PMean: Syllabus for Introduction to SPSS, Fall semester 2017

I am teaching a class, Introduction to SPSS (MEDB 5506). Here is the syllabus for Fall Semester 2017.

MEDB 5506: Introduction to SPSS

1. Introduction

A working knowledge of statistical software is a vital skill for anyone involved in quantitative research.  This class will introduce data management, simple descriptive statistics, and basic graphical display using the SPSS software package. Students will develop the fundamental skills needed to prepare data sets for analysis, and to conduct simple descriptive and graphic analyses and report those analyses.

1.1 Course content

This on-line course is intended to provide a working familiarity with SPSS. Students are not expected to have advanced programming or statistical analysis skills. A basic understanding of statistical terminology is necessary. The class will introduce basic methods for data import, data management, simple graphics, and basic statistical analysis. This class will not cover advanced statistical methods, but will provide you with a firm foundation to address these areas in your statistics classes or in your thesis/dissertation research.

1.2 Student learning objectives

At the completion of this course, students will be able to:

  •  Prepare and manipulate datasets for analysis in SPSS.
  • Conduct simple descriptive and graphic analyses of data in SPSS.
  • Prepare a report with a summary of analyses conducted in SPSS.

1.3 Course framework

This class will be taught as a self-paced, asynchronous online course. The instructor will provide datasets and instructions for running various functions within SPSS. You will apply these functions to import datasets, manipulate these datasets, and produce basic summaries of these datasets. As the final project of the class, you will produce an independent analysis on a dataset of your own choice that demonstrates the techniques and skills that were covered in the course.

2. Instructor

Steve Simon, PhD
Department of Biomedical and Health Informatics
School of Medicine, M5-117

I am very grateful for the work of Karen Williams who developed the outline for this class, the homework assignments, and the videos. As I find time, I will update some of this material, mostly to align it more closely with two other classes: MEDB 5505, Introduction to R; and MEDB 5507, Introduction to SAS. These updates will be optional viewing for Fall Semester 2017.

2.1 Email

My email contact information is at The preferred email address is the UMKC account, but any of these email addresses are fine. I do not check email on my days off from work, so please don’t worry if you don’t get an immediate response. If you have not heard back by 48 hours, please feel free to contact me again.

2.2 Telephone

You are also welcome to call me. My office number is 816-235-6617 and my cell phone number is 913-912-2076.

2.3 Discussion forum

Before you contact me by email or telephone, consider posting your problem or question on the Blackboard discussion board. Others will benefit from the exchange. You are welcome, however, to discuss problems and ask questions via email or telephone if you prefer.

2.4 Office hours

You can get help for many of your questions by email, but sometimes a face-to-face appointment is needed. I am part-time at UMKC and hold two other part-time jobs, so I cannot hold regular office hours. I am more than happy to meet with you face-to-face by appointment. Because of child care responsibilities, I generally cannot meet prior to 9am (10am on Thursdays) and I have to leave most days by 2pm. I have a lot more flexibility for meeting via telephone.

3. Class structure

This class will be taught as a self-paced, asynchronous online course.  The course material is presented in five parts, with 4 assignments that will be submitted to the instructor. In order to achieve learning objectives, students are expected to review provided course materials, including recorded lectures.

During the lectures, the instructor will provide examples to demonstrate application of SPSS. Students should open SPSS on their own and replicate the work shown in the video. Then students will be asked to conduct similar work on a different dataset and turn that in as homework. Students will have access to the SPSS program either directly on a computer that has SPSS installed or through UMKC Remote Labs ( For those students who wish to purchase the student version of SPSS for their private computer, it can be ordered from the UMKC Bookstore at the cost of $110.   As the final project of the class, students will independently apply these skills to their own dataset and produce an analysis of these data.

