Florida Agricultural and Mechanical Universitysupport.famu.edu/coeaccreditation/ExhibitRoom/Course Syllabi/School... · 1 Florida Agricultural and Mechanical University Professional

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Florida Agricultural and Mechanical University Professional Education Unit Tallahassee, Florida 32307

FALL 2008

COURSE SYLLABUS Course Number: PSY 6206-001 Prerequisite(s): Undergraduate statistics course

Course Title: Psychological Statistics, Measurement and Evaluation

Course Credit: 3 Course Hours: College: Arts and Sciences Department: Psychology

Required Text(s):

Faculty Name: Dr.. John W. Chambers

Shrink Wrap Green, S. & Salkind, N. (2008). Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (5th Edition). Uppers Saddler River, NJ: Pearson Prentice Hall. (GS) & SPSS 17.0 Windows and MAC Valuepack for SPSS, SPSS Inc.

Gravetter F. & Wallnau L. (2007). Statistics for the Behavioral Sciences (7th Edition). Belmont, CA: Wadsworth/Thomson Learning. (GW) Supplies: Hand Calculator Term and Year: Fall Semester 2008 Place and Time: E-Classroom, Building 45 Thursday 4:00-6:30 PM

Office Location: Room 304A, GECC Building

Telephone: 850-561-2541 e-mail: [email protected]

Office Hours

Monday Other Times by

Appointment

Tuesday 10:30-2:00 PM

Wednesday

Thursday 10:30-2:00 PM

Friday Saturday

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Course Description

This advanced level course in statistics will cover univariate, bivariate and multivariate statistics, with an emphasis on hypothesis testing and the use of parametric and non-parametric procedures. Specifically, measurement theory and descriptive statistics will be reviewed, followed by probability theory, t-test and analysis of variance, power analyses, correlational analyses, simple and multiple regression, and multivariate analysis of variance. SPSS for Windows statistical data analysis software will be used with the statistical procedures describe above.

Course Purpose

The purpose of this course is give students the statistical skill necessary carry out basic descriptive and inferential statistical analyses, and the ability to evaluate the appropriateness of the application of these analyses in research literature and other statistical applications.

Conceptual Framework

The Conceptual Framework in the Professional Education Unit (PEU) at Florida A&M University is an integrated approach to providing educational experiences that result in exemplary professional educators. The Framework is comprised of six themes with the mission of developing high quality classroom teachers, administrators and support personnel. The term “exemplary” refers to the kind of graduates the PEU strives to produce. The figure below provides a diagram of the Exemplary Professional Conceptual Framework

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TECHNOLOGY •CF 2 •Through this focal area, the FAMU professional education candidate will:

CF: 2.1 (S)

Use of available technology and software to support student learning.

F: 1, 4,12

I: 6, 8

CF: 2.2 (S)

Use technology to manage, evaluate and improve instruction.

F: 1,4, 12

I: 6, 8

CF: 2.3 (K)

Knows fundamental concepts in technology.

F: 1, 12

I: 6

CF: 2.4 (K)

Understands fundamental concepts in technology.

F: 1,12

I: 6

CF: 2.5 (S) Use fundamental concepts in technology. F: 12 I: 6

•CF4 •Through this focal area, the FAMU professional education candidate will:

CF: 4.1 (K)

Understand a variety of instructional/professional strategies to encourage student development of critical thinking and performance.

F:1,4

I: 6, 8

CF: 4.2 (S)

Use a variety of instructional/professional strategies to encourage students’ development of critical thinking and performance.

F:1,4,

I: 6

CF: 4.3 (D)

Values critical thinking and self-directed learning as habits of mind.

F: 1,12

I: 6

CF: 4.4 (K)

Acquire performance assessment techniques and strategies that measure higher order thinking skills of student.

F:1,12

I: 6

CF: 4.5 (S)

Demonstrate the use of higher order thinking skills.

F: 1,12

I: 6

Overall Goals of the Course

The overall goals of this course are to expose students to the concepts and procedures of measurement theory, which underlie psychometric measurement and assessment, and to enable them to acquire the basic competencies and skills needed to organize and analyze behavioral and educational research data utilizing appropriate statistical strategies.

