*Statistics for the Behavioral and Natural Sciences

Number of:

Credit Hours

3

Lecture Hours

3

Lab Hours

0

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 higher. Cross-listed as BIOL/PSYC 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 characteristcs 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 scatterplot 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 probabilty (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

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.