Results That Are Not Statistically Significant

This article is intended to help the reader understand and discuss a non-statistically significant finding for one (or more) of their hypothesis test results (e.g. t-test, Chi-square test, ANOVA) etc.
To begin with, I’ll provide some background to provide context. One thing all inferential statistical analyses have in common is . . .

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Sample Size and Target Population

The text book approach to determining a sample size is to estimate the expected effect size and then use statistical power analysis software to determine the necessary sample size for a given alpha level (e.g. 0.05) and power (e.g. 0.80) in order to detect the estimated effect size.

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Matching Pre-Post Data Annonymously

When utilizing a pre-test/post-test survey study design, it is necessary to match individual study participant’s pre-test data to their post-test data. Intuition might suggest the researcher only needs the study participants to write their name, email address, social security number or some other personal identifier on their pre/post surveys so they can later be matched.

. . .

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Combined Comparative and Correlational Study Desgin

It is very common for a research study to involve some research questions that are comparative (e.g. t-test, ANOVA) and other research questions that are correlational (e.g. Pearson’s correlation). Sometimes, university committee members challenge the doctoral study to define the study design as one or the other, comparative or correlational, and they won’t accept a combination of both comparative and correlational analyses.

In my opinion, this challenge should never come up because . . .

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All But Dissertation (ABD)

A doctoral student has completed all of their coursework but they have not yet completed their dissertation. There are two types of doctoral candidates that fall into this category:

1) The “just arrived” and anxious to move forward.

2) The “been there for awhile” and think they will never move forward.
While both types might require help to move on, it is the latter that is likely to derive the most benefit from this article and become motivated to complete, perhaps, the most important event in their life . . .

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Summative Scale Scores and Parametric Statistics

Occasionally, doctoral students are challenged on the validity of using parametric statistics to analyze summative scale scores. I’m referring to a scale score that is derived by averaging (or summing) many Likert-type survey questions to measure an underlying construct like “emotional intelligence” for example. So, for example, let’s say . . .

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Ordinal data, continuous data, and statistical tests

Several of my clients, and their committee members have had some misunderstandings about the use of parametric statistics with ordinal data, so I decided to write this article.

Many statistical procedures such as Pearson’s correlation and Linear regression analysis require certain assumptions about the data in order for the procedure to be valid. One of those assumptions . . .

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