Hello Attached Is A Data File I Created For A Survey Project

Helloattached Is A Data File I Created For A Survey Project I Am Work

Hello, Attached is a data file I created for a survey project I am working on. The aim of the research is to determine if church attendance has an effect on understanding of theology. There were 9 questions on the survey, each of which are inputted into the data file. The 10th question is the number of weeks the respondent claimed they went to church each year. The last column, tot_und, is simply a total score added up from each of the first 9 questions.

I'm certain I put the information into the data file the way it is supposed to go, so all I need to know is what type of test I should run. It is either one of the t tests or a one or two-way ANOVA. Those are the only options presented. I'm fairly certain I know what to do, but I would like a second opinion. So it is a simple question: which statistical test do I run? I don't need the test to be run, so the answer is as simple as "t test for independent means" or "one-way ANOVA," etc. Quick and simple.

Paper For Above instruction

The objective of this research is to explore whether church attendance influences individuals’ understanding of theology. Given the structure of the data and the research question, determining the appropriate statistical test is essential for valid inference.

Based on the description, the dataset includes responses to nine survey questions, a measure of church attendance expressed as the number of weeks attended per year, and a composite total score derived from the nine questions (tot_und). The key variables for analysis are the total score (dependent variable) and church attendance (independent variable). The core decision is whether to compare means between groups or analyze the relationship continuously.

Since church attendance is expressed as the number of weeks attended annually, it appears to be a continuous variable. If the goal is to assess whether different levels or categories of church attendance are associated with differences in understanding, one approach is to categorize attendance into groups (e.g., low, moderate, high) and then compare the means of the total scores across these groups using an ANOVA. The one-way ANOVA is appropriate here because it compares the means of a continuous dependent variable between three or more groups based on a single independent variable.

Alternatively, if church attendance is kept as a continuous variable, and the focus is on correlation rather than group comparison, then correlation analysis (such as Pearson’s correlation) would be ideal. However, since the options are limited to t-tests and ANOVA, a decision is made based on how attendance is operationalized in the dataset.

If the data is categorized into two groups (e.g., weekly churchgoers vs. non-weekly), an independent samples t-test could be appropriate to compare the mean understanding scores between these two groups. For example, dividing respondents into those who attend weekly and those who attend less frequently. In this case, the t-test for independent means would be suitable.

Therefore, the choice depends on the data's structure and the research hypothesis. If the attendance is split into multiple categories, a one-way ANOVA is most appropriate to compare the mean understanding scores across different attendance groups. If attendance is dichotomized (e.g., attend weekly vs. infrequently), then an independent samples t-test would suffice.

In summary:

  • If church attendance is grouped into categories (e.g., low, medium, high), run a one-way ANOVA to compare the mean total understanding scores across groups.
  • If church attendance is binary (e.g., weekly vs. non-weekly), run a t-test for independent means.

Given the limited options and typical research design, a one-way ANOVA is generally recommended if multiple attendance groups are formed, as it allows the comparison of more than two groups simultaneously. However, if only two groups are present, the t-test should be used.

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