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Survey data underlying the publication: Alleviating the Impact of Crisis-related Demands on Financial Stress and Work-life Balance of Entrepreneurs
No full textMethod: Procedure and Participants Data were collected among Dutch entrepreneurs owning small or medium-sized companies as part of a larger research project. We included entrepreneurs that owned a private company employing less than 250 employees (cf. Jayasekara et al., 2020). After providing informed consent, respondents filled out a general questionnaire, followed by a weekly questionnaire sent out on Thursday afternoons with a reminder e-mail on Monday mornings for four consecutive weeks. To capture the consequences of a crisis for entrepreneurs, we collected data during the (full) lockdown period of autumn October 2020 till spring February 2021 in the Netherlands. This lockdown meant a limited number of people were allowed to get together, only necessary traveling was allowed, and working from home was the standard. Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 91 participants signed up for the study by filling out the general questionnaire, of which 75 finished one or more of the weekly questionnaires resulting in a final sample of N = 229 weeks (M = 3.0 per participant). The sample included 43 men (51.8 %) and 40 women (48.2%), with a mean age of 53 years (SD = 11.2). The average age of their business was 13.3 years (SD = 10.7). Most participants were active in business services (31%), the culture and leisure sector (15%), and health and social work (10%). MeasuresCrisis-related demands and financial resources were measured on the between-level because these variables do not typically change on a weekly basis., The remainder of the study variables was measured on the within-level. We used validated translations of items of existing Dutch scales that were rewritten to apply to entrepreneursGeneral Questionnaire: Crisis-related demands were measured based on the report of the Rijksoverheid (2020). We started with asking the question: “Did the following things change in comparison to the period before the COVID-19 pandemic?” Participants had to answer this question for the number of assignments on a slider scale ranging from -100% (complete decrease) to +100% (complete increase). Next, we asked the question: “In general, to what extent have the government's COVID-19 measures influenced your work?” that could be answered on a scale from 1 (not at all) to 4 (completely). Furthermore, the average number of working hours per week during the COVID-19 pandemic was subtracted from the average number of working hours per week before the COVID-19 pandemic and participants were asked whether they have had to close their business. So, in total we got four indicators change in (1) assignments, and (2) working hours, (3) closed business, and (4) general influence of governmental COVID-19 measures. The Cronbach’s alpha of this scale was 0.74 see Table 1 for more information).Financial resources of entrepreneurs were based on the general financial security of entrepreneurs (Nibud, 2019). We used 3 items asking for having insurances (i.e., professional insurance and/or liability insurance, disability insurance, life insurance) and 3 items for putting money aside (VAT, financial buffer, private bank account). All items could be answered with 0 (no) or 1 (yes). An example item is: “I set aside my money for: the VAT I have to pay and/or the income tax I have to pay”. As the answering categories of the items were dichotomous, we used the tetrachoric correlation matrix from the item response theory to determine the factors (IRT factor analysis). Cronbach’s alpha was 0.62 (see Table 2 for more information).Weekly Questionnaire The weekly survey included shortened scales to minimize the time needed to complete them. All responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the current week. Weekly Financial Stress was measured with the five-item scale developed by Dijk et al., (2022) (e.g., “I wondered all the time if I have enough money.”). Cronbach’s α ranged between .95 and .97 (M = .96).Weekly Work-life Balance was measured with the four-item scale from Brough et al., (2014) (e.g., “I had a good balance between the time I spend at work and the time I have available for private activities.”). Cronbach’s α ranged between .88 and .94 (M = .92).Weekly Leisure Crafting was measured with the four-item scale of Petrou and Bakker, (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α ranged between .83 and .90 (M = .86).Strategy of Analysis The current study had a two-level design, with weeks (N = 229) nested within individuals (N = 75)
Survey data underlying the publication: Crafting through financially demanding times
