Feb 06, 2023
2017-2018 Course Catalog
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# PSYC 2050 - Statistics for Psychology

Credits: 4
Hours/Week: Lecture 4Lab None
Course Description: This course is intended for anyone interested in learning basic psychology research design and statistical analysis. Students will use basic mathematical and computerized procedures to analyze data in the behavioral sciences and to conduct descriptive and inferential data analyses. A statistical software package (e.g., SPSS, R) will be used to analyze data. Students will choose and apply statistical procedures to help to answer psychological and behavioral scientific research questions.  Students will also read, interpret, and write APA-style Results sections for behavioral science research.
MnTC Goals
None

Prerequisite(s): MATH 1025   (preferred) or MATH 1061  or above with a grade of C or higher and PSYC 1020  with a grade of C or higher.
Corequisite(s): None
Recommendation: None

Major Content

1. Central tendency and variability

2. Basics of Inferential statistics: Z scores, the normal curve, sample versus population, and probability

3. Hypothesis testing

4. Making sense of statistical significance: Effect size, confidence intervals, and statistical power

5. Choosing appropriate statistics

6. Using SPSS or another appropriate statistical package, R, or other statistics program

7. Reporting results in APA format

8. The t-test: One and two Samples (between and within)

9. Introduction to analysis of variance

10. Correlation

11. Chi-square tests

Learning Outcomes
At the end of this course students will be able to:

1. demonstrate a college-level knowledge of the mathematics and logic behind selecting and applying statistical procedures appropriate for a given hypothesis, scale of measurement, and experimental design.

2. perform and describe the statistical procedures commonly used by social scientists including their respective advantages and disadvantages. These include:

1. Creating a visual display of data (e.g., bar chart, histogram)

2. Measures of central tendency, variability, and frequency distributions.

3. Correlational and regression analyses.

4. Inferential statistical procedures, including t-tests, ANOVAs, multiple comparison tests, confidence intervals, and effect sizes.

5. Nonparametric tests (e.g., chi-square).

3. interpret, and summarize basic statistical conclusions from psychological and behavioral science sources accurately and critically evaluate the statistical presentations of others.

4. interpret statistical findings and graphs in the context of their level of statistical significance, confidence intervals, effect sizes, and underlying assumptions, and explain these findings using common language and conventions of the American Psychological Association.

5. use SPSS or another statistical package to build data sets, run univariate analyses, and interpret and display results.