Among several quantitative research alternatives is the experimental method. Experimentation is a very rigorous technique to control the conditions during data collection. This is a requirement when the objective is to determine cause and effect.
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One of the most confusing things about academic research is the inconsistency with terms. This is especially challenging with the concepts of research approach, method, and design.
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One of the most confusing things about statistics is the lack of a common vernacular of terms and definitions related to variables. The taxonomy of variables is complex and inconsistently applied throughout academia and the public and private sectors. Terms are often used interchangeably, with great confusion. Nevertheless, there are Read more…
“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” Sir R. A. Fisher, English statistician, First Session of the Indian Statistical Conference, Calcutta, 1938.1 Any worthy research and analysis, Read more…
In many scientific investigations, experiments, or research projects (including dissertations and theses), we’re interested in establishing some form of causation. We might express that objective in the form of “does factor A cause effect B?” This, generally, would be the gold standard, but it is also somewhat and sometimes challenging to prove.
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Previous articles on common multivariate tools (linear regression, binary logistic regression, and ANOVA) highlighted their benefits in real-world analysis, and in academic dissertations and theses. These articles emphasized that the techniques are properly used as components of a comprehensive predictive model-building process. I want to wrap our understanding of multivariate Read more…
A very useful technique in your toolkit of analyses for dissertations and theses, as well as real-world problems, is known as repeated measures. We choose this tool when we are measuring the same items, or people, multiple times. This could be over time, or under different circumstances—but the same participants. Read more…
Another technique in your toolkit of multi-variate analyses is analysis of variance (ANOVA). In this article, I describe ANOVA, contrast it with regression, and tell you what you need to know to execute the technique properly for your theses, dissertations, and real-world analyses. What is ANOVA? Univariate ANOVA is used Read more…
Among the most important components of multi-variate analysis and model-building (e.g., multiple regression and ANOVA) are two-factor interactions (or, 2-factor interactions). 2-factor interactions provide some of the most insightful outcomes of analysis in dissertations, theses, and practical applications. The concept of 2-factor interactions is a bit challenging to understand at Read more…
Multiple linear regression is a powerful tool. It can be used in dissertations and theses when our goal is to: identify the factors that influence an outcome, predict an outcome based on a set of factors, or assess the sensitivity of an outcome to various factors. Here’s the issue: Regression Read more…