An inference is a conclusion or judgment based on facts or evidence.
I have taken a few statistics courses spread mostly over the 90s, but I managed. (To forget what I learned.)
Boiled down, I included in my “Data Analysis” section exactly how I plan to look at my quantitative data, and I’m not scared.
1. Type of data: interval. Logically organized with standardized differences. A Likert scale of 1 to 5 fits here.
2. Frequency distribution: Putting the data in a table according to variables
3. Percent distribution: Another table. Shows the percent of time the piece of data turns up
4. Mean, minimum, maximum, median, mode: I skipped reading this because I felt that my K-12 education hit this pretty hard, but it is covered again and another way to look at the data.
5. Disaggregate the data: Breaking down the totals by using crosstabs in your table. So you compare smaller subcategories and variables to make inferences.
6. Correlation: Determining statistically whether components of data have a strong or weak relationship to one another. Probably advanced software will be involved.
7. ANOVA: Analysis of Variance determines whether the difference in averages between two groups is statistically significant. Definitely advanced software will be involved.
8. Regression: Determines whether one variable is a predictor of another variable. Again, advanced software.
9. These data analysis can only determine correlation, not causation.
So I understand the chapter I wrote, and this is a start.
This is how I am viewing it all, and if you are put off by the data sets and analysis like I am, then maybe it will help you. The word inferential is only an adjective form of the verb “infer.” I have asked countless students to infer from reading clues, and I know how to do that.
I’ll be getting into more detail and breaking it down for you as my brain wraps around the data entry boxes.
Tell me your data analysis tips!