Quantitative data analysis fundamentals

Learn the fundamentals of data types and variables, distinguishing qualitative and quantitative data, discrete and continuous data, and the roles of independent and dependent variables.

Data analysis inspects, cleanses, transforms, and models data to support decision making; contrasts descriptive statistics with inferential statistics to describe samples and predict populations with regression.

Explore the basics of hypothesis and hypothesis testing, using practical examples to deepen your understanding of how to apply hypothesis testing in quantitative data analysis.

Quantitative research begins with formulating a hypothesis, a claim about how the independent variable will affect the dependent variable, and testing whether it is true or false.

Engage in hypothesis testing by sampling data from a population to validate your claim. Distinguish the null hypothesis from the alternative hypothesis to support or refute the claim.

Identify the right quantitative approach for your topic by comparing descriptive, correlational, experimental (randomized controlled trial), and quasi-experimental designs.

Explore univariate, bivariate, and multivariate analysis to match research questions with the number of variables, using descriptive statistics, correlation, and regression techniques.

Explore the quantitative data gathering process, covering sampling, data collection methods, and the pros and cons of surveys within quantitative research design.

Explore structured questionnaires as a core method for collecting data through online and offline surveys, using platforms like SurveyMonkey or Qualtrics to store responses in a database and generate analytics.

Design your survey using a 3-d approach by clarifying the objective, defining the variables to collect, and specifying what you measure, then choose the appropriate questionnaire format.

Explore the four scales of measurement: nominal, ordinal, interval, and ratio, with examples like smoking status, temperature, and income to show how data are categorized and compared.

Identify the scales of measurement to choose appropriate questionnaire types and explain open-ended, Likert, semantic differential, multiple-choice, rank-order, and dichotomous questions.

Explore the advantages of surveys for capturing large, low-cost data and easily producing quantitative summaries with consistent questions, while understanding limits like shallow insights and potential misgeneralization.

Examine how variable count and data distribution influence choosing quantitative data analysis, and review key tests like t test, correlational tests, ANOVA, and chi-square.

Explore quantitative data analysis by examining correlational tests, ANOVA, and chi-square, and understand descriptive, correlational, and experimental designs for relationships between two or more variables.

Understand how data normality guides the choice between parametric and non parametric tests, and apply tests such as t test, Pearson correlation, Wilcoxon tests, Spearman correlation, and Kruskal-Wallis.

Learn how the t-test compares means between two groups (e.g., control vs intervention) and uses p-values to assess significance, linking descriptive and inferential statistics.

Explore how the correlation coefficient measures the strength and direction of the relationship between two variables on a -1 to 1 scale, with Pearson, Kendall, and Spearman.

Explore anova to detect differences between means across multiple groups, and minova for multiple dependent variables; compare with t-test and chi square test.