part 1: course information - msu psychology state university page 1 part 1: course information...

Department of Psychology MA in Program Evaluation PSY 887: Statistics for Evaluators II

Summer 2017 Syllabus

Michigan State University

Part 1: Course Information

Instructor Information

Instructor: Marisa Beeble, Ph.D.

Office Hours: Online office hours will be held by appointment in the

chat room accessible via our Desire2Learn course

page, or by telephone. Please email me to schedule

an appointment.

Telephone: (517) 927-6321

E-mail: [email protected]; you can expect a response

within 24 hours.

Course Prerequisite

Prior to taking this course, students must successfully complete PSY 883,

Evaluation Statistics I.

Course Description

Students in this course will learn about inferential statistics and

quantitative data analysis in an evaluation context. This course will

build upon material from Evaluation Statistics I. The course will cover

parametric and nonparametric statistics and will introduce students to

other statistical tools and data analysis issues, such as handling non-

normal and missing data, calculating the magnitude of an effect, and

determining the sample size needed for specific types of analyses.

Students will build practical skills in conducting, interpreting and

reporting corresponding quantitative data analyses in SPSS.

Course Materials

Textbooks

Field, A. (2013). Discovering statistics using IBM SPSS statistics. (4th ed.).

Thousand Oaks, CA: Sage Publications, Inc.

Pett, M.A. (2016). Nonparametric statistics for health care research:

Statistics for small samples and unusual distributions (2nd ed.).

Thousand Oaks, CA: Sage Publications, Inc.

mailto:[email protected]

PSY 887: Statistics for Evaluators II Summer 2016 Syllabus


Course Pack

Supplemental readings are available in the PSY 887 Electronic Course

Pack, available for $29.58 at:

https://noncredit.msu.edu/listSections.action?catalogid=27&offeringid

=1259

Software

IBM. SPSS statistics premium gradpack version 23 or 24[computer

software]. Armonk, NY: IBM.

Please note: PSY 887 will use the same SPSS version as PSY 883. If for

some reason you need to purchase software for PSY 887, please use

one of the following IBM recommended distributors:

creationengine.com www.hearne.software

journeyed.com onthehub.com

studica.com studentdiscounts.com

thinkedu.com

Course Requirements

For the duration of this course you must have regular access to:

A high-speed (broadband) internet connection

A computer manufactured within the last four years

A minimum screen resolution of 1024x768

Desire2Learn

Course Structure

All of the course content and assignments will be delivered entirely

online through the course management system, Desire2Learn (D2L).

Using your MSU NetID you can login to the D2L home page located at

http://D2L.msu.edu.

This course contains 14 modules, each of which will be covered over

the course of one week. Each week the module will open at 5:00 pm

Eastern Standard Time (EST) on Friday for the following week.

Assignments may be completed and submitted at any time during the

week they are due; however, all assignments must be submitted no

later than 11:55 pm (EST) on Sunday evening during the week they are

due. All assignments will be submitted via D2L. Modules previously

covered will remain open throughout the semester.

https://noncredit.msu.edu/listSections.action?catalogid=27&offeringid=1259

https://noncredit.msu.edu/listSections.action?catalogid=27&offeringid=1259

http://www.creationengine.com/html/pl.lasso?s=spss+grad+pack

http://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/Editions?cm_mc_uid=22327917269214818167804&cm_mc_sid_50200000=1482768718



http://onthehub.com/download/software-discounts/spss-statistics/

http://www.studica.com/IBMSPSS

http://www.studentdiscounts.com/

https://store.thinkedu.com/categories.aspx?Keyword=spss+student

http://d2l.msu.edu/



Within each module, the Roadmap outlines exactly what you should

do to complete that module and in what order. Each module has the

following components: Overview and Objectives, Readings and

Resources, and Assignments and Activities.

The Overview and Objectives introduce the module content and

outline the knowledge and skills you should have acquired upon

completion of the module. The objectives align with the tasks that

masters’ level evaluators would be expected to perform in a real

evaluation setting, and they reflect the content areas that will be

assessed in the course. After completing the module readings and

lectures, look back at the objectives before attempting any graded

assignments to make sure you have mastered the material.

Modules also have a section titled Readings and Resources. This

section contains the module’s Lecture Library, which is a collection of

videos that cover key material, provide examples of concepts, and

expand upon important issues. The lectures build upon each other and

therefore should be watched in the order in which they appear in the

library. When appropriate, additional resources (e.g., links to online

statistical calculators) will be provided to you in the Readings and

Resources section of the module.

Each module also has a section labeled Activities and Assignments.

