Indicate briefly the data edits, data entry procedures, and data transformations that you will undertake before analyzing your survey data to answer your research questions. Describe your data cleaning and treatment of missing values. This may also include reverse scoring of items, adding items to form composite scores, etc.

Next, indicate any analysis to describe your sample. This will help in later chapters where you can use this information to talk about how your sample did or did not represent the larger population. This is also helpful when it comes to defining the external validity of your study. You should also present any analyses that you will do to support your study (e.g., test demographic differences to argue the equivalence of your groups) before addressing the research questions. This begins with a discussion of your response rate.

You would also indicate any analyses that would be conducted to enhance the reliability of your results (e.g., Cronbach alphas). You will want to present how you will compare these values to ones obtained during your pretest or to those of other researchers if using an existing instrument. (Large discrepancies can be used to explain unexpected outcomes.)

 Finally, specify the data analyses that will be used to answer your research questions. Justify your choices considering the nature of your research questions, the level of measurement of your data, and the manner in which the data were collected. Each analysis should be directly linked to each research question. Be sure to include all the necessary tests (e.g., post hoc pairwise comparisons), a measure of effect size, as well as how the assumptions of your tests will be met.

This section should be presented chronologically. First you must manage your data–enter, clean, transform; before you can analyze the data. Do not talk about analyzing data before you talk about how you will get them into a form that is ready to analyze and verified (as much as possible) that they are reliable (e.g., Cronbachs or inter-rater reliability).

 

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