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Specific Behavioral Objectives

1. Know how to, compute and interpret: descriptive statistics, t-test, analysis of variance, post comparison analyses, strength of association, power analysis, bivariate correlation, partial correlation, linear and multiple regression, chi square test of goodness of fit and chi square test of independence with the use of SPSS statistical software. CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

2. Understand the statistical designs that are appropriate for each of the statistical procedures listed in Objective 1 CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

3. Know how to encode data and manage files in SPSS for Windows CF: 1.0., 2.0.,

3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

4. To enable students to understand the inductive and deductive logic of both

theoretical and applied statistics, CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 1.0., 2.0., 3.0., 4.0., 5.0., 6.0., 8.0., 10.0., 11.0., 12.0. FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

5. To enable students to employ quantitative methods of thinking in analyzing and

understanding problems and concepts in psychology, and CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d..

6. Know how to create graphs, charts and tables to describe data in SPSS for

Windows. CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

7. Students will be able to organize and summarize research data with the

application of statistical procedures. CF: 1.0., 2.0., 3.0., 4.0., 5.0.; INTASC: 1.0., 6.0., 8.0., 9.0., 10.0., NASP: 2.1.0., 2.9., 2.11., FEAPS: 2.a. 2.g, 2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d.

National, State, and PEU Standards Addressed in the Course

Interstate New Teacher Assessment and Support Consortium (INTASC) Standards Professional Organization/Learned Society Standards Florida Educator Accomplished Practices (FEAPs)

Florida Teacher Certification Examination (FTCE) Subject Area Examination (SAE) Competencies and Skills

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Course Behavior Objectives Alignment with Course Assessment and Professional Standards

Course: College of Arts & Sciences

Department: Psychology Professor: Dr. John W. Chambers

Course #: PSY 6206 Course Title: Psychological Statistics, Measurement & Evaluation

Term: Fall 2008

Course Assignment

(Assessment)

Behavior Objective

PEU Conceptual Framework Alignment

INTASC Standards

Professional Organization

Florida Educator

Accomplished Practices

Assig. 7: Two-Way ANOVA

B.O. 1, 2, 3, 4, 5, 6, 7

CF: 1.0., 2.0., 3.0., 4.0., 5.0

INTASC: 1.0., 6.0., 8.0., 9.0., 10.0

NASP: 2.9 FEAPS: 2.a. 2.g,2.h, 2.j, 4.3, 4.b, 4.g.4.j, 8.a, 8.c, 8.d

Assig. 11: Partial Correlation

B.O. 1, 2, 3, 4, 5, 6, 7

CF: 1.0., 2.0., 3.0., 4.0., 5.0

INTASC: 1.0., 6.0., 8.0., 9.0., 10.0


Assig. 13: Multiple Regression

B.O. 1, 2, 3, 4, 5, 6, 7

CF: 1.0., 2.0., 3.0., 4.0., 5.0

INTASC: 1.0., 6.0., 8.0., 9.0., 10.0


Cours e Policies SYLLABUS POLICY:

The course syllabus presented in this document will be followed as closely as possible. However, the course syllabus, schedule, policies, and procedures are subject to change at the discretion of the instructor or in the event of extenuating circumstances. This includes the tentative dates listed. As much as possible, changes will be announced in advance by the instructor. Since such announcements are typically made during class, it is the student’s responsibility to clarify any of these changes that may have been made when the student was absent. Although you will be responsible for all of the material in the assigned readings, some of the information may not be covered in class. Therefore, if there is material that is unclear to you it is highly recommended that you discuss this material with me at an appropriate time (e.g., during office hours). It is your responsibility to read the syllabus and ask questions about it so that you have a clear understanding of the expectations. If you are unclear about any of the requirements/ expectations, then please set up a time to speak with me. I will be grading as though you have a clear understanding of the course requirements and material.

ACADEMIC HONESTY STATEMENT:

Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution to maintain academic and behavior excellence. All University policies regarding academic integrity apply to this course. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations,

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facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work of another person or work previously used without informing the instructor, or tampering with the academic work of other students. For any material or ideas obtained from other sources, such as the text or things you see on the web, in the library, etc., a source reference must be given. Direct quotes from any source must be identified as such. All exam answers must be your own, and you must not provide any assistance to other students during exams. Behavior misconduct is also a form of academic dishonesty and disrupts the learning environment. This too is not allowed and is considered impermissible conduct by students. Any instances of academic dishonesty WILL be pursued and filed to the university grievance committee and FAMU’s College of Arts & Sciences Dean, Dr. Ralph Turner.

ATTENDENCE:

Attendance will be recorded daily. Make-up exams can only be taken with an excused absence from the dean’s office. Exams missed because of an unexcused absence cannot be made up. All make-up exams must be taken within a week of the scheduled exam – THERE WILL BE NO EXCEPTIONS.