3.1 Student projects

Students will be presented with either instructions on using SPSS functions and/or syntax files and be shown how to apply those to a provided dataset. Under the supervision of the instructor, students will apply these skills to a different provided dataset. At the end of each class section, students will turn in the produced output and a brief written interpretation. The interpretation is very important. Output without any interpretation will be returned. For the final project, students will use a dataset of their own choosing, import that dataset, manipulate it as needed, and produce a statistical report using at least one graphical display and at least one descriptive statistical method. This final project will include a written explanation of the results. The dataset you work with for this final project should include at least four variables of which two variables will be measured using continuous data and two variables measured using categorical data.

3.2 Grading

This course will be graded as Pass / Fail. In order to receive a passing grade, the student must successfully complete the four assignments, plus the analysis of an independent data set.including the assignment for the final day that includes work using your own data set. This work must be completed and submitted to the instructor by the last day of the semester, Friday, December 8, 2017, in order to get credit for the course.

If a student feels that he/she has been unfairly graded, information on the appeal process can be found in the academic regulations information (

4. Resources

4.1 Textbook

There is NO required textbook for this class.   However, the following books are possible resources you might want to purchase for your future work with SPSS:

  •  Julie Pallant. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS.
  • Andy Field.  Discovering Statistics Using IBM SPSS Statistics.

4.2 SPSS software

This course will use SPSS for all assignments and the final project/exam. You will have access to this program through UMKC remote labs (   SPSS is also available on computers at the School of Medicine Library.  The Department of Biomedical and Health Informatics has student computer workstations available that can be used by arrangement.

Registered students can purchase the SPSS Statistics Standard GradPack for their computers which includes the following modules: Statistics Base, Advanced Statistics, and Regression. This software comes with either a 6- or a 12-month license. The Health Sciences campus bookstore can get a price for you and help you with ordering this software.

4.3 File storage

When using the UMKC Remote Labs resource, it is recommended that you save your course files on your Q drive space. This space is part of the UMKC network and your space is automatically mapped when you log onto the UMKC system. If you have questions about how to access this directory, contact UMKC Information Services (816-235-2000). From the Q drive, files can be copied/moved to other storage locations/devices. Please note that when you are logged onto Remote Labs, it will be difficult to have direct access to a USB drive on the computer you are sitting at (the remote system has a hard time seeing these local devices/ports). You will, however, be able to save files to your Q drive space.

Your Q drive space is limited (approximately 500 mB), so you might want to also use cloud storage for your files. Free cloud storage options include –

  •  Drop Box
  •  Google Drive (free with a Google email address)
  •  Microsoft One Drive

Cloud storage will give you additional space to store files. If you have any question about setting up a cloud storage account using any of these resources, contact UMKC Information Services (816-235-2000).

4.4. Web Sites

All of the material you need for this class will be availabe on Blackboard. Here are some optional websites that you can use to supplement your learning.

  •, Institute for Digital Research and Education, UCLA. This website provides comprehensive guidance on the use of SPSS (as well as other statistical software). Highly recommended.
  •, Central Michigan University Training Workshop. This website includes both topic overview and tutorial movie clips to demonstrate various functions.
  •, SPSS Help Sheets. This pages contains links to several PDF files which outline how to run simple data analyses using the menu system in SPSS.

4.5 Discussion board

Blackboard has a discussion forum and I would encourage you to describe any problems or post any questions to the discussion board. Other students will benefit from seeing your question and are welcome to post suggested solutions.

5. Course outline

For each part of the course, the instructor will provide a recorded lecture that shows a demonstration of SPSS program execution. In addition, students will have a Course Outline document (MEDB 5506_SPSS_Summer 2017_Course Outline_Notes & Assignments.pdf) that provides detailed notes and information on each assignment. Also loaded in the course Blackboard site in the Course Content area will be data files (some that are not publically available), descriptions of these data files, and the SPSS program files used for the recorded examples. These files can be used as the starting point for your own analyses if desired.  Additionally, associated syntax files can be edited using the SPSS syntax editor.

If you are trying to work in SPSS at the same time as viewing the recorded lectures, it is recommended that you use a system that will allow you to follow along with the recordings and make notes as needed.

The most effective system is using 2 monitors.  This can be accomplished by playing the recording on one screen and doing other operations on the other screen.

