Aligning research design and statistical analyses. From the first day I sat in my undergraduate “Research Methods” course staring at SPSS output, I knew I found my calling.

From the first day I sat in my undergraduate “Research Methods” course staring at SPSS output, I knew I found my calling. I can still recall my first research paper. Watching my the completed surveys come in, diligently cleaning the data and crossing my fingers in the hopes of significant results. Despite my results coming back not significant I knew I found my passion.

In spite of engrossing myself in the topic, I found it particularly difficult aligning research design to statistical analysis. As the terminology began to roll-in (ie. t-tests, ANOVA, effect size, IV, MANOVA, ANCOVA, regression, R², etc.) I grew more confused and frustrated. The sheer amount of terminology used in designing experiments, analyzing, and interpreting results can be a daunting reality.

Although to fully describe the in-depth nature of each research design along with the appropriate statistical models we would require a lengthy textbook, I hope to provide a condensed summary. Below is a high-level summary of research design principals and appropriate statistical models used to analyze the data.

For an experiment to be considered a true experiment there needs to be some manipulation. In other words, we need to impose onto our study participants and measure the effect of that imposition. This is where the independent and dependent variables come into play.

**Independent Variable (IV):**The variable which is being actively manipulated by the researcher. Depending on study design, we can have multiple IVs and each IV can have multiple “levels” which participants are subjected to. For example, does the type of teaching style affect students’ test scores? This study’s only IV would be teaching style and it would have 3 unique levels (ie. authoritarian, authoritative, permissive).**Dependent Variable (DV):**The variable which is hypothesized to be affected by the IV. This is also the variable which the research measures to determine what effect, if any, the IV had. In our example, students’ test scores would be the only DV.

The cornerstone of any study is the collection and analysis of data. Depending on the study design, hypotheses, and data collection methods we can have four types of quantitative data. Each scale of measurement has distinct properties which determines the use of specific statistical analyses.

parametric quantitative-analysis research-methods statistical-analysis anova data analysis

These statistical tests allow researchers to make inferences because they can show whether an observed pattern is due to intervention or chance. There is a wide range of statistical tests.

Even though Big data is into main stream of operations as of 2020, there are still potential issues or challenges the researchers.

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

Global Terrorism Database Analysis was a quick project for understanding and implementing various descriptive statistics and exploratory data analysis techniques.

Permissible statistics define a set of statistical tests that are allowed at each level of measurements. While tests allowed at higher levels cannot be performed at lower levels, vice-versa is acceptable.