This section contains all of the graded assignments for each module. A

variety of assessment strategies will be used in this course, including:

Concept Checks – These are graded, timed quizzes to assess your

understanding of the module’s material. Concept checks also give

you a sense of how you will be tested on the exams and whether or

not you have mastered the module’s material.

Brief Activities – These are small scale assignments that require you

to apply the material you learned from the reading materials and

lectures. These activities may involve case examples, discussion

boards, and/or short-answer questions.

Application Assignments - Some modules may include longer,

graded assignments that will require you to apply specific skills you

have developed in the course. This may include responding to

questions and often will require manipulating and/or analyzing data

in SPSS. Application assignments may also entail reporting results



from statistical analyses to hypothetical audiences with or without

statistical knowledge.

Examinations – There will be two non-cumulative examinations in this

course, a midterm and a final. Each of these exams will be timed to

assess your abilities to recall and apply the course material from the

first and second halves of the semester, respectively. You will only

have one attempt to complete these exams. Questions may be

multiple choice, short answer, and long answer.

See the Course Schedule later in the syllabus for module topics for

each week, as well as the associated course readings and

assignments. Detailed instructions for each assignment can be found in

D2L within each learning module. If you have any questions, please

contact the instructor.

Technical Assistance

If you need technical assistance at any time during the course or to

report a problem you can:

Visit the Distance Learning Services Support Site

Visit the Desire2Learn Help Site

Please note: Accommodations for assignments not submitted on time

due to technical difficulties will require documentation from the

helpdesk.

https://www.lib.msu.edu/dls/onlineoffcampus/

http://help.d2l.msu.edu/



Part 2: Course Objectives

Learning Objectives

Upon completion of this course, students will be able to:

1. Apply ethical standards to quantitative data management, analysis, and

reporting

2. Understand the logic, limitations, and application of inferential and

nonparametric statistics in an evaluation context

3. Identify specific evaluation scenarios in which you would use t-tests,

ANOVAs, and various nonparametric statistics, and conduct them in SPSS

4. Interpret and report the results of t-tests, ANOVAs, and various

nonparametric statistics

5. Calculate the effect size of an intervention

6. Conduct a power analysis to determine sample size or power to detect an

intervention effect

7. Address missing and non-normal data

8. Given an evaluation scenario, choose and execute the appropriate

analytic technique

Core Competencies

Program evaluation involves planning, collecting data, analyzing and

interpreting the data, and communicating and utilizing evaluation findings. The

figure below illustrates the skills you will build in this course, as they apply to the

process of conducting an evaluation.

Evaluation

Planning

Collecting Data Analyzing and

Interpreting

Communicating

and Utilization

• Select

appropriate

statistics for an

evaluation

• Conduct power

analysis for

determining

sample size

requirements

• Prepare data

for analysis

• Conduct

statistical tests

• Interpret test

results

• Determine

effect size

• Conduct

power analysis

• Report

statistical test

results to

technical and

non-technical

evaluation

audiences



Part 3: Course Schedule

Date Topic Module Requirements Due

Date

UNIT 1: ETHICS AND INTEGRITY IN STATISTICS

Week 1

5/15 – 5/21

Ethics and

Integrity in

Managing,

Analyzing, and

Reporting

Statistics

Watch: Lecture 1(An Introduction to Ethics)

Watch: Lecture 2 (AEA Guiding Principles)

Complete: Apply Guiding Principles to an Evaluation Context

Watch: Lecture 3 (Data Management, Analysis, & Reporting)

Read: Morris Chapters 5 & 6, pg. 117 - 170

Read: Marco & Larkin, pg. 691 - 694

Complete: Ethics and Data Reporting Discussion

Complete: Concept Check 1

5/21

UNIT 2: PARAMETRIC STATISTICS

Week 2

5/22 – 5/28

Comparing

Group Means:

Independent

Samples t-test

Watch: The Importance of Using Syntax

Read: Field Chapter 9, pg. 357 – 368

Watch: Lecture 1 (Concept and Assumptions)

Watch: Lecture 2 (Mechanics)

Watch: Demonstration 1 (Setting up an SPSS File)

Read: Field Chapter 9, pg. 371 - 378

Watch: Demonstration 2 (Independent Samples t-test)

Watch: Lecture 3 (Interpreting and Reporting Results)

Watch: Demonstration 3 (Producing a Simple Bar Graph)


Complete: Entering Data for an Independent Samples t-test

Complete: Reporting Results to Academic Audiences

Complete: Running and Presenting Results to Evaluation Stakeholders

5/28

Week 3

5/29 – 6/4

Comparing

Group Means:

One Way

ANOVA




Watch: Lecture 2 (Follow Up Tests)