No full textMethod Participants and Procedures Data were collected among Dutch entrepreneurs who own private companies and who employ less than 250 employees (cf. Jayasekara et al., 2020). After completing the informed consent, respondents filled out a general questionnaire, followed by a weekly questionnaire sent out on Thursday afternoon with a reminder e-mail on Monday morning for four consecutive weeks. Due to its enormous societal impact, a so-called ‘COVID-19 lockdown period’ (October 2020 till February 2021) was chosen to collect data because more entrepreneurs experienced financial demands like income loss (cf. Torrès et al., 2022). During this lockdown period, the Dutch government implemented restrictions concerning traveling (i.e., not allowed to travel unless absolutely necessary) and meeting other people (i.e., not allowed to meet in groups and mandatory self-isolation if a person tested positive for the COVID-19 virus). Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 91 entrepreneurs signed up for the study and received a weekly invitation to participate in the dairy study. Of these N = 91, N = 69 entrepreneurs finished one or more of the weekly surveys (75.8%), resulting in N = 189 weekly inputs (M = 3.0 per participant). The sample included 32 men (46.4 %) and 37 women (53.6%). Their mean age was 54 years (SD = 11.38). The average age of their business was 13.9 years (SD = 11.42), and most participants were active in business services (31.7%), the culture and leisure sector (16.4%), and health and social work (11.6%). Weekly Survey For all constructs, we used shortened scales to minimize the time needed to fill out the weekly surveys (cf. Ohly et al., 2010). All responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the current week. All variables were adapted to the week-level, in Dutch, and rewritten to apply to entrepreneurs. Weekly income loss was measured by asking the entrepreneurs to compare their current weekly income to a workweek before the Covid-19 pandemic. We asked the participants to rate their income loss on a scale of -100 (complete loss of income) to +100 (complete gain of income). Weeks with income gain (rating > 0) were excluded in this study. This led to the exclusion of N = 6 entrepreneurs and N = 40 weeks. Weekly business crafting was measured with adapted job crafting scales by Demerouti and Peeters (2018) and Petrou et al., (2012). In the process of scale adaptations, we interviewed (N = 5) and consulted (N = 4) entrepreneurs from different industries to make sure the items were written in the “language” of entrepreneurs. We selected three items for each business crafting strategy (Table 1). For increasing resources (e.g., “I have tried to learn new things for my business”), Cronbach’s α ranged from .76 to .86 (M = .81). For increasing challenging demands (e.g., “I have tried new approaches”), Cronbach’s α ranged from .80 to .87 (M = .82). For optimizing demands (e.g., “I look for ways to do my work more efficiently”), Cronbach’s α ranged from .89 to .96 (M = .93). Weekly leisure crafting was measured with the four-item scale of Petrou and Bakker (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α ranged from .83 to .90 (M = .86).Weekly well-being was distinguished in motivation and fatigue severity and measured using shortened subscales of the Checklist Individual Strength (CIS; Bültmann et al., 2000). We used four items to measure motivation (e.g., “I was full of plans”). Cronbach’s α ranged from .71 to .87 (M = .81). Another four items were used to measure fatigue severity (e.g., “I felt tired”), Cronbach’s α ranged from .91 to .94 (M = .93). Weekly goal attainment was measured with three items from Grebner et al. (2010) (e.g., “I completed my tasks”). Cronbach’s α ranged from .84 to .89 (M = .87). Strategy of Analysis We expected differences within entrepreneurs because some weeks may be more demanding than others. Therefore, we used multilevel analyses to test all hypotheses. Within-person analyses compare the differences within one individual (e.g., differences in weekly behavior). Between-person analyses compare the differences between different individuals (e.g., differences between the behavior of different entrepreneurs). We analyzed the data using lmer in RStudio. In multilevel analysis, the intraclass correlation (ICC) decomposes the variance in two components (variance at the within-person level and at the between-person level). There were medium amounts of variance to be explained by between-person variations (between .50-.75; Hox & Maas, 2006), justifying the multilevel approach. The week-level predictor variables were centered around the person-mean for the within-person analyses. For the between-level analyses, the variables were centered around the grand mean
