PSYC 2170 - Psychological Statistics

Credit Hours: 4.00

Objectives:

1. Describe the difference between populations and samples, descriptive and inferential statistics, and continuous and discrete variables.
2. Identify and describe the scientific method and the design of research studies.
3. Identify the various scales of measurement.
4. Recognize and recall various statistical notations.
5. Explain and create frequency distribution tables and graphs.
6. Explain and compute measures of central tendency.
7. Explain the concept of variability and compute variance and standard deviation.

Outcome 2: Upon completion of this course, students will be able to explain, compute, and apply z-scores.

Objectives:

1. Locate z-scores in a distribution, and explain how to use them to standardize a distribution.
2. Explain the concept of probability and apply it to z-scores and the distribution of sample means.

Outcome 3: Upon completion of this course, students will be able to explain and apply the concepts and statistical procedures of hypothesis testing using z-scores and the t-statistic.

Objectives:

1. Identify, explain, and conduct hypothesis tests using z-scores
2. Explain the concept of t, and conduct hypothesis tests with single-samples, independent samples, and related samples.

Outcome 4: Upon completion of this course, students will be able to explain and apply the concept and procedure of Analysis of Variance (ANOVA).

Objectives:

1. Identify, explain, and compute Analysis of Variance (ANOVA).
2. Describe and explain the concepts and use of planned and unplanned comparisons, and post hoc tests (e.g., Tukey’s Honestly Significant Difference (HSD) test, and Scheffé test).

Outcome 5: Upon completion of this course, students will be able to explain and apply the concept and statistical procedures of correlation and regression.

Objectives:

1. Calculate and interpret correlations including Pearson and point-biserial correlations.
2. Calculate and interpret regression equations.

Outcome 6: Upon completion of this course, students will be able to explain and apply the concept and statistical procedures of chi-square.

Objectives:

1. Describe the difference between parametric and nonparametric tests.
2. Calculate and interpret the chi-square test for goodness of fit, and the chi-square test for independence.

Outcome 7: Upon completion of the course students will be able to explain and apply the procedures involved with the use of SPSS.

Objectives:

1. Use SPSS to enter, analyze, and interpret data including: frequency distributions, means, measures of variance, z-scores, t-tests, correlation and regression, and ANOVA.
2. Interpret SPSS output.

1. Introduction to statistics:
   1. Populations and samples
   2. The scientific method and the design of research studies
   3. Variables and measurement
   4. Statistical notation
2. Frequency Distributions:
   1. Frequency distribution tables
   2. Frequency distribution graphs
   3. The shape of the frequency distribution
3. Central Tendency:
   1. The mean
   2. The median
   3. The mode
   4. Selecting a measure of central tendency
4. Variability:
   1. The range
   2. Standard deviation and variance for a population and samples
5. Z-scores:
   1. Z-scores and location in a distribution
   2. Using z-scores to standardize a distribution
6. Probability:
   1. Probability and the normal distribution
   2. Probabilities and proportions for scores from a normal distribution
7. Probability and samples:
   1. Samples and sampling error
   2. The distribution of sample means
8. Introduction to hypothesis testing:
   1. Uncertainty and errors in hypothesis testing (e.g., type I/II error)
   2. Conducting hypothesis tests
   3. Measuring effect size and power
9. Introduction to the t-statistic:
   1. The t-statistic as an alternative to z
   2. Hypothesis tests with the t-statistic
10. The t-test for two independent samples:
    1. The t-statistic for an independent-measures research design
    2. Assumptions
11. The t-test for two related samples:
    1. The t-statistic for related samples
    2. Assumptions
12. Estimation:
    1. Estimation with the t-statistic
13. Introduction to analysis of variance (ANOVA):
    1. ANOVA notation and formulas
    2. The distribution of F-ratios
    3. Hypothesis testing and effect size with ANOVA
    4. Post hoc tests
14. Repeated-measures and two-factor analysis of variance:
    1. Conducting repeated-measures ANOVA
    2. Conducting two-factor ANOVA
15. Correlation and Regression:
    1. Using and interpreting the Pearson correlation
    2. Hypothesis tests with Pearson correlation
    3. The Point-biserial correlation
    4. Introduction to regression
16. The chi-square statistic:
    1. Parametric and nonparametric statistical tests
    2. The chi-square test for goodness of fit
    3. The chi-square test for independence
    4. Measuring effect size
    5. Assumptions and restrictions
17. SPSS Lab
    1. Entering data in SPSS
    2. Using SPSS to create frequency distribution graphs and tables
    3. Using SPSS to get mean, variance, standard deviation, and z-scores
    4. Using SPSS for t-tests, correlation, regression, and ANOVA
    5. Interpretation of SPSS output