Measures

4.4.1 Overview of the measures

An overview of the self-report measures applied in the three studies is presented in Table 2, and the wording of all items are provided in Appendix 4. Except for two scales (perceived feedback quality and achievement ambition), all scales were derived from measures in earlier published work. Some of them had undergone slight adjustments, as noted in the individual papers. Since particular importance lies in the dependent variable (intentions to quit school), a more thorough description is devoted to this measure after Table 2. Strategies to evaluate the psychometric qualities of the measures are described in Section 4.4.2.

Table 2. Overview of measures Construct

(no. of items) Item example Response

categories Derived from Study Reliability (α) Intentions to quit

school (5) I have concrete plans to

quit school. 1 (Absolutely not true)

to 6 (Absolutely true) Frostad et al., 2015;

Vallerand et al., 1997

I feel that my teachers care about me.

Perceived feedback quality (5)

I often get feedback from the teachers that I can use to improve my schoolwork.

1 (Completely disagree) to 6

(Completely agree). - Study Ⅰ T1: .89

Loneliness among

peers at school (6) I feel lonely at school. 1 (Absolutely not true) to 6 (Absolutely true).

- denotes that the scale was constructed for the present work.

Reliability (α) is Cronbach’s alpha.

4.4.1.1 Detailed description of assessment of intentions to quit school The five indicators of intentions to quit school in the current research were derived primarily from Frostad et al.’s (2015) measure of intention to leave upper secondary school, which in turn refers to Valås (2001) and Vallerand et al. (1992). The latter (Vallerand et al., 1992) is a validation of the Academic Motivation Scale (AMS) based on the tenets of different types of motivation in SDT. It is in the AMS subscale for amotivation that we find some of the formulations included in the scale of intention to leave (e.g., I really feel that I’m wasting my time in college). This origin of the measure underscores the conceptual link to amotivation.

In the process of adjusting the scale by Frostad et al. (2015) to the current research, we were guided by three principles. First, we purposely strengthened the element of intentions to behavior, aiming to be more in line with the motivational model of dropout by Vallerand et al. (1997), and thus added the item I have concrete plans to quit school. Second, we did not allow wording that pointed to an explanation of why these intentions emerge; thus, we excluded the following three items originally in Frostad et al., 2015: I often consider leaving this school because the subjects are too theoretical; I often consider leaving this school because of problems in my family; and, I often consider leaving this school because of continuous conflicts with my teachers.

Finally, we wanted the items to represent intentions to quit school in general, and not an intention to change school or educational program. Item formulations like I consider leaving this school and finding a job instead were therefore adjusted to I consider leaving school and finding a job instead.

Through this process we ended up with the five items reported in Appendix 4.

4.4.2 Measurement models and measurement invariance

In Study Ⅰ, the statistical models were specified with latent factors (and thus all observed indicators), whereas composite scores were created in Study Ⅱ and Ⅲ due to overall model complexity. However, measurement models with latent factors were initially inspected for constructs involved in the latter studies as well, to assess how well the expected measurement model fitted the covariance

Methodology

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matrix of the observed data (Brown, 2015). A well-fitting measurement model would support construct validity. Particular attention was given to factor loadings (preferably > .40), the root mean squared error of approximation (RMSEA, preferably < .070), the standardized root mean square residual (SRMR, preferably < .080), the comparative fit index (CFI, preferably > .95), and the Tucker-Lewis index (TLI, preferably > .95) (Hooper et al., 2008).

Modification indices were routinely inspected to identify possible misspecifications. See individual papers for detailed results. In sum, standardized factor loadings were satisfactory (lowest = .47; found for one item of academic self-concept; Study Ⅲ), and an overall consideration of the above-mentioned fit indices indicated acceptable to good model fit (Brown, 2015;

Hooper et al., 2008).

Multigroup and longitudinal invariance testing were performed to verify that meaningful comparisons across groups and time could be made with the instruments of interest. Regarding longitudinal measurement models, we followed Little’s recommendation (2013) to allow the residuals of items measured repeatedly to correlate; to account for any systematic variance associated with the particular item and avoid forcing it into other parameters in the model (Little, 2013). The measurement models were tested for invariance across genders in Study Ⅰ, and across time points in Study Ⅱ and Study Ⅲ. More and more restrictive models (adding equality constraints) were inspected and evaluated according to the recommendations provided for multigroup and longitudinal invariance testing (Chen, 2007; Cheung & Rensvold, 2002; Meade et al., 2008). The series of tested models allowed us to conclude that the relevant measures were sufficiently invariant across gender (at T1, Study Ⅰ), and across time (Study Ⅱ and Study Ⅲ).

4.4.3 Control variables

Aiming to adjust for potential alternative explanations for the associations between the constructs of interest, gender, academic achievement from lower secondary school, and study track were consequently included as control variables in the structural models. These variables were chosen as control variables because they have repeatedly been found as predictors of motivation, dropout, and dropout intentions (Battin-Pearson et al., 2000; Frostad et al.,

2015; Hardre & Reeve, 2003; Markussen et al., 2011; Wang & Fredricks, 2014), and could also be expected to covary with our independent variables.

These variables were obtained from county register data. To represent academic achievement level from lower secondary school, the mean of the three core subjects Norwegian, Mathematics, and English was used (α = .86).

In Study Ⅰ, immigrant background was also included as a control variable (based on participants’ reports of their parents’ country of birth), but as this variable showed no effect or influence on models in Studies Ⅱ and Ⅲ, it was not used further. As noted in Study Ⅰ, other available variables were initially tested as control variables, but since they did not add or change anything in the models as long as prior achievement was included, they were regarded as inessential and not retained. This supports prior research conclusions, that the effects of family SES and demographic characteristics on educational outcomes in upper secondary education are primarily indirect (Markussen et al., 2011), that is, they are captured by the level of academic achievement by the end of lower secondary school.