ACCOMMODATION FOR STUDENTS WITH DISABILITIES:

The Americans with Disabilities Act of 1990 requires that the University make reasonable accommodations to persons with disabilities, as defined in the Act. Students who believe they qualify under the ADA guidelines for these accommodations should approach FAMU’s “Learning Development and Evaluation Center” (850-599-3180) to discuss such considerations. The LDEC will then contact each of your instructors to inform of them of how they may help you meet your academic goals.

DISCRIMINATION AND HARASSMENT:

Discriminatory and harassing behaviors are reprehensible and will not be tolerated at Florida A & M University. They subvert the mission of the University and the MSW program, violate social work code of ethics, and threaten the careers, educational experiences and well-being of students, faculty and staff. The University and the MSW program will not tolerate behaviors between or among members of the University community, which create an unacceptable working/learning environment. Similarly, the Instructor will tolerate neither discriminatory or harassing behaviors between or among students enrolled in the course.

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Topical Outline

COURSE OUTLINE: The assignments are located at the end of each chapter in the GS SPSS textbook. Homework assignments will be completed using the data contained on the data disk that accompanies the MJN text. These homework assignment and their due dates will be given throughout the semester. Week Course Assignments 1. August 24: Introduction GS: Lessons 1-4: Getting Started with SPSS 2. August 31 GS: Lessons 5-9: Creating and Working with Data Files

Lessons 12-15: Working With Data

3. September 7: GW: Chapters 1-13 4. September 14: GS: Lesson 22: One-Sample t test Lesson 23: Paired-Samples t test 5. September 21: GS: Lesson 24: Independent-Samples t test GW: Chapters 13-15: Analysis of Variance 6. September 28: GS: Lesson 25: One-Way Analysis of Variance (ANOVA)

GS: Lesson 26: Two-Way Analysis of Variance

7. October 5: GS: Lesson 26: Two-Way Analysis of Variance Review for Mid-Term Exam

8. October 12: Mid-Term Exam 9. October 19: GS: Lesson 28: One-Way Multivariate Analysis of Variance

(MANOVA)

10. October 26: GS: Lesson 29: One-Way Repeated-Measures Analysis of Variance (ANOVA)

11. November 2: GS: Lesson 30: Two-Way Repeated-Measures Analysis

of Variance 12. November 9: GS: Lesson 31: Pearson Product-Moment Correlation

Coefficient

13. November 16: GS: Lesson 32: Partial Correlations

14. November 23: GS Lesson 34: Multiple Linear Regression

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15. November 30: GS: Review for final exam Final Exam: December 7, 2008, 4:00-6:30 PM Course Assignments The assignments are located at the end of each chapter in the GS SPSS textbook. Homework assignments will be completed using the data contained on the data disk that accompanies the MJN text. These homework assignment and their due dates will be given throughout the semester. All assignment must be typed and submitted in the pocket of a folder. I suggest that you purchase several folders because later assignments may be due before prior ones have been returned. Each assignment must have a cover page that contains the course title, the assignment number, assignment title, your name and date. Each submitted assignment must also contain a copy of the questions for that assignment along with your answers. The completed assignments should be stapled and placed in the folder pocket and submitted to the instructor. Assignment 1

1) Students will learn how to navigate the SPSS tool bar to carry out basic functions such as open, close, save, cut, copy, save files, save and print output, navigate between edit view and variable view of the data editor. Students will also learn how to navigate between data editor (.sav), output (.spo), syntax (.sps), script (.sbs) SPSS windows.

: Navigate SPSS tool bar, develop data files, data entry & descriptive statistics.

2) Students will learn how to format data files in SPSS. This will include variables that represent nominal, ordinal or ordinal/ratio measurement scales. Students will learn how to format questions that have a single response or those that can have multiple responses.

3) Students will learn how to enter data into SPSS. 4) Students will learn how methods to check on the accuracy of data entered in SPSS. 5) Students will learn how compute fundamental descriptive statistics including, mean,

median, mode, standard deviation, variance, sum, range, standard error, minimum, maximum, and frequency distributions.

Assignment 2

1) Students will learn how to recode data for number of different purposes including recoding of questions that are reverse scored, and to transform an interval variable into an ordinal variable.

: Recode data for reversal scoring; compute means and sums for individual subjects when there are missing values.

2) Students will learn how to compute a composite score for a construct that results in a total score or a mean score. Particular instructions will be given regarding the processes for accommodating missing values.