Survey data underlying the publication: Crafting through financially demanding times
No full textMethod Participants and Procedures Data were collected among Dutch entrepreneurs who own private companies and who employ less than 250 employees (cf. Jayasekara et al., 2020). After completing the informed consent, respondents filled out a general questionnaire, followed by a weekly questionnaire sent out on Thursday afternoon with a reminder e-mail on Monday morning for four consecutive weeks. Due to its enormous societal impact, a so-called ‘COVID-19 lockdown period’ (October 2020 till February 2021) was chosen to collect data because more entrepreneurs experienced financial demands like income loss (cf. Torrès et al., 2022). During this lockdown period, the Dutch government implemented restrictions concerning traveling (i.e., not allowed to travel unless absolutely necessary) and meeting other people (i.e., not allowed to meet in groups and mandatory self-isolation if a person tested positive for the COVID-19 virus). Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 91 entrepreneurs signed up for the study and received a weekly invitation to participate in the dairy study. Of these N = 91, N = 69 entrepreneurs finished one or more of the weekly surveys (75.8%), resulting in N = 189 weekly inputs (M = 3.0 per participant). The sample included 32 men (46.4 %) and 37 women (53.6%). Their mean age was 54 years (SD = 11.38). The average age of their business was 13.9 years (SD = 11.42), and most participants were active in business services (31.7%), the culture and leisure sector (16.4%), and health and social work (11.6%). Weekly Survey For all constructs, we used shortened scales to minimize the time needed to fill out the weekly surveys (cf. Ohly et al., 2010). All responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the current week. All variables were adapted to the week-level, in Dutch, and rewritten to apply to entrepreneurs. Weekly income loss was measured by asking the entrepreneurs to compare their current weekly income to a workweek before the Covid-19 pandemic. We asked the participants to rate their income loss on a scale of -100 (complete loss of income) to +100 (complete gain of income). Weeks with income gain (rating > 0) were excluded in this study. This led to the exclusion of N = 6 entrepreneurs and N = 40 weeks. Weekly business crafting was measured with adapted job crafting scales by Demerouti and Peeters (2018) and Petrou et al., (2012). In the process of scale adaptations, we interviewed (N = 5) and consulted (N = 4) entrepreneurs from different industries to make sure the items were written in the “language” of entrepreneurs. We selected three items for each business crafting strategy (Table 1). For increasing resources (e.g., “I have tried to learn new things for my business”), Cronbach’s α ranged from .76 to .86 (M = .81). For increasing challenging demands (e.g., “I have tried new approaches”), Cronbach’s α ranged from .80 to .87 (M = .82). For optimizing demands (e.g., “I look for ways to do my work more efficiently”), Cronbach’s α ranged from .89 to .96 (M = .93). Weekly leisure crafting was measured with the four-item scale of Petrou and Bakker (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α ranged from .83 to .90 (M = .86).Weekly well-being was distinguished in motivation and fatigue severity and measured using shortened subscales of the Checklist Individual Strength (CIS; Bültmann et al., 2000). We used four items to measure motivation (e.g., “I was full of plans”). Cronbach’s α ranged from .71 to .87 (M = .81). Another four items were used to measure fatigue severity (e.g., “I felt tired”), Cronbach’s α ranged from .91 to .94 (M = .93). Weekly goal attainment was measured with three items from Grebner et al. (2010) (e.g., “I completed my tasks”). Cronbach’s α ranged from .84 to .89 (M = .87). Strategy of Analysis We expected differences within entrepreneurs because some weeks may be more demanding than others. Therefore, we used multilevel analyses to test all hypotheses. Within-person analyses compare the differences within one individual (e.g., differences in weekly behavior). Between-person analyses compare the differences between different individuals (e.g., differences between the behavior of different entrepreneurs). We analyzed the data using lmer in RStudio. In multilevel analysis, the intraclass correlation (ICC) decomposes the variance in two components (variance at the within-person level and at the between-person level). There were medium amounts of variance to be explained by between-person variations (between .50-.75; Hox & Maas, 2006), justifying the multilevel approach. The week-level predictor variables were centered around the person-mean for the within-person analyses. For the between-level analyses, the variables were centered around the grand mean