Session 1. Getting Acquainted with the SPSS program

For this session and each of the following, I may add a few optional videos or handouts during the semester. I am reviewing the videos in detail to get an outline of topics, but this takes time. Please be patient.

This section has lecture notes, two data sets, a homework assignment and three videos (total viewing time: 1 hour 35 minutes).

Video 1.1. Review of terminology

  •  Basic categories of research
  • What is a variable
  • Categorical versus continuous
  • Independent versus dependent variables
  • Nonrepeated versus repeated measures variables
  • Measurement scales
  • Common statistical programs
  • Orientation to SPSS program
  • What is under each menu

Video 1.2. Getting data into SPSS

  •  Creating a new data set
  • Valid variable names
  • Variable view
  • Adding value labels
  • Reading in an existing data set (Excel)

Video 1.3. Modifying and saving your data

Session 2. Data management and descriptive statistics

This session has lecture notes, two data sets, a homework assignment (with a third data set), and four videos (total viewing time: 1 hour 36 minutes).

Video 2.1. Overview.

Video 2.2. SPSS techniques for cleaning data.

Video 2.3. Univariate statistics, creating, modifying, and copying charts/graphs for categorical variables.

Video 2.4. Creating histograms and boxplots for continuous variables.

Session 3. Missing data.

This section has lecture notes, one dataset, a homework assignment (with a second data set), and two videos (total viewing time: 35 minutes).

Video 3.1. Overview

Video 3.2. Missing values analysis

Session 4. Bivariate analyses.

This section has lecture notes, three data sets, a homework assignment (with a fourth data set) and three videos (total viewing time: 1 hour)

Video 4.1. Overview

  • Bivariate correlations
  • Interpreting correlation coefficients
  • Pearson, spearman, and point-biserial correlations
  • Scatterplots
  • Adding a linear regression line
  • Caution: outliers and non-linear relationships
  • Scatterplots to demonstrate time trends
  • Phi coefficient
  • Crosstabs

Video 4.2. Bivariate associations with continuous variables

  • Correlations using HIV_Time_Corrected
  • Scatterplots
  • Adding line of best fit
  • Scatterplots using FloridaHIV_Cases

Video 4.3. Bivariate associations for dichotomous variables

Session 5. Merging and restructuring datasets.

This section has lecture notes, seven data sets, a homework assignment and three videos (total viewing time: 51 minutes)

Video 5.1. Overview

  • Why not to use cut-and-paste
  • Add variables
  • Consistency of subject identifiers
  • Add cases
  • Consistency of variable types
  • Restructuring datasets
  • Long/thin/vertical
  • Short/wide/horizontal

Video 5.2. Merging data files

  • Merging HIV_Time_Corrected with PerceivedStressScale_items
  • Adding cases from Confidence, Motivation12 subjects to Confidence, Motivation 255 subjects.

Video 5.3. Restructuring datasets

  • Restructuring Earthquake_LabEx5b from vertical to horizontal format
  • Restructuring from horizontal to vertical format
  • Plotting in the vertical format

On Your Own Assignment

For the final project, use a dataset of your own choosing from those available through this course or a dataset of your own. Demonstrate your mastery of elements that have been covered in the course, including importing and manipulating the dataset as needed, and produce a brief statistical report using at least one graphical display and at least one descriptive statistical method. This final project should include a written explanation of the results. The dataset you work with for this final project should include at least four variables of which two variables will be measured using continuous data and two variables measured using categorical data.

6. Course expectations, course policies, requirements, and standards for student coursework and student behavior

Important UMKC Resources and Policies are applicable to every course and every student at UMKC. These are located in the Blackboard site for this course under the “UMKC Policies” tab. As a UMKC student, you are expected to review and abide by these policies. If you have any questions, please contact your instructor for clarification. In addition to the standard UMKC policies, the Department of Biomedical and Health Informatics includes self-plagiarism in their definition of plagiarism. Self-plagiarism is reuse, without prior discussion and consent of the course director, of an existing paper that has been submitted for credit in a different course.

This course follows the “Faculty allowing recording” option of the Academic Inquiry, Course Discussion and Privacy policy.