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Assignment 3

1) Students will learn the types of research designs that are appropriate for a single-sample t test.

: Single-Sample t test (GS: Lesson 22, Exercises 1-6)

2) Students will learn the assumptions required to conduct a single-sample t test 3) Students will learn how to carry out a single-sample t test in SPSS. 4) Students will learn how to interpret the results of an SPSS output for single-sample t test 5) Students will learn how to write an APA results section for a single-sample t test.

a. Describe the test, the variables and the purpose of the test b. Give the alpha level of significance c. Report results of the statistical test along with effect size (Cohen’s d) if the

statistical results are significant. d. Report results in two decimal places beyond original units of the data used in the

statistical analysis. e. Report test value, degrees of freedom, and significance level f. Report a confidence intervals when possible, typically 95% confidence interval g. Report relevant descriptive statistics, such as mean and the standard deviation for

a single-sample t test. h. APA Publication Manual (2001) provide the following guidelines for choosing

among various reporting methods when reporting results; Use a single sentence for 3 or fewer number, a table for 4-20 numbers and a graph for more than 20 numbers.

i. Summarize the specific conclusions that can be reached on the basis of the analyses, but save interpretation and elaboration on these conclusions for a Discussion section. The hypotheses are not discussed in the results section, only the results of the analyses.

Assignment 4

1) Students will learn the types of research designs that are appropriate for a paired-sample t test.

: Paired-Sample t test (GS: Lesson 23, Exercises 1-8)

2) Students will learn the assumptions required to conduct a paired-sample t test 3) Students will learn how to carry out a paired-sample t test in SPSS. 4) Students will learn how to interpret the results of an SPSS output for an paired-sample t

test 5) Students will learn how to write an APA results section for a paired-sample t test.

Parts a thur i as in Assignment 4 An eta square (η2) may be computed as an alternative to d.

Assignment 5

1) Students will learn the types of research designs that are appropriate for an independent-sample t test.

: Independent-Sample t test (GS: Lesson 24, Exercises 1-10)

2) Students will learn the assumptions required to conduct an independent-sample t test 3) Students will learn how to carry out an independent-sample t test in SPSS.

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4) Students will learn how to interpret the results of an SPSS output for an independent-sample t test

5) Students will learn how to write an APA results section for an independent-sample t test. Parts a thur i as in Assignment 4 An eta square (η2) may be computed as an alternative to d.

Assignment 6

1) Students will learn the types of research designs that are appropriate for a between-subjects one-way Anova.

: Between-Subjects One-Way Analysis of Variance (ANOVA) (GS: Lesson 25, Exercises 4-6)

2) Students will learn the assumptions required to conduct a between-subjects one-way Anova

3) Students will learn how to carry out a between-subjects one-way Anova in SPSS. 4) Students will learn how to interpret the results of an SPSS output for a between-subjects

one-way Anova 5) Students will learn how to write an APA results section for a between-subjects paired-

sample t test. a. Describe the statistical test(s), the variables, and the purpose of the statistical

test(s). b. Describe the factor or factors, and label them as between-subjects or within-

subjects factor(s). c. Indicate the number of levels of each factor and the name of each level, e.g.,

different treatments. However, it is not necessary to report the number of levels and what the levels for factors with obvious levels such as gender.

d. Describe the dependent variable e. Give the alpha level of significance f. Report results of overall test(s).

i. Describe any decision about which test was chosen based on assumptions. ii. Report the test value and significance level. For multifactor designs,

report the statistic for each of the main and interaction effects and tell the reader whether the statistic is significant or not.

iii. Report statistics that allows the reader to make a judgment about the magnitude of the effect of each overall test, eta square (η2).

g. Report the descriptive statistics. Refer the reader to a table or figure that presents the relevant descriptive statistics. For a simple design with three groups, a table or figure may not be necessary. In this case the descriptive statistics can be given in text.

h. Describe and summarize the general conclusions of the analysis i. Report the results of the follow-up tests.

i. Describe the procedures used to conduct the follow-up tests that control of Type I error across the multiple tests. Explain any decision you made about the choice of tests based on their assumptions. For example, the multiple comparisons tests selected based on homogeneity of variance.

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ii. Summarize the results of the follow-up tests. It may be useful to present the results of the significant tests among pairwise comparisons with a table of means and standard deviations.

iii. Describe and summarize the general conclusions of the follow-up analyses.

j. Report the distribution of the dependent variable for levels of the factor(s) in a graph.