Survey data underlying the publication: Alleviating the Impact of Crisis-related Demands on Financial Stress and Work-life Balance of Entrepreneurs
No full textMethod: Procedure and Participants Data were collected among Dutch entrepreneurs owning small or medium-sized companies as part of a larger research project. We included entrepreneurs that owned a private company employing less than 250 employees (cf. Jayasekara et al., 2020). After providing informed consent, respondents filled out a general questionnaire, followed by a weekly questionnaire sent out on Thursday afternoons with a reminder e-mail on Monday mornings for four consecutive weeks. To capture the consequences of a crisis for entrepreneurs, we collected data during the (full) lockdown period of autumn October 2020 till spring February 2021 in the Netherlands. This lockdown meant a limited number of people were allowed to get together, only necessary traveling was allowed, and working from home was the standard. Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 91 participants signed up for the study by filling out the general questionnaire, of which 75 finished one or more of the weekly questionnaires resulting in a final sample of N = 229 weeks (M = 3.0 per participant). The sample included 43 men (51.8 %) and 40 women (48.2%), with a mean age of 53 years (SD = 11.2). The average age of their business was 13.3 years (SD = 10.7). Most participants were active in business services (31%), the culture and leisure sector (15%), and health and social work (10%). MeasuresCrisis-related demands and financial resources were measured on the between-level because these variables do not typically change on a weekly basis., The remainder of the study variables was measured on the within-level. We used validated translations of items of existing Dutch scales that were rewritten to apply to entrepreneursGeneral Questionnaire: Crisis-related demands were measured based on the report of the Rijksoverheid (2020). We started with asking the question: “Did the following things change in comparison to the period before the COVID-19 pandemic?” Participants had to answer this question for the number of assignments on a slider scale ranging from -100% (complete decrease) to +100% (complete increase). Next, we asked the question: “In general, to what extent have the government's COVID-19 measures influenced your work?” that could be answered on a scale from 1 (not at all) to 4 (completely). Furthermore, the average number of working hours per week during the COVID-19 pandemic was subtracted from the average number of working hours per week before the COVID-19 pandemic and participants were asked whether they have had to close their business. So, in total we got four indicators change in (1) assignments, and (2) working hours, (3) closed business, and (4) general influence of governmental COVID-19 measures. The Cronbach’s alpha of this scale was 0.74 see Table 1 for more information).Financial resources of entrepreneurs were based on the general financial security of entrepreneurs (Nibud, 2019). We used 3 items asking for having insurances (i.e., professional insurance and/or liability insurance, disability insurance, life insurance) and 3 items for putting money aside (VAT, financial buffer, private bank account). All items could be answered with 0 (no) or 1 (yes). An example item is: “I set aside my money for: the VAT I have to pay and/or the income tax I have to pay”. As the answering categories of the items were dichotomous, we used the tetrachoric correlation matrix from the item response theory to determine the factors (IRT factor analysis). Cronbach’s alpha was 0.62 (see Table 2 for more information).Weekly Questionnaire The weekly survey included shortened scales to minimize the time needed to complete them. All responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the current week. Weekly Financial Stress was measured with the five-item scale developed by Dijk et al., (2022) (e.g., “I wondered all the time if I have enough money.”). Cronbach’s α ranged between .95 and .97 (M = .96).Weekly Work-life Balance was measured with the four-item scale from Brough et al., (2014) (e.g., “I had a good balance between the time I spend at work and the time I have available for private activities.”). Cronbach’s α ranged between .88 and .94 (M = .92).Weekly Leisure Crafting was measured with the four-item scale of Petrou and Bakker, (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α ranged between .83 and .90 (M = .86).Strategy of Analysis The current study had a two-level design, with weeks (N = 229) nested within individuals (N = 75)
Survey data underlying the publication: An intervention among entrepreneurs: Effects on well-being, financial stress, and work-life balance
