DUE 3/16/19   8P.M EST BE ON TIME AND ORIGINAL WORK!! HAVE SPSS AND READ DIRECTIONS!! DATA IS ATTACHED AND STEP BY STEP GUIDE IS ATTACHED Two-way ANOVA enables researchers to study the effects of a variable upon two independent variables at multiple levels. Researchers might wish to compare the exercise habits (represented by number of steps taken per month) of individuals, based on their gender and education. Two categories of gender and three education levels may be assessed. Two-way ANOVA can account for the effects of these groups, independently, on the number of steps taken each month. It can also help to determine whether interaction exists. For this Assignment, you use two-way ANOVA with interaction. Be sure to complete all of the parts of the assignment listed below. As this is an ANOVA, you also use multiple comparisons to determine for which factors the differences are significant. Also, to avoid additional type 1 errors, you must use Tukey, one of a number of possible methods to adjust for your multiple comparisons. Purchase the answer to view it

Introduction

Two-way ANOVA is a statistical technique that allows researchers to investigate the effects of two independent variables on a dependent variable at multiple levels. It is particularly useful when researchers want to compare the effects of different factors on a particular outcome variable. In this case, we will be studying the exercise habits of individuals based on their gender and education levels using two-way ANOVA with interaction.

The purpose of this study is to determine if there are any significant differences in the exercise habits of individuals based on their gender and education levels. The two independent variables are gender (with two categories: male and female) and education levels (with three categories: high school, college, and graduate). The dependent variable is the number of steps taken per month, which serves as a measure of exercise habits.

Main Analysis

To conduct the two-way ANOVA with interaction, we will use SPSS software. The first step is to load the dataset provided and specify the variables we will be using in the analysis. The dataset must contain the variables for gender, education, and number of steps taken per month. Additionally, we need to ensure that the data is coded appropriately (e.g., male = 1, female = 2; high school = 1, college = 2, graduate = 3) for the analysis.

Next, we will conduct the two-way ANOVA analysis using the General Linear Model (GLM) procedure in SPSS. We will specify the dependent variable (number of steps taken per month) as the outcome variable and the independent variables (gender and education) as fixed factors. We will also include the interaction term between gender and education to examine if there is any interaction effect.

The results of the analysis will provide information on the main effects of gender and education, as well as the interaction effect between the two variables. The main effects represent the overall differences in exercise habits across levels of each independent variable, while the interaction effect indicates whether the effect of one variable depends on the level of the other variable.

Post Hoc Tests

To further investigate significant differences identified by the ANOVA, we will conduct post hoc tests. In this analysis, we will use the Tukey’s method to adjust for multiple comparisons and control the overall type I error rate. Tukey’s method allows us to make pairwise comparisons between different levels of gender and education, taking into account the number of comparisons being made.

Conclusion

In conclusion, two-way ANOVA with interaction is a powerful statistical technique that allows researchers to examine the effects of two independent variables on a dependent variable. It is particularly useful in situations where the researcher wants to compare the effects of different factors on a particular outcome variable. In this study, we will be using two-way ANOVA with interaction to analyze the exercise habits of individuals based on their gender and education levels. The results of the analysis will provide insights into the main effects of gender and education, as well as any interaction effect. This information can help researchers understand the factors that influence exercise habits and design interventions and programs accordingly.