Assignment 7

1) Students will learn the types of research designs that are appropriate for a between-subjects one-way ANOVA

: Between-Subjects Two-Way ANOVA (GS: Lesson 26, Exercises 1-4)

2) Students will learn the assumptions required to conduct a between-subjects one-way Anova

3) Students will learn how to carry out a between-subjects one-way Anova in SPSS. 4) Students will learn how to interpret the results of an SPSS output for a between-subjects

one-way ANOVA 5) Students will learn how to write an APA results section for a between-subjects paired-

sample t test. a. Describe the statistical test(s), the variables, and the purpose of the statistical




d. Describe the dependent variable e. Give the alpha level of significance f. Report results of overall test(s).




g. Report the descriptive statistics. Refer the reader to a table or figure that presents the relevant descriptive statistics. For a simple design with three groups, a table or figure may not be necessary. In this case the descriptive statistics can be given in text.

h. Describe and summarize the general conclusions of the analysis i. Report the results of the follow-up tests.


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iii. Conduct interaction comparisons following a significant interaction. Describe the methods and procedures to evaluation significant interactions and the control for Type I error.

iv. Describe and summarize the general conclusions of the follow-up analyses.

j. Report the distribution of the dependent variable for levels of the factor(s) in a graph.

Assignment 8

1) Students will learn the types of research designs that are appropriate for a between-subjects one-way MANOVA

: Between-Subjects One-Way Multivariate Analysis of Variance (MANOVA) (Lesson 28, Exercises 1-4)

2) Students will learn the assumptions required to conduct a between-subjects one-way MANOVA

3) Students will learn how to carry out a between-subjects one-way MANOVA in SPSS. 4) Students will learn how to interpret the results of an SPSS output for a between-subjects

one-way MNOVA 5) Students will learn how to write an APA results section for a between-subjects one-way

MANOVA. a. Describe the statistical test(s), the variables, and the purpose of the statistical




d. Describe the dependent variables e. Give the alpha level of significance f. Report results of overall MANOVA test.

Report differences among the dependent measures. g. If MANOVA is significant, report the results of the univariate ANOVAs for each

of the dependent variables as follow-up tests to the MAMOVA. h. If ANOVAs are significant, then report post hoc analyses




i. Report the descriptive statistics. Refer the reader to a table or figure that presents the relevant descriptive statistics.

j. Describe and summarize the general conclusions of the analysis k. Report the results of the follow-up tests.

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l. Report the distribution of the dependent variable for levels of the factor(s) in a graph(s).

Assignment 9

1) Students will learn the types of research designs that are appropriate for a one-way repeated-measures ANOVA

: One-Way Repeated-Measures Analysis of Variance (ANOVA) (Lesson 29, Exercises 4-8)

2) Students will learn the assumptions required to conduct a one-way repeated-measures ANOVA

3) Students will learn how to carry out an one-way repeated-measures ANOVA in SPSS. 4) Students will learn how to interpret the results of an SPSS output for a one-way repeated-

measures ANOVA 5) Students will learn how to write an APA results section for a one-way repeated-measures

ANOVA. a. Describe the statistical test(s), the variables, and the purpose of the statistical




d. Describe the dependent variables e. Give the alpha level of significance f. Report results of overall MANOVA test.

Report differences among the dependent measures. g. If MANOVA is significant, report the results of the univariate ANOVAs for each

of the dependent variables as follow-up tests to the MAMOVA. h. If ANOVAs are significant, then report post hoc analyses



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i. Report the descriptive statistics. Refer the reader to a table or figure that presents the relevant descriptive statistics.

j. Describe and summarize the general conclusions of the analysis k. Report the results of the follow-up tests.





l. Report the distribution of the dependent variable for levels of the factor(s) in a graph(s).

Assignment 10

1) Students will learn the types of research designs and variables that are appropriate for Pearson Product-Moment correlations.

: Pearson Product-Moment Correlation Coefficient (Lesson 31, Exercises 5-8)

2) Students will learn the assumptions required to conduct Pearson Product-Moment correlations.

3) Students will learn how to carry out Pearson Product-Moment correlations 4) Students will learn how to interpret the results of an SPSS output for Pearson Product-

Moment correlations. 5) Students will learn how to write an APA results section for Pearson Product-Moment

correlations. a. Describe the statistical test(s), the variables, and the purpose of the statistical

test(s). b. Only interval/ratio measures can be used in Pearson Product-Moment

correlations. There are several exceptions where ordinal and dichotomous nominal variables can be used in Pearson Product-Moment correlations.

c. Describe the factor or factors. d. If multiple correlations are computed and reported as a set, then the Bonferroni

approach to control for Type I error should be used to adjust the alpha level. e. Give the alpha level of significance. f. Report the descriptive statistics. Refer the reader to a table or figure that presents

the relevant descriptive statistics. g. Report results of the Pearson Product Moment correlations.