Watch: Demonstration 1(One-Way ANOVA w/ Post hoc Tests)


Watch: Demonstration 2 (One-way ANOVA w/ Planned

Comparisons)



Complete: Running and Reporting Results to Evaluation Stakeholders

Complete: Running and Reporting Results to Academic Audiences

6/4

Week 4

6/5 – 6/11

Comparing

Group Means:

Two Way

ANOVA

and

Conceptual




Watch: Demonstration 1 (Factorial ANOVA w/ Planned Contrasts)

Watch: Demonstration 2 (Simple Effects Analysis)


6/11




Date

Introduction to

MANOVA


Watch: Lecture 3 (Conceptual Introduction to MANOVA)



Complete: Reporting Results to Evaluation Stakeholders

Week 5

6/12 – 6/18

Analyzing

Repeated

Measures

Data: Related

Samples t-test



Watch: Demonstration 1 (Setting up an SPSS File)


Watch: Demonstration 2 (Related Samples t-test)



Complete: Entering Data for a Related Samples t-test


6/18

Week 6

6/19 – 6/25

Analyzing

Repeated

Measures

Data:

Repeated

Measures

ANOVA




Watch: Demonstration 1 (One-Way Repeated Measures ANOVA)




Complete: Reporting Results to Evaluation Stakeholders

6/25

Week 7

6/26 – 7/2

Midterm Examination

7/2

UNIT 3: EFFECT SIZE AND POWER

Week 8

7/3 – 7/9

Determining

Effect Sizes

Read: Ferguson, pg. 532 – 538


Watch: Lecture 1 (Concept of Effect Size)

Watch: Demonstration 1 (Independent Samples t-test Effect Size)

Watch: Demonstration 2 (One-way ANOVA Effect Size)

Watch: Demonstration 3 (Factorial ANOVA Effect Size)

Watch: Demonstration 4 (Related Samples t-test Effect Size)

Watch: Demonstration 5 (Repeated Measures ANOVA Effect Size)

Read: Calculating Omega Squared (Available in D2L)

Read: Reporting Effect Size Results (Available in D2L)


Complete: Calculate and Interpret Eta Squared and r Estimates

Complete: Compute and Report Omega Squared and r Estimates

Complete: Produce and Interpret Partial Eta Squared and r Estimates

7/9

Week 9

7/10 – 7/16

Statistical

Power

Read: Cohen, pg. 1 -17

Watch: Lecture 1 (Concept of Statistical Power)

Read: Faul et al., pg. 175-183

Watch: Demonstration 1 (Independent Samples t-test in G*Power)

Watch: Demonstration 2 (One-way ANOVA in G*Power)

Watch: Demonstration 3 (Factorial ANOVA in G*Power)

7/16




Date

Watch: Demonstration 4 (Related Samples t-test in G*Power)

Watch: Demonstration 5 (Repeated Measures ANOVA in G*Power)

Watch: Supplement to Demonstration 5 (Repeated Measures

ANOVA in G*Power)

Read: Writing up Power Analysis Results (Available in D2L)


Complete: Conducting A Priori Power Analysis

Complete; Determining Sample Size from Pilot Test Data

Complete: Determining Sample Size from Existing Literature

UNIT 4: NONPARAMETRIC STATISTICS

Week 10

7/17 – 7/23

Nonparametric

Statistics:

Comparing

Independent

Samples

Watch: Lecture 1 (An Introduction to Nonparametric Statistics)

Read: Pett Chapter 11, pg. 397 – 400

Read: Pett Chapter 7, pg.152 – 198

Watch: Lecture 2 (Nonparametric Statistics for Two Independent

Groups)

Watch: Demonstration 1 (Fisher’s Exact and Chi-square Tests)

Watch: Demonstration 2 (Mann-Whitney U Test)


Watch: Lecture 3 (Nonparametric Statistics for 3 or More

Independent Groups)

Watch: Demonstration 3 (Kruskal Wallis One-Way ANOVA Test by

Ranks)

Read: Reporting Results (Available in D2L)




Complete: Interpreting Dunn Multiple-Comparisons Procedure

7/23

Week 11

7/24 – 7/30

Nonparametric

Statistics:

Comparing

Related

Samples


Watch: Lecture 1 (Nonparametric Statistics for Two Related Samples)

Watch: Demonstration 1 (McNemar Test)

Watch: Demonstration 2 (Wilcoxon Signed-Ranks Test)


Watch: Lecture 2 (Nonparametric Statistics for 3 or More Related

Groups)

Watch: Demonstration 3 (Cochran’s Q Test)

