Isgender associated with political party affiliation?
Thevariables in this study are gender, which has two categories, andpolitical party affiliation, which has three categories (politicalparties).
Nullhypothesis (H0):There is no association between gender and political partyaffiliation
Alternativehypothesis (H1):There is an association between gender and political partyaffiliation.
Chi-squaretest is appropriate for this study because the sampling method issimple random sampling. The study has two variables, which aremeasured at nominal level. The two variables consist of categoricalindependent groups (Franke et al, 2012).
TheChi Square statistic will indicate if gender is associated withpolitical affiliation or not. If the chi-statistic is greater thanthe Chi-Square critical the null hypothesis is rejected and hence anassociation between gender and political party association. If theChi statistic is less than the chi critical then we fail to rejectthe null hypothesis hence no association between gender and politicalassociation (McHugh,2013). The chi square test though cannot indicate if one variable isdependent on the other. For example, it cannot tell if politicalparty affiliation is dependent on gender of an individual (Frankeet al, 2012).
HowSample Size Related To Statistical Tests and Outcomes
Itis important to calculate the sample size before data collection asthis helps one to know the appropriate sample size and to avoidwastage of resources. Very small sample size will affect the validityof the results and the representativeness of the population while alarge sample size can make small differences to be extremelystatistically significant even though they might not be legit (Faberet al, 2014). For example in this study if there is a reasonablesample size, there will be representative and accurate results.
Faber,J., & Fonseca, L. M. (2014). How sample size influences researchoutcomes. Dentalpress journal of orthodontics,19(4),27-29.
Franke,T. M., Ho, T., & Christie, C. A. (2012). The Chi-Square TestOften Used and More Often Misinterpreted. AmericanJournal of Evaluation,33(3),448-458.
McHugh,M. L. (2013). The chi-square test of independence. BiochemiaMedica,23(2),143-149.