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i. Report test value, significance and degrees of freedom ii. Report effect size, r2

h. Describe and summarize the general conclusions of the analysis Assignment 11

1) Students will learn the types of research designs and variables that are appropriate for partial correlations.

: Partial Correlations (Lesson 32, Exercises 5-8)

2) Students will learn the assumptions required to conduct partial correlations. 3) Students will learn how to carry out Pearson Product-Moment correlations 4) Students will learn how to interpret the results of an SPSS output for partial correlations. 5) Students will learn how to write an APA results section for partial correlations.

a. Describe the statistical test(s), the variables, and the purpose of the statistical test(s).

b. Describe the factor or factors c. If multiple correlations are computed and reported as a set, then the Bonferroni

approach to control for Type I error should be used to adjust the alpha level. d. Give the alpha level of significance. e. Report the descriptive statistics including means, standard deviation and zero

order correlations. Refer the reader to a table or figure that presents the relevant descriptive statistics.

f. Report results of the Pearson Product Moment correlations. i. Report zero order and partial correlations, significance and degrees of

freedom ii. Report effect size, r2 for partial correlations

g. Describe and summarize the general conclusions of the analysis Assignment 12

1) Students will learn the types of research designs and variables that are appropriate for a bivariate linear regression analysis.

: Bivariate Linear Regression (Lesson 33, Exercises 1-4)

2) Students will learn the assumptions required to conduct a bivariate linear regression analysis.

3) Students will learn how to carry out a bivariate linear regression analysis in SPSS. 4) Students will learn how to interpret the results of an SPSS output for a bivariate linear

regression analysis. 5) Students will learn how to write an APA results section for partial correlations.

a. Describe the statistical test(s), the variables (criterion & predictor variables) and the purpose of the statistical test(s).

b. Show the scatter plot of the two variables, to indicate that the two variables are linearly related.

c. Show the regression equation for predicting the criterion variable. d. Show the 95% confidence interval for the slope. e. Show the effect size, r2. f. Describe and summarize the general conclusions of the bivariate linear regression

analysis.

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Assignment 13

1) Students will learn the types of research designs and variables that are appropriate for a multiple linear regression analysis.

: Multiple Linear Regression (Lesson 34, Exercises 1-4)

2) Students will learn the assumptions required to conduct a multiple linear regression analysis.

3) Students will learn how to carry out a multiple linear regression analysis in SPSS 4) Students will learn how to interpret the results of an SPSS output for a multiple linear

regression analysis. 5) Students will learn how to write an APA results section for a multiple linear regression

analysis. a. Describe method of multiple linear regression (stepwise, forward, backward,

enter, remove) computed, the variables (criterion & predictor variables) and the purpose of the statistical analysis.

b. Report descriptive statistics (means, standard deviations & bivariate correlations). i. If there are only a few bivariate correlations (3 or fewer) they can be listed

in text (e.g., r(24) = .40, p < .01). ii. If there are more than three bivariate correlations it is best to summarize

these statistics in a table, typically the lower triangle of the correlation matrix. Means and standard deviations can also be presented in the table.

c. Method: Enter i. Report the overall strength of the relationship between the predictors and

the criterion as well as the results of the overall significance test. ii. Report R2 and adjusted R2.

iii. Report the standard error if the dependent variable (criterion) has a meaningful metric.

d. Method: Stepwise, forward & backward i. Report the overall strength of the relationship between the predictors and

the criterion as well as the results of the overall significance test. ii. Report R2 and adjusted R2.

iii. Report the standard error if the dependent variable (criterion) has a meaningful metric.

iv. If multiple sets of predictors are evaluated, report the changes in R2 and the significance tests associated with those changes in R2.

e. Report the contribution of the individual predictors, i. Consider relevant statistics to evaluate the relative importance of each

predictor. The bivariate correlations, the partial correlations, and the standardized regression coefficient might be presented.

ii. Report whether the individual variables make a significant contribution to the predictor equation (e.g., t(98) = -3.13, p > .01).

f. Describe the specific research conclusions that should be drawn from the regression analysis.

