The following section discusses how data from the survey were analyzed and reported. Because the study sought information from both qualitative and quantitative sources, being able to represent the data in a format that can be easily read and condensed for inferences to be made was integral to the effectiveness of the study. SES, self-efficacy and parent involvement were analyzed to determine whether there is an association between them, and what the strength of that association
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is. Survey results were also examined to see whether a high level of self-efficacy correlates with a high level of parent involvement with learning at the secondary level.
3.4.1 Hypothesis Testing
For this study, a hypothesis was investigated that stated there is a correlation between parental SES, self-efficacy, and involvement. The null hypothesis stated that these variables are not correlated and that no relationship is present, with the alternative hypothesis and test of statistical significance being non-directional. The standard significance level of .05 was utilized for all statistical analysis.
3.4.2 Categorization of Variables
This study sought to determine if the independent variables of socioeconomic status and self- efficacy correlate with the dependent variable of parent involvement. Composite variables were created by adding together scores of individual questions. For self-efficacy, after two outliers and one question response was removed; it was able to be viewed as a continuous independent variable because the data adjustment made the responses normally distributed. Parent SES was treated as a categorical independent variable (High vs. Low). Parent involvement questions were either dichotomous (Yes vs. No) or categorical. These were all combined into a dichotomous grouping (Never vs. Ever) to form a composite variable. Because the distribution was normal for parent involvement and variances were equal at both levels of SES, the parent involvement composite variable was treated as continuous.
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3.4.3 Tests of Normality and Variance
So that the parametric statistical methods being proposed accurately reflect the data collected, tests of statistical assumption were performed. Field (2009) stated “Different statistical models assume different things, and if these models are going to reflect reality accurately, than these assumptions need to be true” (p. 132). First, the frequency of the responses to questions was analyzed with the Shapiro-Wilk test to determine if the requirement for normal distribution was met. In addition, the Brown-Forsythe was used to test for the homogeneity of variance of the variables. The Brown- Forsythe analyzed parent involvement with high and low SES groups to compare their variances in order to determine if they are equal across the two SES groups.
The results of these two tests of assumption determined which statistical tests of significance may be employed.
Normality and variance were affected by the number of responses and the response items selected. Contingent upon the response data meeting the statistical assumptions for testing, ANCOVA was used to measure the associations among the composite variables. The ANCOVA is an inferential test used to analyze the relationships of the variables among the population sample and was the strongest test possible to measure them.
To further measure the relationships of the individual variables and reinforce the ANCOVA, additional alternative non-parametric tests of association were performed. Fisher’s Exact test was used to establish a p-value between the dichotomous categorical variables of parent involvement and SES to determine if significant relationships exist between them. This was done to measure the difference of how many respondents fell into which category of SES and dichotomous indicators of parent involvement. T-tests were used to examine mean score
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differences on the continuous variable of self-efficacy, comparing means for dichotomous groups for individual parent involvement variables. The use of these descriptive statistics allowed the research to examine the average self-efficacy score per category. These allowed for inferences to be made about relationships of the variables in the sample.
3.4.4 Concerns of Statistical Significance Tests
Testing for statistical significance helps to ensure that the values of the associations among the variables are not the result of sampling error. This serves to rule out associations between the variables that might not represent genuine relationships in the population being studied (Babbie, 1998). These tests do not address the effect size or the practical significance of the observed relationship. The statistical tests helped to determine whether the null hypothesis was true or could be rejected.
3.5 RESEARCH QUESTIONS
The methodology in this study used several formats in which the research questions may be answered. Using contingency tables, the frequency and type of parent involvement were able to be measured among parents of high and low SES. This was compared with parents’ degrees of self-efficacy. These data informed the first and third research questions. Tables were used to depict both the frequency and type of parental involvement activities parents engage in with their 10th grade students. This answered the second research question that sought to identify the types of parent involvement activities in which parents engage at the secondary level. Lastly, the open-
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ended survey questions that asked parents for their input on what educators can do to help them improve their own self-efficacy with learning were coded and reported in a separate table. This addressed the fourth and final research question. Taken together, the data collected describes the type and frequency of parent involvement, while informing the reader on whether a correlation between self-efficacy and parental involvement exists, if it varies due to a parent’s socioeconomic status.