Statistics For The Behavior Sciences –
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.
Introducing Statistics and Describing and Organizing Data
- Describe how statistics are used in psychological research.
Identify the differences between sample and population data.
Discriminate between descriptive and inferential statistics.
Describe and interpret psychological data using descriptive statistics.
- Present data using graphs or charts.
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.
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.
Evaluate group differences using a one-way ANOVA.
Interpret the IBM® SPSS® output of a one-way ANOVA.
- Identify appropriate use of statistical tests using a decision tree.
Correlation and 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.
- Describe the logic of prediction with linear regression.
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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- While widely available, not all programs are available in all locations or in both online and on-campus formats. Please check with a University Enrollment Advisor.
- Transferability of credit is at the discretion of the receiving institution. It is the student’s responsibility to confirm whether or not credits earned at University of Phoenix will be accepted by another institution of the student’s choice.