Watch: Demonstration 4 (Friedman Test)

Read: Reporting Results (Available in D2L)




Complete: Running and Interpreting Results from the Wilcoxon

Signed-Ranks Test

7/30



UNIT 5: DATA ANALYSIS ISSUES

Week 12

7/31 – 8/6

Data Analysis

Issues: Dealing

with Non-

Normal Data



Watch: Lecture 1 (An Introduction to Non-Normal Data)

Watch: Demonstration 1 (Assessing for Normality Video 1)

Watch: Demonstration 2 (Assessing for Normality Video 2)

Review: A Quick Guide on How to Assess for Normality in SPSS

Read: Reporting on Statistical Tests of Normality

Watch: Lecture 2 (Remedying Non-Normal Data)

Watch: Demonstration 3 (Applying Data Transformations)


Complete: Assessing for Normality using Descriptive Statistics and

Graphs

Complete: Running and Interpreting Statistical Tests of Normality

Complete: Applying Transformations to Non-Normal Data

8/6

Week 13

8/7 – 8/13

Data Analysis

Issues: Dealing

with Missing

Data

Read: McKnight et al. Chapter 3, pg. 40 – 64

Watch: Lecture 1 (An Introduction to Missing Data)

Watch: Lecture 2 (Diagnosing and Remedying Missing Data)

Watch: Demonstration 1 (Little’s MCAR Test)


Complete: Preventing Missing Data by Design

8/13

Week 14

8/14 – 8/18

Final Examination

8/18

Part 4: Grading Policy

Graded Course Activities

Your grade for this course will be based on your performance on the

following assignments. The table below shows the maximum number of

points you can earn for each assignment.

Course Requirements % of Total

Grade

Points

Concept Checks (12 x 5 points each) 15 60

Brief Activities (14 X 5 points each) 17.5 70

Application Assignments (15 X 10 points each) 37.5 150

Midterm Examination 15 60

Final Examination 15 60

Possible Total 100% 400

Viewing Grades

Grades will be available within one week of the due date of an



assignment, unless otherwise specified by the instructor. You can view

your grades for all assignments in the gradebook available in D2L.

Grading Scale

Final grades are determined based on your mastery of the course

materials and demonstration of the required skills as determined by

professional standards at the graduate level. Final grades will be

calculated based upon the total number of points you have

accumulated across the semester, using the following grading scale.

Part 5: Course and University Policies

Late Work Policy

It is in your best interest to turn all work in on time, as no late

assignments will be accepted. An assignment is considered late if it is

submitted after 11:55 pm (EST) the date the assignment is due.

Exceptions to this policy will be made at the discretion of the instructor,

and only in the case of a documented emergency situation that was

reported in advance of an assignment due date.

Participation

Students whose names do not appear on the official class list for this

course may not participate in this class. Students who fail to log-in

during the first week will be dropped from the course.

You are expected to participate in all online activities as listed on the

course schedule. If you miss more than two consecutive weeks of class,

(i.e., do not participate actively in class activities or assignments) and

have not communicated with the instructor to be excused from class,

you will receive a failing grade of 0.0 in the course.

Total Points Percent of Total Points Grade

360 – 400 90% to 100% 4.0

340 – 359 85% up to 90% 3.5

320 – 339 80% up to 85% 3.0

300 – 319 75% up to 80% 2.5

280 – 299 70% up to 75% 2.0

260 – 279 65% up to 70% 1.5

240 – 259 60% up to 65% 1.0

< 240 Less than 60% 0.0



If you have an emergency situation, the instructor must be contacted

prior to an assignment due date to make alternative arrangements.

Otherwise, you will receive a 0.0 for the missed assignment(s). As noted

above, decisions about whether or not an assignment will be

accepted is left to the discretion of the instructor.

Understand When You May Add or Drop This Course

It is your responsibility to understand when you need to consider un-

enrolling from a course. Refer to the Michigan State University Office of

the Registrar for important dates and deadlines.

The last day to add this course is the end of the first week of classes

(5/19 at 8:00 pm EST). The last day to drop this course with a 100

percent refund and no grade reported is 6/7 (at 8:00 pm EST). The last

day to drop this course with no refund and no grade reported is 6/30

(at 8:00 pm EST). You should immediately make a copy of your

amended schedule to verify you have added or dropped this course.

Inform Your Instructor of Accommodations Needed

Michigan State University is committed to providing equal opportunity

for participation in all programs, services and activities. If you have a

documented disability and verification from the Resource Center for

Persons with Disabilities (RCPD), and wish to discuss academic

accommodations, please contact your instructor as soon as possible. It

is the student’s responsibility to provide documentation of disability to

RCPD and meet with an RCPD specialist to request special

accommodation before classes start.

