To account for the nested nature of data — Level 1 daily observations in Level 2 participants — multilevel models will be conducted. We will use a multi-level structural equation modelling (ML-SEM) because of its superiority especially when examining indirect effects (Preacher, Zhang, &amp; Zyphur, 2011). This approach allows to separate within- and between-level components. Random slopes will be estimated for the within-person effects. To test Hypotheses 1, two models will be estimated, one for the same day effect of school-based discrimination on school disengagement and one for the next day effect. The three indicators of school-based racial and ethnic discrimination will be added as predictors. To enhance model convergence, each dimension of school disengagement will be tested as an outcome separately. To test Hypothesis 2, the model will be extended by including direct paths from school-based racial and ethnic discrimination to three mediators (need frustrations), and from three mediators to each dimension of school disengagement. 





The regression model with one predictor (all sources and types of school-based racial and ethnic discrimination combined), three parallel mediators, and one outcome will be estimated on both levels. Mediation hypotheses will be tested via inspection of the statistical significance of the indirect effects (these will be obtained via the MODEL INDIRECT command in Mplus). At the student level (i.e., Level 2), we will control for baseline levels of discrimination, psychological need frustration, disengagement, age, gender (0 = female, 1 = male, 2 = non-binary), ethnicity (1 = Turkish, 0 = Arab descent), generational status (1 = first generation, 0 = second generation), and family cultural capital. Model fit will be evaluated via the root means square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean squared residual (SRMR). Preliminary analyses (i.e., MCFA) will be performed in R using lavaan (Rosseel, 2012; 0.6-5), and the ML-SEM in Mplus (version 8.6, Muthén &amp; Muthén, 2021). 

23. What will you do should your data violate assumptions, your model not converge or some other analytic problem arises?


If the estimation procedure outputs a non-convergence message, the random effect structure will be simplified by dropping the random slope. This means that the model will only include a random intercept.

 
24. Transformations (if applicable)

We will conduct additional sensitivity analyses, using dichotomous indicators of school-based racial-ethnic discrimination. More specifically, all eleven variables measuring school-based racial and ethnic discrimination will be first recoded into a dichotomous indicator (i.e., 0 = no daily discrimination, 1 = at least one daily discrimination). Subsequently, we will repeat the exact same analyses to check whether the direction or the significance of the results change. 

25. Inference criteria (if applicable)

N/A

26. Missing data (if applicable)
26.1. Expectations of missingness.

Consistent with prior research on racial and ethnic discrimination using daily diaries among adolescents (e.g., Seaton &amp; Ilda, 2019), we anticipate some degree of missingness on study and daily variables. Missing data will be accounted for by Full Information Maximum Likelihood estimation. 

Due to the absence of measurements on weekends and thus to account for unequal time intervals, days will be coded based on their actual differences. For example, the first Friday will be coded as Day 5, and the second Monday as Day 8. The Kalman filter approach, as implemented by default in Mplus (Hamaker &amp; Grasman, 2012), will be employed to address for unequal time. In the first step, the Kalman filter predicts the next observation by leveraging all available information up to the current observation. In the second step, this prediction is compared with the actual observation, and, in the third step, the prediction is updated in the light of the actual observation. In instances where no observation is present, the Kalman filter persists with the last available prediction and updates it with the subsequent observation.
 

27. Data exclusion (if applicable)


If students withdraw their consent during or after its completion but before the start of the analyses, responses will be excluded from the analyses. 
 
27.1.1. How will you define compliance? E.g., Will notifications missing due to technical problems be counted as non-compliance? Will you use information regarding valid responses to determine compliance? In case you set a minimum compliance rate or threshold for each participant, please specify the value or range that you will use in the analysis and provide a justification for this.

For each participant, the compliance rate will be defined as the number of non-missing questions over the 10 days of ESM testing. 

Participants who report that their data is missing for technical reasons will be treated as follows:
- Participants who report a technical problem and miss more than one full day of data will be included in all analyses until the day in which the technical problem occurred. 
- Participants who have missing data for any other reasons (e.g. illness) will be included in all analyses.

27.2. Which criteria will you use to define an outlier? How will outliers be handled? How are you going to deal with careless responding?

N/A

28. Exploratory analysis (if applicable)


Other

29. Other (if applicable)
29.1. Which software is used to estimate the statistical model(s)? Specify whether the default options or specific options of functions are used.

All the analyses will be conducted in R and Mplus 8.9 (see point 22 for the specifics). 

29.2. If there is any additional information that you feel needs to be included in your pre-registration, please enter it here, e.g. literature cited within your (pre-)registration.











References

Amemiya, J., Fine, A., &amp; Wang, M. T. (2020). Trust and discipline: Adolescents’ institutional and teacher trust predict classroom behavioral engagement following teacher discipline. Child Development, 91(2), 661-678.

Hamaker, E. L., &amp; Grasman, R. P. P. P. (2012). Regime switching state-space models applied to psychological processes: Handling missing data and making inferences. Psychometrika, 77, 400-422.

 Leszczensky, L., &amp; Santiago, A. G. (2015). The development and test of a measure of youth’s ethnic and national identity. Methods, Data, Analyses, 9(1), 87-110.

Muthén, L. K., &amp; Muthén, B. O. (1998–2021). Mplus user's guide. Muthén &amp; Muthén.

Preacher, K. J., Zhang, Z., &amp; Zyphur, M. J. (2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural equation modeling, 18(2), 161-182.

Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5 – 12 (BETA). Journal of Statistical Software, 48, 1-36.

Seaton, E. K., &amp; Iida, M. (2019). Racial discrimination and racial identity: Daily moderation among Black youth. American Psychologist, 74, 117.

Sieben, S., &amp; Lechner, C. M. (2019). Measuring cultural capital through the number of books in the household. Measurement Instruments for the Social Sciences, 1(1), 1-6.

Wang, M. T., Fredricks, J., Ye, F., Hofkens, T., &amp; Linn, J. S. (2017). Conceptualization and assessment of adolescents’ engagement and disengagement in school. European Journal of Psychological Assessment.