Choose a research question you want i do not mind what it is.
This week’s main Discussion requires you to answer the question completely and correctly to receive full credit.
This week we talk about the uses of a crosstabulation (crosstabs) and the benefits of creating this “snapshot” of your data.
For this forum, provide a brief introduction to your study to remind your classmates what we are reading about here. Include:
1. Your overall research question
2. The research hypothesis and null hypothesis
Next, create a crosstab for your data and include it in the post. Be sure to explain your findings, including a description of the data, a calculation of the epsilons, and a discussion of the 10% rule. The epsilons in short are the differences between the highest and lowest column % in any given row. As long as one epsilon makes the 10% threshold, we’ll deem two variables have “enough” going on to with each other to warrant further statistical analysis.
Special note:
When a variable is continuous (interval/ratio level of measurement), for example, age of respondent, we do not run crosstabs directly b/c it will result in a really spread-out table with lot of 0s and low frequency cells. Such a crosstab does not help us understand the data.
The correct way is to reduce the level of measurement to either ordinal level or nominal level (group the numbers into categories) and then run the cross table. You do this by recoding (as you demonstrated in Assignment 1). In this way, your crosstabs will help us better understand data. Here is another example of recoding in a video.
Discussion Guidelines
Week 4 Forum Crosstabs.pdf
You must start a thread before you can read and reply to other threads
Week 4 wrap up
Class-
As we wrap up week 4, please be reminded of:
1. wording research questions and hypotheses for crosstabs (correlation or relationship)
2. data assumptions of either nominal or ordinal data for crosstabs
3. recoding continuous data into categories
4. interpretation of epsilons and the 10% rule to assess crosstab relationships between variables
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