psych625 | Graduate

Statistics For The Behavior Sciences

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This course presents fundamental statistical concepts and tools for understanding and analyzing data from studies in the social and behavioral sciences. Topics include measures of central tendency and dispersion, probability theory, data distributions, significance testing, and statistical inference. Students will learn how to analyze and interpret data from psychological studies using descriptive statistics, correlational methods, t tests, and analysis of variance procedures.



This graduate-level course is 6 weeks This course is available as part of a degree or certificate program. To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 6 weeks

topic title goes here

    Introducing Statistics and Describing and Organizing Data

    • Present data using graphs or charts.

    • Identify the differences between sample and population data.

    • Describe how statistics are used in psychological research.

    • Discriminate between descriptive and inferential statistics.

    • Describe and interpret psychological data using descriptive statistics.

    Other Ways to Describe Data – Evaluating Data Quality and the Standard Normal Distribution

    • Define reliability and validity.

    • Interpret data quality using measures of reliability and validity.

    • Explain the role of probability in statistical analysis using the standard normal distribution.

    • Define a standard score and use standard scores to evaluate individual performance.

    Correlation and Regression

    • Describe the logic of prediction with linear regression.

    • Explain the difference between correlation and causation.

    • Describe relationships among variables using the Pearson product-moment correlation coefficient.

    • Describe statistical significance in reference to the correlation coefficient.

    • Interpret the IBM® SPSS® output of a Pearson product-moment correlation.

    Introduction to Inferential Statistics – Hypothesis Testing, Statistical Significance, and Z Tests

    • Explain the logic of hypothesis testing.

    • Differentiate between the null and research hypothesis.

    • Describe significance testing and the meaning of probability levels.

    • Differentiate between Type I and Type II error.

    • Test a hypothesis using the standard normal distribution.

    Comparing Means Using the T Test and Analysis of Variance (ANOVA)

    • Evaluate group differences using a two-sample independent t test.

    • Describe the advantages of using an ANOVA over the use of multiple t tests.

    • Interpret the IBM® SPSS® output of a one-way ANOVA.

    • Identify appropriate use of statistical tests using a decision tree.

    • Evaluate group differences using a one-way ANOVA.

    When Your Data Does Not Fit: Nonparametric and Other Tests

    • Describe the assumptions of t tests and ANOVA.

    • Discriminate between parametric and nonparametric tests.

    • Identify when to use parametric versus nonparametric tests.

    • Describe the comparisons in a chi-square analysis.

    • Interpret the IBM® SPSS® output of a chi-square test.
    • Identify the need for multivariate statistical procedures (MANOVA).
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