
If you wish to learn more in-depth about analyzing data, this is best accomplished by trying this out at your own pace. Some code and data sets used are freely available for download via links from http://gss.princeton.press.
Introduction to Stata
Stata is a popular statistical programming environment. It is available in several forms and runs on Macintosh, Windows and Linux computers.
Though not a graded assignment in this course, if you wish to explore and practice elementary Stata functionalities using data sets, visit http://www.stata.com. At its most basic level, Stata can be used as a calculator.
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.
Explore how statistics collect, analyze, present, and interpret data to understand relationships between variables in psychology. Describe descriptive statistics that summarize data.
Explore descriptive data analysis and statistics, describing data with mean, median, mode, range, variance, and standard deviation to reveal patterns and distributions.
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 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.
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 conditional probability and random variables through approachable examples and essential concepts, aligning with the quantitative analysis course goals.
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).
Don't be afraid of Math, Numbers or Statistics. If you have to take any statistics course, TAKE THIS CLASS FIRST. Very friendly to "non-math" students.
Quantitative social science is an interdisciplinary field encompassing a large number of disciplines, including economics, education, political science, public policy, psychology, and sociology.
In quantitative social science research, scholars analyze data to understand and solve problems about society and human behavior.
Because social scientists directly investigate a wide range of real-world issues, the results of their research have enormous potential to directly influence individual members of society government, policies, and business practices.
Course Description: This course involves a study of statistical concepts used in psychology and social science applications. Successful students will acquire knowledge and develop skills which will enable them to compare and contrast descriptive and inferential statistics; calculate and interpret descriptive measures for data sets; describe the role of probability in statistical inference; formulate an hypothesis, test it, and interpret the results; calculate and interpret the correlation between two factors; and identify the appropriate statistical technique to use in order to solve various social science applications. Prerequisite: None
Course Objectives: The primary goal of this course is to establish a foundation of knowledge for the understanding and acceptance of quantitative analysis and statistics in its many forms. In order to achieve this goal, the following objectives are offered as guidelines for evaluation of the progress of the student in the course. At the end of the course, the student should be knowledgeable of the following quantitative analysis core information:
--Identify the basic theories of quantitative analysis and statistics, how they work, and what they profess to accomplish.
--Explain quantitative analysis in a meaningful way by utilizing basic terms of the discipline.
--Become familiar with how researchers apply basic theories to solve real world problems.
--Begin to think critically about statistics and develop their own statistical sense.
--Demonstrate how research designs and methods are utilized by the scientific method to test theories.