Factorial Analysis of Variance in SPSS

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For this week 6 application 1 we are going to be using the data set named USW1_RSCH_8260_ statsAnxietyAdvQuant. sav file. This week assignment will make emphasis in factorial analysis of variance or two way Anova. According to Warner (2013) twoway ANOVA is used whenever we have two independent variables and want to verify the interaction or connection among this two groups. In this week application, our interest is in whether undergraduate students male and female have anxiety concerning their studies. Sample consisted of 335 participants, after they were classified between male and female (Gender), subsequently they were identified whether the undergraduate studies were natural science or social science (Degree), and afterwards assess the effect that anxiety (Class2) has among them. Therefore, the dependent variable was “class anxiety time 2”, and the two independent variables were “gender” and “degree”. A two-way ANOVA was conducted that examined the effect of gender and undergraduate degree and the anxiety. There was no statistically significant interaction in mean anxiety between gender p= .120 and neither between degree having an alpha level of p = .137. When we compare both variables together we got more evidence that there is not significance between gender and degree with anxiety F (1, 331) = 2.218, p = .493 (see appendices section). Furthermore, when we analyze our plot slopes both lines appear to be parallel at some point. Therefore, since they cross their lines we can said that there is no statistical significance among the selected factors. When it comes to social change we can said that there is no impact between males and females of undergraduate degree when it comes in having anxiety in their class. In conclusion, there is no positive or negative outcome when it comes to anxiety and gender, also, between anxiety and undergraduate degree.

References Warner, R. M. (2013). Chapter 13, “Factorial Analysis of Variance” . In Applied statistics: From bivariate through multivariate techniques(2nd ed., pp. 501-546). Thousand Oaks, CA: Sage Publications.