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Teaching Methods Lectures, Demonstrations, and Discussions Course Evaluation Evaluation Rubrics Assignment 1: Completed assignment and print 1) copy of code book (file information), 2) copy of data entered, and 3) frequency distribution of all variables entered into the data file.

Psychological Statistics, Measurement & Evaluation, PSY 6206-001 Fall 2008

Assignment 1

Name__________________________________ Date_______________________ Total Score:________ ______(30 pts) 1. Copy of codebook Comment: ______(30 pts) 2. Copy of data Comments: ______(40 pts) 3. Copy frequencies Nominal and Ordinal variables and means and standard

deviations for interval variables. Comments: Assignment 2: Competed assignment and print 1) Recoded data with reversal scoring, 2) compute mean scores, 3) sum scores, and 4) copy of SPSS program for computing means and sums. Each component is worth 25%

Psychological Statistics, Measurement & Evaluation, PSY 6206-001 Assignment 2, Fall 2008

Name__________________________________ Date_______________________ Total Score:________ ______(40 pts) 1. Data with recoding for HIV/AIDS Knowledge Questionnaire. Comments:

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______(30 pts) 2. Data with MEAN scores with replacement; must have a minimum of 16 valid scores to compute mean scores

Comments: ______(30 pts) 3. Data with TOTAL scores with replacement; must have a minimum of 16

valid scores to compute mean and total scores Comments: Assignments 3-13 Compute assignment and print 1) SPSS statistical analyses output, 2) answer all questions, And 3) Write APA results section.

The purpose of ALCs is to establish policy guidelines and procedures for universities, through their boards of trustees, to develop and implement Academic Learning Compacts to account for student achievement in baccalaureate degree programs in the State University System. The ALCs at Florida A&M University can be found at

Parts 1 & 2 are worth 25% each, and part 3 is worth 50%

Psychological Statistics, Measurement & Evaluation, PSY 6206-001 Assignments 3-13_____, Fall 2008

Name__________________________________ Date_______________________ Total Score:________ ______(25 pts) 1. SPSS statistical analyses output Comments: ______(25 pts) 2. Answers to Questions Comments: ______(50 pts) 3. APA results section Comments: Academic Learning Compact (ALCs) The Academic Learning Compacts (ALCs) are guidelines issued by the State University

System requiring each State University in Florida to identify, by academic program, what it is that students will have learned by the end of the baccalaureate degree program, and how that learning will be measured above and beyond course grades.

http://www.famu.edu under Assessment.

http://www.famu.edu/�

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Grading There will be two exams: Percentages Points A mid-term, and a comprehensive final exam: 20%@ 470@ 40% 940 13 Homework Assignments: 100@ 60% 1300 TOTAL 100% 2240 After the final points are computed, the letter grade will be assigned as indicated below: Points Percentages A – 2006-2240 90 – 100 B – 1772-2005 80 – 89 C – 1538-1771 70 – 79 D – 1214-1537 60 – 69 F – 0-1213 0 – 59

Examination Scheduled: Mid-Term Exam: October 12, 2008 Final Comprehensive Exam: December 7, 2008 Other Required Materials

Calculator: You will need a calculator for: adding, subtracting, multiplying, dividing, squaring, and obtaining square roots. You may NOT use the calculator on your cell phone or on your computer for exams or other graded work. During exams other graded work, you may NOT borrow a calculator. If you do not bring a calculator, you must complete the assignment without one. You can purchase a satisfactory calculator for about $5.00 or less. However, it is recommended that you purchase a Texas Instruments TI-30XA scientific calculator. This can be purchased in the University Bookstore for $15.00.

Homework Assignments

All assignment must be typed and submitted in the pocket of a folder. I suggest that you purchase several folders because later assignments may be due before prior ones have been returned. Each assignment must have a cover page that contains the course title, the assignment number, assignment title, your name and date. Each submitted assignment must also contain a copy of the questions for that assignment along with your answers. The completed assignments should be stapled and placed in the folder pocket and submitted to the instructor.