Once your eligibility for an accommodation has been determined, you

will be issued a verified individual services accommodation (“VISA”)

form. Please present this form to the instructor at the start of the term

and/or two weeks prior to the accommodation date (test, project,

etc). Requests received after this date will be honored whenever

possible.

RCPD may be contacted by phone at (517) 884-7273 (884-RCPD), or

via their website (http://www.rcpd.msu.edu). RCPD is located in 120

Bessey Hall, near the center of the Michigan State University campus,

on the southwest corner of Farm Lane and Auditorium Road.

http://www.reg.msu.edu/

http://www.reg.msu.edu/

https://www.rcpd.msu.edu/



http://www.rcpd.msu.edu/



Commit to Integrity

Academic Honesty

Article 2.3.3 of the Academic Freedom Report states that "The student

shares with the faculty the responsibility for maintaining the integrity of

scholarship, grades, and professional standards." In addition, the

Psychology Department adheres to the policies on academic honesty

as specified in General Student Regulations 1.0, Protection of

Scholarship and Grades; the all-University Policy on Integrity of

Scholarship and Grades; and Ordinance 17.00, Examinations. (See

Spartan Life: Student Handbook and Resource Guide and/or the MSU

Web site: www.msu.edu.)

Academic integrity is a minimal expectation of this course. Academic

dishonesty in any form will not be tolerated. Academic dishonesty

includes, but is not limited to, cheating, plagiarizing, fabricating

information or citations, facilitating acts of academic dishonesty by

others, and submitting work of another person. Any student involved in

academic dishonesty will be reported to the Office of Academic

Affairs and the Office of Student Affairs and a grade of 0.0 may be

issued for the course.

Lectures and other course materials must remain the property of the

Department of Psychology and must not be copied from the internet

for distribution to anyone who is not registered for this course. Online

discussions and exercises are confidential and should not be discussed

with others who are not enrolled in the class.

It is important for each course participant to express his/her ideas. All

ideas need to be respected in discussions and exercises. Any “group

projects” that are required, still require individual work as a minimal

expectation.

All assignments are to be done on your own, without the assistance of

additional materials, i.e., internet, texts, articles, other people, etc.,

unless you are instructed to do otherwise. This includes weekly

assignments and exams.

Plagiarism

Taking credit for someone else’s work or ideas, submitting a piece of

work (for example, a paper, assignment, discussion post) which in part

or in whole is not entirely your own work without fully and accurately

attributing those same portions to their correct source. This includes

http://splife.studentlife.msu.edu/academic-freedom-for-students-at-michigan-state-university

http://splife.studentlife.msu.edu/

http://www.msu.edu/

http://www.msu.edu/

http://www.msu.edu/



information taken from the Internet.

Unless authorized by their instructors, you are expected to do your own,

original work on each assignment in each class. If you recycle your

own course work from one class to another, you may face an

allegation of academic dishonesty. If your instructor believes you have

committed an act of plagiarism, he/she may take appropriate action,

which includes the issuing of a “penalty grade” for academic

dishonesty. Article 11 of the Academic Freedom Report for Students at

Michigan State University, or the “AFR,” defines a penalty grade as “a

grade assigned by an instructor who believes a student to have

committed academic dishonesty. . . .” A penalty grade can include,

but is not limited to, a failing grade on the assignment or in the course.

For examples of what constitutes plagiarism, see:

Indiana University Writing Tutorial Services

Purdue Online Writing Lab

Evaluate the Course

Michigan State University takes seriously the opinion of students in the

evaluation of the effectiveness of instruction, and has implemented

the SIRS (Student Instructional Rating System) process to gather

student feedback. This course utilizes the “online SIRS” system, and you

will receive an e-mail sometime during the last two weeks of class

asking you to fill out the SIRS at your convenience. As a reminder to be

sure to fill out the SIRS evaluation form, the final grade for this course

will not be accessible on STUINFO during the week following the

submission of grades for this course unless the SIRS online form has

been filled out. You have the option on the online SIRS form to decline

to participate in the evaluation of the course – we hope, however,

that you will be willing to give us your frank and constructive feedback

so that we may instruct students even better in the future.”

Note: The instructor reserves the right to make changes to the syllabus

during the course of the semester. Changes will be announced in the

course announcement area.

http://splife.studentlife.msu.edu/academic-freedom-for-students-at-michigan-state-university/article-11-definitions

http://www.indiana.edu/~wts/pamphlets/plagiarism.shtml

http://owl.english.purdue.edu/owl/resource/589/01/

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