The parents who indicated their willingness to participate in an interview were asked to give their consent by signing the consent form (provided in Appendix I) and returning it to the school with their child. The parent (either father or mother) of the selected student interviewees was interviewed at their home straight after their child was interviewed. The parent interviews took approximately 20-30 minutes. Only on one occasion was the student participant present during the parent interview. All the interviews started with an informal chat about the family and participants’ interests to put the interviewee at ease. The parents who preferred to be interviewed in English were interviewed in English and those who chose to communicate in Malayalam were
interviewed in Malayalam. All the interviews were audio recorded using an iPhone.
3.10 Summary
This chapter has addressed the research design used in the study. An explanation of the instruments used for data collection for both students and parents was provided. A description of the participants and data collection procedures for both questionnaires, achievement test, and interviews was provided. The next chapter, Chapter 4, describes
CHAPTER 4 DATA ANALYSIS PLAN
4.1 Introduction
This chapter describes the procedures associated with data analyses employed in the study. Descriptive and measurement analyses were used to describe the nature of student data from the student questionnaire and the achievement test, and parent data obtained from the parent questionnaire. The hypothesised relationships among the studied
variables were explained using inferential data analysis techniques. The interview data were analysed using content text analysis. The chapter first describes the quantitative data analysis steps, followed by the steps in the analysis of the qualitative data obtained in the study.
4.2 Analysis of quantitative data
The data from all the measures of student and the parent questionnaires were analysed according to the analysis plan shown in Figure 4.1.
In accordance with the analysis plan, the first step was to prepare the data for analysis. The prepared student and parent data were subjected to Rasch measurement analysis separately. In the next step, the Rasch estimates for person ability were
calculated for students and parents. The student and parent data were then collated and the multivariate normality was checked for further analysis. As shown in the analysis plan (Figure 4.1), Rasch analysed variables were then compared using multivariate analysis of variance (MANOVA) and the structural relationships were established using structural equation modeling.
Student questionnaire with no missing data N=107 Partial credit models for student data (outlier removed) N=106 Person ability estimates for student data N=106 Student questionnaire raw data N=135 Selective case deletions Rasch analysis Export data Parent questionnaire with no missing data N=107 Partial credit models for parent
data (outlier removed) N=106 Person ability estimates for parent data N=106 Parent questionnaire raw data N=118 Selective case deletions Rasch analysis Rasch analysis Rasch analysis Case matching Collation of data Export data All group models Between groups models SEM MA NO VA Case matching Multivariate normality check
Figure 4.1. Overview of the analysis plan for quantitative data in this study
4.3 Preparation of data
The student and parent data obtained were entered into SPSS file and proofread to make sure that no errors occurred while transferring the data from paper to computer. A second round of proofreading of the data against the original was done to ensure that no errors occurred. Unmatched data (either no student data or parent data) were discarded from analysis. Using SPSS version 21.0 (IBM corp, 2012), a frequency analysis was conducted to find any missing or irregular data. A descriptive analysis was carried out using SPSS to report the sample population. All the details of these analyses are reported
in the next chapter, Chapter 5. The next section of this chapter details the analysis procedures used to describe the data fit to the Rasch model and their performance.
4.4 The Rasch models
Measuring a latent trait (a complex behaviour or concept that can’t be observed directly) with precision has been a crucial issue in social science research. Generally, these concepts or behaviours are measured using questionnaires, with items describing the various aspects of the trait under study. The participants indicate their level of agreement with each statement by means of raw score. Because the raw score is an ordinal measure indicating a possible measure of the latent trait, it has little inferential value, and
comparisons about the latent trait under study are difficult (Bond & Fox, 2007).
The introduction of Rasch family of measurement models (Rasch, 1960, 1980; Bond & Fox, 2007) solved the problem and provided a stronger basis for comparisons. Rasch measurement models allow the researcher to produce real interval measures and the Rasch person ability estimates and the item difficulty estimates generated can be used for further statistical analysis (Bond & Fox, 2007). In the Rasch models based on the
performance of persons and items, a set of measures are produced which define the position of persons and items on the same measurement scale. This provides an interval scale in units of logits, the logarithm of the odds of success (Bond & Fox, 2007). The participants’ data from the student questionnaire and parent questionnaire in this study were ordinal. Rasch measurement models were used to produce interval measures
allowing better comparisons and more meaningful interpretation. The Rasch modelling software Winsteps version 3.75.1 was used in this study (Linacre, 2012).