Master Syllabus

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Administrative Unit:	Psychology and Sociology
Course Prefix and Number:	PSYC 324
Course Title:	*Statistics for the Behavioral and Natural Sciences

Number of:

Credit Hours

Lecture Hours

Lab Hours

Catalog Description:	The study of parametric and nonparametric statistics commonly used in the behavioral and natural sciences. Included are analyses of relationship and variance, as well as effect sizes associated with each. Students majoring in Biology or Psychology must earn a grade of C or better. Cross-listed as BIOL/SOCI 324. Prerequisite: Grade of C or higher in MATH 150 or higher.

Prerequisite(s) / Corequisite(s):	Grade of C or higher in MATH 150 or higher.

Course Rotation for Day Program:	Offered Fall and Spring.

Text(s):	Most current editions of the following: Basic Statistics for the Behavioral Sciences By Heiman, G.W. (Houghton-Mifflin) Recommended Statistics for the Behavioral and Social Sciences By Aron, A., & Aron, E.N. (Prentice Hall) Recommended Introductory Statistics for the Behavioral Sciences By Toothaker, L.E., & Miller, L. (Brooks/Cole) Recommended Basic Standards for the Social and Behavioral Sciences By Diekhoff, G.M. (Prentice Hall) Recommended Behavioral Statistics in Action By Vernoy, M.W., & Vernoy, J.A. (Wadsworth) Recommended Statistical Concepts for the Behavioral Sciences By Kiess, H.O. (Allyn and Bacon) Recommended Statistical Methods for Psychology By Howell, D.C. (PWS Kent) Recommended Applied Statistics for the Behavioral Sciences By Hinkle, D.E., Wiersma, W., & Jurs, S.G. (Houghton-Mifflin) Recommended

Course Objectives	To correctly choose the appropriate statistical test for a given set of data. To compute basic descriptive statistics. To compute basic parametric and nonparametric statistics. To interpret the results of descriptive and inferential statistical analyses. To use a scientific calculator and a packaged computer program (e.g. Statistica, SAS, SPSS, etc.) to compute statistics.

Measurable Learning Outcomes:	Explain the basic research designs, including correlational method and experimental method. Define sample and population. Describe the four scales of measurement. Create simple, relative and cumulative frequency distributions from data sets. Describe the characteristics of normal and non-normal distributions of data. Calculate measures of central tendency, including mean, median and mode using a scientific calculator. Describe when the use of mean, median and mode is appropriate. Using a scientific calculator, calculate measures of variability, including range, sample and population variances, sample and population standard deviations, estimations of the population variance and standard deviation, and the standard error of the mean. Apply the standard deviation to a normal distribution. Describe the usefulness of transformed scores. Calculate and interpret z-scores, T scores and percentiles. Describe correlations between two variables (e.g., negative, positive, none). Interpret a scatter plot based on the slope of regression line and the dispersion of data around the line of best fit. Calculate a simple regression line and use it for prediction. Calculate the standard error of the estimate and demonstrate an understanding of the error in prediction. Describe and explain the basics of probability (e.g., region of rejection, alpha level, p). Describe and explain statistical hypothesis testing, including rejecting and failing to reject the Null Hypothesis. Describe and explain errors in statistical decision-making (i.e., Type I and Type II Errors). Define power of a statistical test and the ways in which power can be maximized. Calculate and interpret Confidence Intervals. Define independent samples. Correctly choose which statistic is appropriate for a given sample, calculate results and interpret, for the z-test and the single-sample t-test. Describe and explain when it is appropriate to choose parametric verses non-parametric statistics. Describe and explain the logic of an analysis of variance (ANOVA). Demonstrate competence for when it is appropriate to choose to calculate post-hoc comparisons (e.g., Tukey Test). Calculate post-hoc comparisons using a statistical software program and interpret the results. Correctly choose which statistic is appropriate for a given sample and develop a statistical hypothesis to test. Then using a statistical software program, develop a spreadsheet, calculate the main statistic and interpret the result. Next, calculate an effect size/coefficient of determination and interpret the result. This process should be demonstrated for at least 10 of the following statistical tests: - Spearman Rank-Order Correlation - Pearson Product-Moment Correlation - t-test for independent samples - t-test for dependent samples - one-way ANOVA for independent samples - one-way ANOVA for dependent samples - two-way ANOVA for independent samples - two-way ANOVA for dependent samples - mixed design two-way ANOVA - Mann-Whitney U test - Rank sums test - Wilcoxon Test - Kruskal-Wallis H - Freidman's ANOVA - One-way chi-square analysis - Two-way chi-square analysis

Topical Outline:	Basic scientific methods Measurement sclaes Sampling distributions Descriptive statistics: measures of central tendency, variability, standardized test scores Basics of probability Statistical hypothesis testing Inferential statistics: parametric and nonparametric statistics (i.e., choosing appropriately, calculating, and interpreting) Effect size and power

Recommended maximum class size for this course: 15

Library Resources: Online databases are available at http://www.ccis.edu/offices/library/index.asp. You may access them using your CougarTrack login and password when prompted.

Prepared by: Cheryl Hardy

Date: November 29, 2010

NOTE: The intention of this master course syllabus is to provide an outline of the contents of this course, as specified by the faculty of Columbia College, regardless of who teaches the course, when it is taught, or where it is taught. Faculty members teaching this course for Columbia College are expected to facilitate learning pursuant to the course objectives and cover the subjects listed in the topical outline. However, instructors are also encouraged to cover additional topics of interest so long as those topics are relevant to the course's subject. The master syllabus is, therefore, prescriptive in nature but also allows for a diversity of individual approaches to course material.

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