Checklist (total sub-score of the “What My Child Has” subscale) and the criterion variable (students’ letter identification scores on the Letter Identification Test) for preschool-aged children.
Descriptive Statistics
The descriptive statistics for the correlation of Total Letter Score and The Get Ready to Read: Home Literacy Environment Checklist Score are listed in Table 1.
Table 1
Descriptive Statistics of Total Letter Score and Get Ready to Read: Home Literacy Environment Checklist
M SD N
Survey Score 31.63 3.76 82
Letter Score 10.46 1.42 82
The descriptive statistics for the correlation of Total Letter Score and The Get Ready to Read: Home Literacy Environment Checklist subscale of “What my Child Has” are listed in Table 2.
Table 2
Descriptive Statistics of Total Letter Score and The Get Ready to Read: Home Literacy Environment Checklist subscale “What my Child Has”
M SD N Survey “What my Child Has” 33.96 18.94 82 Letter Score 10.46 1.42 82 Results Assumption Testing
The requirement of linearity of data could not be demonstrated. The scatterplots for linearity of data are presented in Figure 1 and Figure 2. Thus the assumption of linearity was found not tenable; therefore the Pearson Product-Moment correlation could not be used. The Spearmen Rank-Order correlation which is a nonparametric test was used to determine the results.
Figure 1. Scatterplot for Assumption Testing for Letter Score and Total Score Get Ready to Read
Checklist.
Figure 2. Scatterplot for Assumption testing for Letter Score and Subscale “What my child has”.
Null Hypothesis One
The first null hypothesis was: There will be no significant predictive relationship between the predictor variable the Get Ready to Read: Home Literacy Environment Checklist (total score of the Get Ready to Read: Home Literacy Environment Checklist) and criterion variable,
students’ letter identification scores (score from the Letter Identification test) for preschool-aged children. The null hypothesis was tested using both the Pearson product-moment correlation and the Spearman rank-order correlation. The Spearman rank-order correlation was used because the data did not meet the linearity of the data assumption test. The Spearman rank-order test is used
when researchers have data that does not have a normal distribution (Cohen, Cohen, West, & Aiken, 2003). A Spearman’s rank-order correlation was performed to determine the relationship between the total score on the Get Ready to Read: Home Literacy Environment Checklist and the total letter score on the Letter Identification Test. The results of the Spearman’s rank-order correlation indicated a statistically significant relationship between the variables, r(80) = .684, p = 0.000 (see Table 3). Therefore, the first null hypothesis was rejected based on the Spearman rank-order correlation. The Pearson product-moment correlation also indicated a statistically significant relationship between the variables, r(80) = .721, p = 0.000 (See Table 4). This also indicated that the first null hypothesis could be rejected.
Table 3
Spearman Rank-Order Correlation of Total Score Letter Identification Test and Get Ready to Read: Home Literacy Environment Checklist
r p (2-tailed) N
Total score 1.00 .000 82
Letter score .684** .000 82
Note. **Correlation is significant at p < .01 (2-tailed).
Table 4
Pearson Product-Moment Correlation of Total Score Letter Identification Test and Get Ready to Read: Home Literacy Environment Checklist
r p (2-tailed) N
Total score 1.00 .000 82
Letter score .721** .000 82
Note. **Correlation is significant at p < .01 (2-tailed).
Null Hypothesis Two
The second null hypothesis was: there will be no significant predictive relationship between subscale (“What My Child Has”) from the predictive variable the Get Ready to Read: Home Literacy Environment Checklist (total sub-score of the “What My Child Has” subscale) and the criterion variable (students’ letter identification scores from the Letter Identification Test) for preschool-aged children. The null hypothesis was tested using both the Pearson
product-moment correlation and the Spearman rank-order correlation. The Spearman rank-order correlation was used because the data did not meet the linearity of the data assumption test. The Spearman rank-order test is used when researchers have data that does not have a normal distribution (Cohen et. al., 2003). A Spearman’s rank-order correlation was performed to
determine the relationship between the subscale “What my Child Has” of the Get Ready to Read: Home Literacy Environment Checklist and the total letter score from the Letter Identification Test. The results indicated a statistically significant relationship between the variables, r(80) = .300, p = .006 (see Table 5). Therefore, the null hypothesis was rejected. The results of the
Pearson product-moment correlation also indicated a statistically significant relationship between the variables, r (80) = .312, p = .004 (see Table 6). This also indicated that the second null
hypothesis could be rejected. Table 5
Spearman Rank-Order Correlation of Total Score Letter Identification Test and Sub-scale “What my Child Has” Get Ready to Read: Home Literacy Environment Checklist
r p (2-tailed) N
Total score 1.00 .006 82
Letter score .30** .006 82
Note. **Correlation is significant at p < .01 (2-tailed). Table 6
Pearson Product-Moment Correlation of Total Score Letter Identification Test and Sub-scale “What my Child Has” Get Ready to Read: Home Literacy Environment Checklist
r p (2-tailed) N
Total score 1.00 .004 82
Letter score .312** .004 82
Note. **Correlation is significant at p < .01 (2-tailed).
The Pearson product-moment correlation is used when the variables are expressed using continuous scores to determine the significance of the relationship between the two variables (Gall et al., 2007). This statistical analysis was most appropriate due to the nature of the variables within the study. The Pearson product-moment correlation examines the variables in terms of their relationship to each other on a straight line. The closer the variables are to the straight line, the weaker their correlation. The correlation is strong when the line is positioned in
a diagonal which represents a negative or positive correlation depending on the direction of the line (Boslaugh & Watters, 2008). The data in this study indicated that the Pearson product- moment correlation was not the most appropriate method to analyze due to the requirement of linearity. Therefore, the Spearman rank-order correlation was used. The Spearman rank-order correlation is a non-parametric statistical analysis and therefore, does not have the statistical power that a parametric analysis has (Gall et al., 2007). A non-parametric analysis is used when assumptions cannot be made about the distribution of data (Gall et al., 2007). This information should be taken into consideration when examining the results of this study. The Spearman rank-order correlation determines the relationship between variables in the same way that the Pearson product-moment correlation does except it puts the data in rank order and determines the relationship based on that information (Boslaugh & Watters, 2008). The Spearman rank-order correlation was most appropriate in this study due to the absence of the requirement of linearity of data for the Pearson product-moment correlation. The Spearman rank-order correlation provides information about the relationship between the variables, and the researcher therefore used the information to determine if the null hypotheses could be accepted or rejected.
Summary
Chapter Four reviewed the statistical findings of the research study, including a review of the research questions and null hypotheses. The descriptive statistics for the research were provided and the results were presented. Within the results was a review of the assumption testing that took place, and each null hypotheses was reviewed in terms of the statistical
significance. Chapter Five discusses the conclusions that can be drawn from the research and the implications for future research.
CHAPTER FIVE: CONCLUSIONS