No full textMETHODS: Sample Data were collected among Dutch entrepreneurs who owned private companies and employed less than 50 employees (cf. Jayasekara et al., 2020). After completing the informed consent, respondents filled out the questionnaire. Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 75 entrepreneurs signed up for the intervention and filled out the first questionnaire. Of these N = 74, N = 67 started the self-training intervention, N = 53 participated in the pre-questionnaire, and N = 30 participated in the post-questionnaire of the workshop (Dropout of 40%). A total of N = 59 entrepreneurs participated in the pre- questionnaire and N= 56 participated in the post-measure of the control group (Dropout of 5%). Giving a total sample of N = 87 participants. The sample included 45 men (51.7 %) and 35 women (40.2%). Their mean age was 40 years (SD = 13.59). The average age of their business was 6.7 years (SD = 6.25), and most participants were active in business services (23%), the culture and leisure sector (12.6%), health and social work (11.5%), and trading (9.2%). The control group and the intervention matched based on gender and sector in which the entrepreneur was active. However, the groups differed in age (t = -7.25, p < .05). The intervention group was on average older (M = 51.6, SD = 9.02). In contrast, the control group had an average age of M = 34 (SD = 11.4). As age has a positive effect on how entrepreneurs manage their business (Zhao et al., 2021), this variable was added as control variables in our analysis. General surveyAll responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the past two weeks. All variables were in Dutch and rewritten to apply to entrepreneurs. Business crafting was measured with adapted job crafting scales by Demerouti and Peeters (2018) and Petrou et al. (2012) that had been used previously (Boesten et al., 2023) and was measured with nine items. For increasing resources (e.g., “I have tried to learn new things for my business”), Cronbach’s α was at T1 was .77 and Cronbach’s α at T2 was .64. For increasing challenging demands (e.g., “I have tried new approaches”), Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .78. For optimizing demands (e.g., “I look for ways to do my work more efficiently”), Cronbach’s α was at T1 was .83 and Cronbach’s α at T2 was .71. Leisure crafting was measured with the four-item scale of Petrou and Bakker (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α was at T1 was .89 and Cronbach’s α at T2 was .77. Network crafting was measured with the five-item scale of van Gool et al. (2023), (e.g., “I improve my network of relations with connections outside of our company to make my job more productive”). Cronbach’s α was at T1 was .91 and Cronbach’s α at T2 was .83. Financial Stress was measured with the five-item scale developed by Dijk et al., (2022) (e.g., “I wondered all the time if I have enough money.”). Cronbach’s α was at T1 was .94 and Cronbach’s α at T2 was .86. Work-life Balance was measured with the four-item scale from Brough et al., (2014) (e.g., “I had a good balance between the time I spend at work and the time I have available for private activities.”). Cronbach’s α was at T1 was .90 and Cronbach’s α at T2 was .48. Well-being was distinguished in motivation and fatigue severity and measured using shortened subscales of the Checklist Individual Strength (CIS; Bültmann et al., 2000). We used four items to measure motivation (e.g., “I was full of plans”). Cronbach’s α was at T1 was .81 and Cronbach’s α at T2 was .50. Another four items were used to measure fatigue severity (e.g., “I felt tired”), Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .57.Operational goal attainment was measured with three items in total (Grebner et al., 2010); e.g., “I completed my financial goals”). Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .89. Strategy of analysisData was analyzed using univariate analyses of covariance (ANCOVA) in R-studio (Egbewale et al., 2014; Valente & MacKinnon, 2017). Only the complete data (N = 29 intervention group; N = 56 control group) was used to examine the effects of the intervention over time. With ANCOVA, the effect of the intervention was tested using T0 as the covariate and T1 as the outcome. Besides ANCOVA being a reliable method to analyze the effect of interventions (Egbewale et al., 2014; Wan, 2021), we also decided to use ANCOVA because of a significant difference in our baseline. The mediating effect of the intervention through the trained strategies was tested using latent change scores (LCS) ANCOVA (Valente et al., 2021)
Survey data underlying the publication: An intervention among entrepreneurs: Effects on well-being, financial stress, and work-life balance
