Data Analysis & Statistics makes quantitative and qualitative analysis approachable for researchers at every level. These guides explain core concepts — descriptive and inferential statistics, choosing the right statistical test, regression, correlation, sample size, significance, and analysis software such as SPSS — in clear, practical terms. Rather than drowning you in formulas, each article focuses on understanding what a method does, when to use it, and how to interpret the results correctly. Whether you are analysing survey data, preparing a results chapter, or learning statistics for the first time, you will find reliable explanations written to build genuine analytical confidence.
TL;DR — Quick Answer Mediation and moderation answer two different questions about a relationship between variables. Mediation asks how or why X affects Y — it identifies a third variable (M) that carries the effect, so X influences M, and M in turn influences Y. Moderation asks when or for whom X affects Y — […]
Read More →TL;DR — Quick Answer Structural Equation Modelling (SEM) is a statistical technique that tests an entire theoretical model at once — measuring latent constructs from observed items and estimating the relationships between those constructs simultaneously. It combines two parts: the measurement model (Confirmatory Factor Analysis, which checks that your survey items actually measure the constructs […]
Read More →MK — Dr. Madhuri Kanojiya, Founder & Director · Empire Research Press Most researchers meet the t-test and ANOVA first, run into a dataset that breaks their assumptions, and only then discover there’s an entire parallel set of statistical tests built for exactly that situation. Non-parametric tests don’t get the spotlight parametric tests do, but […]
Read More →TL;DR — Quick Answer Cronbach’s alpha (α) is the most widely used measure of internal consistency reliability — it tells you how closely the items in a multi-item scale hang together as a measure of one construct. Alpha ranges from 0 to 1, and the conventional benchmark is that 0.70 or above is acceptable, 0.80+ […]
Read More →TL;DR — Quick Answer The t-test, ANOVA, and chi-square test are the three most commonly used statistical tests in research, and each answers a different kind of question. A t-test compares the means of two groups on a continuous variable (e.g., do male and female employees differ in average job satisfaction?). ANOVA extends this to […]
Read More →TL;DR — Quick Answer A Likert scale is a rating scale used to measure attitudes, opinions, and perceptions by asking respondents how strongly they agree or disagree with a series of statements — most commonly on a 5-point scale from “Strongly disagree” to “Strongly agree.” Strictly, a single statement is a Likert item; the Likert […]
Read More →TL;DR — Quick Answer SPSS (Statistical Package for the Social Sciences) is a widely used software application for managing and analysing quantitative data. It lets researchers enter data, run statistical tests, and produce tables and charts through a menu-driven interface — without needing to write code. SPSS is popular in the social sciences, business, and […]
Read More →TL;DR — Quick Answer Regression analysis is a statistical method that models the relationship between one outcome variable and one or more predictor variables, allowing you to explain and predict outcomes. In simple terms, it answers the question: “How does Y change as X changes?” Simple linear regression uses one predictor; multiple regression uses several. […]
Read More →TL;DR — Quick Answer Descriptive statistics summarise and describe the data you actually have, while inferential statistics use that data to draw conclusions about a larger population. Descriptive statistics include measures like the mean, median, range, and standard deviation, along with charts and tables — they tell you what your data look like. Inferential statistics, […]
Read More →TL;DR — Quick Answer Choosing the right statistical test depends on four things: your research question, the type of variables you have, the number of groups or variables involved, and whether your data meet the assumptions for parametric tests. In short — decide whether you are comparing groups, looking for relationships, or predicting outcomes; identify […]
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