Quantitative Analysis in Psychology / Social Science

Understand the foundations of statistics and quantitative analysis, using probability, inference, estimation, and hypothesis testing with mathematical and statistical methods to evaluate data and inform decisions and predictions.

Learn how social science research methods collect and analyze data to explain behavior across anthropology, economics, political science, psychology, and sociology, guiding policy, education, and marketing.

Review major psychological perspectives from structuralism and functionalism to behaviorism and Freudian psychology, and explore cognitive, biological, evolutionary, social-cultural, and positive psychology.

Explore descriptive, correlational, and experimental research methods, including naturalistic observation, survey research, and case studies; learn about independent and dependent variables, experimental manipulation, and why correlation does not imply causation.

Psychologists apply the scientific method to understand behavior by identifying questions, forming explanations, conducting research, and communicating findings, guided by theories, hypotheses, and operational definitions.

Explain how to write a research report using APA style, covering in-text citations, references, plagiarism avoidance, and the literature review versus experimental report formats for psychology.

Differentiate quantitative analysis, which uses hard data and numbers, from qualitative analysis, which relies on subjective characteristics and non-numeric insights, and learn to describe data numerically and calculate the mean.

Explore how statistics collect, analyze, present, and interpret data to understand relationships between variables in psychology. Describe descriptive statistics that summarize data.

Explore how quantitative analysis uses mathematical and statistical modeling to measure, evaluate, and predict events and the value of financial instruments, while contrasting it with qualitative analysis.

Describe data using descriptive statistics and graphs to summarize central tendency and variability, including mean, median, mode, range, standard deviation, and variance in psychology and social science research.

Explore descriptive data analysis and statistics, describing data with mean, median, mode, range, variance, and standard deviation to reveal patterns and distributions.

Describe data using graphs and tables, presenting descriptive statistical results in writing, graphs, and tables per APA or APRA guidelines, with simple, interpretable graphs that add essential information.

Analyze correlation analysis to quantify the association between two continuous variables, and examine regression analysis to relate an outcome variable Y to predictors X, including confounders and effect modification.

Explore how inferential statistics use sample data to draw conclusions about a population, test hypotheses, and estimate population characteristics with confidence intervals.

Investigate causal effects and counterfactual reasoning within quantitative analysis and statistics, applying clear concepts to understand how outcomes differ under alternative scenarios.

Explore the difference between categorical and quantitative data, define categorical variables and their ordinal and nominal measurement scales, and illustrate with data tables and examples.

Explore measurement and survey sampling in psychology, using questionnaires, observations, and physiological measures to quantify constructs; compare bar graphs, histograms, and box plots, and analyze correlation, scatterplots, and regression.

Explore measurement and bar plots in statistics, highlighting how data is collected, visualized, and interpreted to support quantitative analysis.

Explore how survey research collects data from a predefined sample using questionnaires, quantitative and qualitative methods, and mixed methods to analyze attitudes, behavior, and correlations.

Explore survey sampling in the context of quantitative analysis and statistics, as presented in this course.

Explore how prediction uses knowledge to forecast future behaviors in psychology. Define prediction, prediction error, bias, and root mean squared error, and note empirical testing with psychometric assessments.

Investigate prediction in the context of quantitative analysis and statistics for a certificate of completion.

Explore probability concepts in psychology, from p values of 0.05 or less for significance to the three probability axioms, and learn to construct discrete distribution tables and graphs.

Explore the building blocks of probability and how psychologists use significance testing to assess whether results occurred by chance, using p = 0.05 and probability laws.

Explore conditional probability and random variables through approachable examples and essential concepts, aligning with the quantitative analysis course goals.

Interpret probability through a guided analysis to grasp uncertainty in quantitative analysis and statistics for informed decision making.

Explore uncertainty in psychological statistics, including random error, sampling variability, and confidence intervals. Learn hypothesis testing with five steps and how parameter estimation informs decision making under uncertainty.

Explore uncertainty and estimation within quantitative analysis and statistics, turning sparse lecture cues into practical insights for data interpretation and decision making.

Master hypothesis testing within quantitative analysis and statistics, building essential skills for evaluating data and drawing evidence-based conclusions.

Explore discovery and anova in psychological statistics using exploratory data analysis to identify patterns, assess false discovery rate, and compare population means across multiple groups.

Analyze variance to split variability into systematic and random factors, compare more than two groups, and interpret the F statistic under the null hypothesis in one-way ANOVA.

Explore discovery tools in quantitative analysis and statistics, as presented in the discovery tools lecture for the course quantitative analysis / statistics (cert. of completion).

Celebrate completing the course and solidify your basic foundations of quantitative analysis in social science. Focus on the principles of statistics and data analysis while keeping math anxiety low.