No full textMETHODS: Sample Data were collected among Dutch entrepreneurs who owned private companies and employed less than 50 employees (cf. Jayasekara et al., 2020). After completing the informed consent, respondents filled out the questionnaire. Confidentiality and anonymity of responses were emphasized and assured, and participation was voluntary. The Ethics Review Board of the university approved the study. A total of N = 75 entrepreneurs signed up for the intervention and filled out the first questionnaire. Of these N = 74, N = 67 started the self-training intervention, N = 53 participated in the pre-questionnaire, and N = 30 participated in the post-questionnaire of the workshop (Dropout of 40%). A total of N = 59 entrepreneurs participated in the pre- questionnaire and N= 56 participated in the post-measure of the control group (Dropout of 5%). Giving a total sample of N = 87 participants. The sample included 45 men (51.7 %) and 35 women (40.2%). Their mean age was 40 years (SD = 13.59). The average age of their business was 6.7 years (SD = 6.25), and most participants were active in business services (23%), the culture and leisure sector (12.6%), health and social work (11.5%), and trading (9.2%). The control group and the intervention matched based on gender and sector in which the entrepreneur was active. However, the groups differed in age (t = -7.25, p < .05). The intervention group was on average older (M = 51.6, SD = 9.02). In contrast, the control group had an average age of M = 34 (SD = 11.4). As age has a positive effect on how entrepreneurs manage their business (Zhao et al., 2021), this variable was added as control variables in our analysis. General surveyAll responses were given on 5-point Likert scales ranging from 1 ((almost) never) to 5 ((almost) always) referring to the past two weeks. All variables were in Dutch and rewritten to apply to entrepreneurs. Business crafting was measured with adapted job crafting scales by Demerouti and Peeters (2018) and Petrou et al. (2012) that had been used previously (Boesten et al., 2023) and was measured with nine items. For increasing resources (e.g., “I have tried to learn new things for my business”), Cronbach’s α was at T1 was .77 and Cronbach’s α at T2 was .64. For increasing challenging demands (e.g., “I have tried new approaches”), Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .78. For optimizing demands (e.g., “I look for ways to do my work more efficiently”), Cronbach’s α was at T1 was .83 and Cronbach’s α at T2 was .71. Leisure crafting was measured with the four-item scale of Petrou and Bakker (2016) (e.g., “I have tried to build relationships through leisure activities.”). Cronbach’s α was at T1 was .89 and Cronbach’s α at T2 was .77. Network crafting was measured with the five-item scale of van Gool et al. (2023), (e.g., “I improve my network of relations with connections outside of our company to make my job more productive”). Cronbach’s α was at T1 was .91 and Cronbach’s α at T2 was .83. Financial Stress was measured with the five-item scale developed by Dijk et al., (2022) (e.g., “I wondered all the time if I have enough money.”). Cronbach’s α was at T1 was .94 and Cronbach’s α at T2 was .86. Work-life Balance was measured with the four-item scale from Brough et al., (2014) (e.g., “I had a good balance between the time I spend at work and the time I have available for private activities.”). Cronbach’s α was at T1 was .90 and Cronbach’s α at T2 was .48. Well-being was distinguished in motivation and fatigue severity and measured using shortened subscales of the Checklist Individual Strength (CIS; Bültmann et al., 2000). We used four items to measure motivation (e.g., “I was full of plans”). Cronbach’s α was at T1 was .81 and Cronbach’s α at T2 was .50. Another four items were used to measure fatigue severity (e.g., “I felt tired”), Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .57.Operational goal attainment was measured with three items in total (Grebner et al., 2010); e.g., “I completed my financial goals”). Cronbach’s α was at T1 was .88 and Cronbach’s α at T2 was .89. Strategy of analysisData was analyzed using univariate analyses of covariance (ANCOVA) in R-studio (Egbewale et al., 2014; Valente & MacKinnon, 2017). Only the complete data (N = 29 intervention group; N = 56 control group) was used to examine the effects of the intervention over time. With ANCOVA, the effect of the intervention was tested using T0 as the covariate and T1 as the outcome. Besides ANCOVA being a reliable method to analyze the effect of interventions (Egbewale et al., 2014; Wan, 2021), we also decided to use ANCOVA because of a significant difference in our baseline. The mediating effect of the intervention through the trained strategies was tested using latent change scores (LCS) ANCOVA (Valente et al., 2021)