Browsing Tag

Research Methods

19 posts
benefits of nvivo for students

NVivo and Qualitative Data: Can Stats Students Benefit?

Great insights await stats students exploring NVivo’s potential—discover how this tool can transform qualitative data analysis and why it’s worth exploring further.
choosing statistical test appropriately

Chi-Square Vs Fisher’s Exact: When to Use Each

Keen to determine whether Chi-Square or Fisher’s Exact test fits your data? Discover the key differences and when to choose each.
understanding variance analysis methods

ANCOVA Explained in Plain English

Understanding ANCOVA in plain English reveals how it clarifies comparisons by accounting for influencing factors, but there’s more to uncover.
understanding variable relationships systematically

Structural Equation Modeling (SEM) Explained

Breaking down SEM reveals how complex relationships are modeled; continue reading to unlock its full potential for your research.
step by step hypothesis testing

Solving Hypothesis Testing Problems Step-by-Step

When solving hypothesis testing problems step-by-step, understanding each phase is essential to draw accurate conclusions and master the process.
null and alternative hypotheses

Hypothesis Testing Basics: Null and Alternative Hypotheses

Hypothesis testing involves comparing two key statements: the null hypothesis, which assumes…
statistical significance interpretation

P-Values and Confidence Intervals: Interpreting Test Results

Ineffective interpretation of p-values and confidence intervals can mislead your understanding of test results; discover how to interpret them correctly to make better decisions.
guide to test selection

Choosing the Right Statistical Test: Decision Flowcharts

Thinking about selecting the appropriate statistical test? Discover how decision flowcharts can guide your choice confidently and accurately.
normality variance independence

Assumptions of Parametric Tests: Normality, Homogeneity of Variance, and Independence

Learning about the assumptions of parametric tests—normality, homogeneity, and independence—helps ensure valid results, but understanding how to meet these requirements is essential.