Blindly trusting the output of a computer program, such as SPSS, without understanding the data my lead to misleading results. It is possible for results to appear statistically significant when the data as a whole do not support the results. There are also significant limitations to the chi-squared test that should be taken into consideration before embracing the results.
It is a common practice to assume that results of a Pearson Chi-squared are significant if the p value is 0.05 (chance) or less (Penn State Eberly College of Science, n.d.). A contingency table is used to enumerate the combination of categorical variables and values (Field, 2013). The table shows the counts of each combination.
Without knowing more about the specifics of the management style survey that is referenced in this assignment it is difficult to speak with exactness about the cause of the overall results being significant while being of low value, however, it is likely that that several combinations are highly correlated while the majority are not. To determine the specific situation a crosstabulation would be constructed and the standardized residuals compared to 1.96. If the absolute value for each combination is less than 1.96 that combination indicates the relationship is not significant.
Sample size and other factors can impact tests for significance (Runkel, 2012). It is important that when results of a Chi-squared test indicate significance that an appropriate test of strength (McHugh, 2013). Calculation of a value, such as Lambda, as a means of measuring the degree of association between conditions is an appropriate means of determining the strength of the relationship (AcaStat Software, 2015).
When doing analysis, it is important to consider the overall picture the data is showing. Rather than assuming the values produced by a statistical package are all-knowing, the researcher must dig deeper. When a result is shown to be statistically significant, it is an indication that additional analysis, such as effect size, is required.
References
AcaStat Software. (2015). Chi Square Measures of Association. Retrieved from http://www.acastat.com/statbook/chisqassoc.htm
Field, A. (2013). Discovering statistics using IBM SPSS statistics: Sage.
McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149. doi:10.11613/BM.2013.018
Penn State Eberly College of Science. (n.d.). 11.2 - Chi-Square Test of Independence. Retrieved from https://onlinecourses.science.psu.edu/stat200/node/73
Runkel, P. (2012). Large Samples: Too Much of a Good Thing? Retrieved from http://blog.minitab.com/blog/statistics-and-quality-data-analysis/large-samples-too-much-of-a-good-thing
No comments:
Post a Comment