Course Description. Learning Objectives

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1 STAT X400 (2 semester units in Statistics) Business, Technology & Engineering Technology & Information Management Quantitative Analysis & Analytics Course Description This course introduces students to the basic practice of statistics by using SPSS Statistics, a statistical software program designed for data management and data analysis. The goal is to familiarize students with the workings of SPSS and to teach them how to perform basic statistical analyses with SPSS. The course will focus on database management tasks, descriptive statistics and graphics, and basic inferential statistics. This is a hands-on class: students will explore and analyze real-world data sets using the tools provided by SPSS. The focus of this course will be data not formulas, recipes or derivations. In class, following the instructor s lead, students will produce a variety of statistics that are usually included in an introductory undergraduate course. They will work in groups and be asked to interpret data sets provided for instruction. The process will be: explore the data, use the appropriate statistical method to answer the research question, interpret the SPSS output and present the results. At the end of this course, students should be equipped to study more advanced statistical techniques and explore other functionalities available in SPSS not covered in this course. Learning Objectives When students have completed this course, they will: 1. use the graphical user interface of SPSS 2. perform a number of useful data management tasks 3. write some basic syntax and know how to obtain syntax 4. generate numerical and graphical summaries for variables 5. perform inferential statistical tests 6. define fundamental statistical terms and concepts used in research 7. avoid common misuses and misinterpretations of statistical results 8. acquire an understanding of how statistical methods are selected Intended Audience The course is intended for those who are new to statistics or need a refresher and would like to gain an understanding of the basic notions that underlie statistical thinking through realistic examples, while at the same time learning how to use SPSS, one of the leading statistical software programs used in academia, government, and industry. This 1

2 class is meant to meet the needs of those who are seeking entry-level positions, and of those who want to move forward in their career by broadening their analytical skills. Prerequisites No particular requisite is required for this course, although some basic notion of quantitative data (i.e. being comfortable with numbers) and a general familiarity with the Windows (or Mac) operating system are necessary. Students should come to class with SPSS (preferably, version 12.0 or higher) preloaded on their laptop. Registered students who do not already have SPSS can lease the software for 6 months (or longer) by going to and selecting IBM SPSS GradPack for Windows or Mac. Course Contents 1. The SPSS Environment (Meeting 1) Data, syntax and output windows, multiple instances of each and the designated window; the Help Menu. SPSS Data Editor: creating a data file The Editor s Data View: entering data manually, opening an existing database (SPSS format v. other file formats), saving an SPSS data file into another file format The Data Editor s Variable View: variable names and labels, value label, variable types, data formatting, and other formatting issues; displaying data file information; annotating data file; coding missing data. Data Management: modifying an existing database (adding/deleting variables, cases); sorting; computing new variables based on existing ones; recoding existing categorical variables; filtering cases. SPSS Syntax: what is syntax (commands, subcommands, and keywords); syntax vs. the menu system; generating syntax automatically and manually; including comments (documenting your work); executing commands. SPSS Output Viewer: navigating through the output: viewing results; two panes (outline and results); exporting the output (file format options). Homework 1 (due on Meeting 2) Textbook Chapter 1: Introduction Textbook Chapter 2: An Introductory Tour of IBM SPSS Statistics Textbook Appendix B: Transforming and Selecting Data Syllabus Homework and Reading Schedule 2. Describing Data (Meeting 2 & Meeting 3) Exploratory Data Analysis: numerical and graphical methods UC Berkeley Extension STAT X400 2

3 Describing Quantitative/Metric (scale) Variables: measures of central tendency, dispersion and shape; Importance of Plotting Your Data Univariate Graphics for Scale Variables: histogram, boxplots, stem-and-leaf plots, dot plots (horizontal and serial), etc. Bivariate Graphics for Scale Variables: scatter plots, scatter matrix, etc. Graphics for Categorical Variables: Tables and charts Editing charts: Using the Chart Editor; saving and applying a chart template. Screening Data Prior to Analysis: missing data, outliers, data anomalies, assessing distributions (types of probability distributions) Homework 2 (due on Meeting 3) Homework 3 (due on Meeting 4) Textbook Chapter 4: Counting Responses Textbook Chapter 5: Computing Descriptive Statistics Textbook Chapter 6: Comparing Groups Textbook Chapter 7: Looking at Distributions Textbook Chapter 8: Counting Responses for Combinations of Variables Textbook Chapter 9: Plotting Data 3. Population, Samples, Estimation and Inference (Meeting 4 & Meeting 5) Producing Data: observational studies vs. experiments Population and samples The Normal Distribution The Central Limit Theorem and confidence intervals Simulating the Central Limit Theorem Meeting 5: In class mid-term. Homework 4 (due on Meeting 5) Homework 5 (due on Meeting 6) Textbook Chapter 3: Sources of Data Textbook Chapter 10: Evaluating Results from Samples Textbook Chapter 11: The Normal Distribution Textbook Appendix D: Areas under the Normal Curve 4. Introduction to Hypothesis Testing & Statistical Significance (Meeting 6) Probability and the logic of hypothesis testing (types of error) Translating research hypotheses into statistical terms (the null) Region of rejection (one-tail, two-tail) Inference about a population mean (μ) Inference about a population proportion (π) Confidence intervals and their interpretation Interpreting results of a statistical test: p-values, significance and importance UC Berkeley Extension STAT X400 3

4 Homework 6 (due on Meeting 7) Textbook Chapter 12: Testing a Hypothesis about a Single Mean Textbook Appendix C: The T Distribution 5. Analyzing Categorical Variables (Meeting 7) Chi-square for one sample (goodness-of-fit test) Chi-square for independence Chi-square test of homogeneity (comparing populations) McNemar test for related samples (e.g. before-after) Measures of Association (for nominal and ordinal variables) Homework 7 (due on Meeting 8) Textbook Chapter 17: Comparing Observed and Expected Counts Textbook Chapter 19: Measuring Association 6. Comparing Populations on Scale Variables (Meeting 8) Two types of t-tests: independent samples and paired measurements One-way and two-way (factorial) ANOVA Non-parametric methods (Mann-Whitney; Wilcoxon signed ranks test; Kruskal- Wallis; etc.) Multiple comparisons and controlling the risk of a Type I error Homework 8 (due on Meeting 9) Textbook Chapter 13: Testing a Hypothesis about Two Related Means Textbook Chapter 14: Testing a Hypothesis about Two Independent Means Textbook Chapter 15: One-Way Analysis of Variance Textbook Chapter 16: Two-Way Analysis of Variance Textbook Chapter 18: Nonparametric Tests 7. Correlation & Regression (Meeting 9) Correlation and causation Bivariate correlation: parametric (Pearson) and non-parametric (Spearman) How to read a bivariate correlation matrix Simple linear regression: the least-squares method Homework 9 (due on Meeting 10) Final project (due on Meeting 10) Textbook Chapter 20: Linear Regression and Correlation Textbook Chapter 21: Testing Regression Hypotheses Textbook Chapter 22: Analyzing Residuals 8. A Brief Introduction to Multiple Regression (Meeting 10) Assumptions in Multiple Regression Multicollinearity Measures of Model Fit UC Berkeley Extension STAT X400 4

5 Regression Diagnostics Final class project (Homework 10) and Homework 9 due on this Meeting. Textbook Chapter 23: Building Regression Models Textbook Chapter 24: Multiple Regression Diagnostics Methods of Instruction This is a hands-on class. Students will perform tasks as the instructor is demonstrating them. Therefore students should bring their laptop in class. The instructor will demonstrate (using the Windows version of the software) the various tasks while students will be asked to follow along on their computers. Outside of class, students will read assigned chapters from the textbook and will be given homework to familiarize themselves with both SPSS and the basic practice of statistics. Credit Requirements To earn credit in this course, students must receive a passing grade on both their homework and final class project. Students will be assigned homework weekly. There will also be an in-class midterm exam, which is mandatory for those who are taking this course for credit. The class project will count as the final. It is due on the last day of class. Students are asked to analyze a real-world data set. The (non-spss) data file will be provided after the third meeting, as well as the instructions regarding what students are expected to do. They will have to submit a report in which they will describe data management tasks, perform exploratory data analyses (generate numerical and graphical summaries of variables), state hypotheses in statistical terms, describe the statistical techniques they used, discuss whether the assumptions underlying the techniques were or were not met, present the results and their conclusions discussing both statistical issues and substantive topics as defined by the research hypotheses. Students will be expected to use statistical techniques learned up to and including Meeting 8. Deliverables: the report and the project data set. The final project will be evaluated based on the ten tasks described in the instructions, which include importing data into SPSS, performing exploratory data analysis, categorical data analysis and comparative analyses on a scale variable. Deliverables for the homework will be specified in the document describing the weekly assignment. All homework is due by 5PM on the day of the next class. Late homework, including the final project, will have 20% of its points deducted automatically unless UC Berkeley Extension STAT X400 5

6 the student asked for and was granted an extension by the instructor. Homework must be ed to the instructor. The homework reports should follow these rules: File format: pdf; document format: double-spaced; Times-New-Roman 12 font. Grades on the homework will be distributed at the next class meeting. A website has been created for this course (the URL will be given in class). There students will download the syllabus, the data sets for in-class instruction, the lecture notes and the homework assignments. The data sets and the lecture notes will be posted before each class meeting. The homework assignment will be posted for students to download soon after each meeting; the previous homework s answers and explanations will also be provided to students. Course Grade Weighting Criterion 1 (Homework) 45% Criterion 2 (Class participation, attendance, lateness, feedback) 5% Criterion 3 (Midterm) 20% Criterion 4 (Final Course Project) 30% Please inform the instructor ahead of time if you are going to miss a class or are going to be late returning your homework. Grades A % B C D F < 60 P 70 NP < 70 Grading options CLG Credit Letter Grade (DEFAULT) CP/NP Pass/Not Pass NC Not for Credit W Withdrawal (must be student-initiated) You have until the last class to declare your preference. Required Text for the Course Any one of the three textbooks listed below can be used. IBM SPSS Statistics 19 Guide to Data Analysis UC Berkeley Extension STAT X400 6

7 Marija J. Norušis ISBN-10: ISBN-13: Prentice-Hall a division of Pearson Education If the above textbook is not available: either PASW Statistics 18 Guide to Data Analysis Marija J. Norušis ISBN-10: ISBN-13: Prentice-Hall a division of Pearson Education or SPSS 17.0 Guide to Data Analysis Marija J. Norušis ISBN-10: ISBN-13: Prentice Hall Barnes & Noble has a textbook rental program. General Information & Policies All students should be familiar with the following general information and policies on UC Berkeley Extension s website, including information about textbook ordering, library services, student conduct expectations, etc.: There you can also obtain a petition to withdraw, a petition for incomplete and UC Berkeley Extension s refund policy. Student Disability Services Students who require an accommodation can contact Disability Student Services at: Instructor Biography Dominic Lusinchi is a partner and research consultant at Far West Research, a privately owned firm in San Francisco, specializing in statistical and quantitative research. He has over two decades of experience working in industry, academia, and government, as well as in international organizations. He has used SPSS for over 20 years in his daily practice as a researcher and consultant. He is a graduate of the University of California, Berkeley, and holds a Diploma and a Master s degree from the Department of Economics and Mathematical Methods of the School of Advanced Studies in the Social Sciences (Paris), and earned a Ph.D. in UC Berkeley Extension STAT X400 7

8 sociology (specialty area: applied statistics) from the University of Paris (VIII). He is a member of the American Statistical Association, the Pacific Sociological Association and the American Association for Public Opinion Research. Instructor s full CV can be found at dominic@farwestresearch.com. UC Berkeley Extension STAT X400 8