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Assignment Due Date Introduction August 23 Assignment 1: Data Entry, Code Book and Frequency August 31 Assignment 2: Reverse Coding and Computing Descriptive Statistics September 7 Assignment 3: GS: Lesson 22-Exercises: 1-6 (single-sample t test) September 14 Assignment 4: GS: Lesson 23-Exercises: 1-8 (Related-sample t test) September 28 Assignment 5: GS: Lesson 24-Exercises: 1-10 (Indenp.-sample t test) October 5 Assignment 6: GS: Lesson 25-Exercises: 4-6 (One-Way ANOVA) October 12 Assignment 7: GS: Lesson 26-Exercises: 1-4 (Two-Way ANOVA) October 19 Assignment 8: GS: Lesson 28-Exercises: 51-4 (MANOVA) October 26 Assignment 9: GS: Lesson 29-Exercises: 5-8 (One-Way November 2

Repeated-Measures ANOVA)

Assignment 10: GS: Lesson 30: Two-Way Repeated-Measures November 9 Analysis of Variance Assignment 11: GS: Lesson 31-Exercises: 5-8 (Pearson November 16

Product-Moment Correlation Coefficient)

Assignment 12: GS: Lesson 32-Exercises: 5-8 (Partial Correlation) November 23 Assignment 13: GS: Lesson 34-Exercises: 1-4 November 30

(Multiple Linear Regression)

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Bibliography

Howell, David, C. (2002). Statistical Methods for Psychology, 5th Edition. Pacific Grove, CA: Duxbury.

Pagano, Robert, R. (2001). Understanding statistics in the behavioral sciences, 6th

Edition. Stamford, CT: Wadsworth. Minium E., King, M. & Gear, G. (1993). Statistical reasoning in psychology and education. New York: John Wiley & Sons. Vogt, W. (1993). Dictionary of statistics and methodology: An nontechnical guide for the social sciences. Newbury Park, CA: Sage Publications. McCall, R., (1990). Fundamental statistics for behavioral sciences (5th ed.). New York: Harcourt Brace Jovanovich. Sidman, M. (1988). Tactics of scientific research: Evaluating experimental data in psychology. Boston: Authors Cooperative, Inc, Publishers. Hayes, W. (1994). Statistics (5th ed.). New York: Holt, Rinehart and Winston. Yaremko, R., Harari, H., Harrison, R. & Lynn, E. (1986). Handbook of research and quantitative methods in psychology: For students and professionals. Hillsdale, NJ: Lawrence Erlbaum Associates. Nesselroade, J. & Cattell, R. (Eds.). (1988). Handbook of multivariate experimental psychology (2nd ed.). New York: Plenum Press. Tabachnick, B. & Fidell, L. (1996). Using multivariate statistics, Third edition. New York: Harper/Collins. Couch, J. (19987). Fundamentals of statistics for the behavioral sciences (2nd ed.). St. Paul, MN: West Publishing Company. Cook, T. & Campbell, D. (1979). Quasi-experimentation: Design and analysis issues for field settings. Boston: Houghton Mifflin. Green, S., Salkind, N. & Akey, T. (1997). Using SPSS for windows: Analyzing and understanding data. Upper Saddle River, New Jersey: Prentice Hall. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

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Spatz, C. (1993). Basic statistics: tables of distributions (5th ed.). Pacific Grove, CA: Brooks/Cole Publishing Company. Diekhoff, G. (1992). Statistics for the social and behavioral sciences: Univariate, bivariate, multivariate. Dubuque, IA: Wm. C. Brown Publishers. Heiman, G., (1992). Basic statistics for the behavioral sciences. Boston: Houghton Mifflin Company. Berstein, I., Garbin, C. & Teng, G. (1988). Applied multivariate analysis. New York: Springer-Verlag. Keppel, G. & Zedeck, S. (1989). Data analysis for research designs: analysis of variance and multiple regression/correlation approaches. New York: W.H. Freeman and Company. Brook, R. & Arnold, G. (1985). Applied regression analysis and experimental design. New York: Marcel Dekker. Dillon, W. & Goldstein, M. (1984). Multivariate analysis: Methods and applications. New York: John Wiley & Sons. Ashworth, P., Giorgi, A. & Koning, A. (Eds.). (1986). Qualitative research in psychology: Proceedings of the International Association for Qualitative Research in Social Sciences. Pittsburgh, PA: Duquesne University Press. Aiken, S. & West, S. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Publications. Runyon, R., Haber, A., Pittenger, D. & Coleman, K. (1996). Fundamentals of behavioral statistics (8th ed.). New York: McGraw-Hill.

Marascuilo, L.A. & Sirlin, R.C. (1998). Statistical methods for the social and behavioral

sciences. New York: W.H. Freeman and Company. Hinkle, D.E., Wiersma W. & Jurs, S.G. (1998). Applied statistics for the behavioral

sciences, 4th Edition. New York: Houghton Mufflin Company.

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