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Nonparametric Tests
Many interesting questions related to students are categorical. For instance, there is considerable interest in the different enrollment patterns of male and female students in the following majors: Science, technology, engineering, and mathematics (STEM). While the literature does provide robust data for the numbers of students enrolled in these majors, intriguing questions remain that may be better suited to more qualitative data collection. For instance, a research question is: What barriers are perceived by female students with regard to focusing on these majors, and do male students face similar barriers? Categorical representation of presumed barriers could form the basis for a study using Chi-square analysis to explore the frequency of students who identify particular barriers to selecting STEM majors. Chi-square analysis, or goodness of fit, relies on frequency data, which means that it has applicability for nominal data ("SPSS Help Sheet," n.d.). The variables of gender and the barrier categories are discrete, categorical (fitting into bins), and nominal ("UCLA Statistics," 2013). Retrieved from VCU's Statistical Sciences & Operations Research Web Chi-Square Goodness-of-Fit Test in SPSS. Retreived from http://www.or.vcu.edu/help/SPSS/SPSS.GoodnessOfFit.PC.pdf
RQ1: What barriers do female and male students perceive with regard to selecting majors in science, technology, engineering, and mathematics (STEM) courses?
H0: there are no differences in the barriers perceived by male and female students with regard to majoring in science, technology, engineering, and mathematics (STEM) courses
H1: there are differences in the barriers perceived by male and female students with regard to majoring in science, technology, engineering, and mathematics (STEM) courses.
The Chi-square test of independence assumes that each subject contributes data to only one cell. This means that the sum of frequencies in the cells of the table must equal the number of subjects in the study (Lane, 2007).
References
Lane, D.M. (2007). HyperStat online statistics textbook. Chapter 16: Chi Square: http://davidmlane.com/hyperstat/chi_square.html
UCLA Statistics Online Computational Resource Wiki page. (2013, April 4) Chapter XII: Non-Parametric Inference. Retrieved from ? http://wiki.stat.ucla.edu/socr/index.php/AP_Statistics_Curriculum_2007_Contingency_Fit
Part 2. Article Critique.
Hill, G., & Hannon, J.C. (2008). An analysis of middle school students physical education physical activity preferences. The Physical Educator, 65 (4), 180-194. Proquest doi: 1671954361.
In accordance with the long-term objective of establishing a proclivity for healthy physical exercise in students that will last throughout their lifetimes, Hill and Hannon (2008) explore the activity preferences of middle school students for physical education classes.
Research Question
The purpose of the research study was to ascertain the preferences of middle schools students for their physical education classes.
RQ1: What activities do middle school students prefer for physical education instruction, and are there differences in those preferences that are attributable to gender, grade level, the motor skill competencies of the students, and student participation in physical activities during non-school hours?
H0: there are no differences in physical education activity preferences among middle grade students
H1: there are differences in physical education activity preferences among middle grade students
Methods and Study Design
The sampling frame consisted of 1500 students in grades 7, 8, and 9 at two middle schools in the Southwestern United States. The student ethnic populations in the two schools were similarly distributed. Returning parental consent forms for participating in the survey netted extra credit for the students. A total of 881 surveys were appropriately completed, with 97% of…
references of middle grade students, findings that have long-term implications for healthier lifestyle choices. Moreover, the Chi-square analyses offered a partitioning of the data that pointed to specific preference patterns that are useful to educators planning physical education courses for middle grade students.
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