Mathematics Anxiety and Mathematics Self-efficacy in Relation to Medication Calculation Performance in Nurses Page: 86
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categories were at least interval data; for instance, the math test had a score from 0 to 20 points.
There were no perfect linear relationships, and the data had variation with no predictor variations
of zero. The data have a relatively normal distribution supported by the values (see Table 12)
that have only one kurtotic values greater than absolute one (Neill, 2008). The data were
reviewed and both tolerance and Variance Inflation Factor (VIF) are in the region of one,
diminishing the probability of multicollinearity (Foster et al., 2006) (see Table 13). Scatter plots
were examined to view data for possible curvilinear distributions (see Figure 6). None of the
plots appear curvilinear. With low probability of multicollinearity and with these values of
skewness and kurtosis, no transformations were deemed necessary.
Skewness and Kurtosis Statistics
Mean Std. deviation Variance Skewness Kurtosis
Std. error Statistic Statistic Statistic Std. error Statistic Std. error
MAS 1.144 10.489 110.012 .182 .263 -.175 .520
NSE-Math 1.558 14.277 203.832 -.715 .263 .474 .520
BB .257 2.356 5.553 -.487 .263 -.330 .520
Hours Worked .864 7.29 62.771 -.533 .263 2.229 .520
MAS=Mathematics Anxiety Scale; NSE-Math=Nurse Self-Efficacy for Mathematics; BB=Bayne Bindler Medication
Correlations Collinearity statistics
Partial Part Tolerance VIF
MAS -.196 -.187 .739 1.384
NSE .066 .061 .716 1.397
Hrs Worked .229 .219 .964 1.354
Dependent Variable: Test Score; NSE-Math=Nurse Self-Efficacy for Mathematics; MAS= Mathematics Anxiety Scale
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Melius, Joyce. Mathematics Anxiety and Mathematics Self-efficacy in Relation to Medication Calculation Performance in Nurses, dissertation, May 2012; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc115119/m1/94/: accessed February 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .