SPSS Data Analysis

Discuss the issues related to z Test Versus t Test and their applications in the research.

2. Follow instructions from Doing Data Analysis with SPSS, Session 11 and practice

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  • Logic of Hypothesis Testing,
  • A More Realistic Case: We Don’t Know My of Sigma,
  • Small Sample Example
  • Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Doing Data Analysis
    with SPSS®
    Version 18
    Robert H. Carver
    Stonehill College
    Jane Gradwohl Nash
    Stonehill College
    Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
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    Doing Data Analysis with SPSS®
    Version 18
    Robert H. Carver, Jane Gradwohl Nash
    Publisher: Richard Stratton
    Senior Sponsoring Editor: Molly Taylor
    Assistant Editor: Shaylin Walsh
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    In loving memory of my brother and teacher Barry,
    and for Donna, Sam, and Ben, who teach me daily.
    RHC
    For Justin, Hanna and Sara—you are my world.
    JGN
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    Contents
    Session 1. A First Look at SPSS Statisitcs 18 1
    Objectives 1
    Launching SPSS/PASW Statistics 18 1
    Entering Data into the Data Editor 3
    Saving a Data File 6
    Creating a Bar Chart 7
    Saving an Output File 11
    Getting Help 12
    Printing in SPSS 12
    Quitting SPSS 12
    Session 2. Tables and Graphs for One Variable 13
    Objectives 13
    Opening a Data File 13
    Exploring the Data 14
    Creating a Histogram 16
    Frequency Distributions 20
    Another Bar Chart 22
    Printing Session Output 22
    Moving On… 23
    Session 3. Tables and Graphs for Two Variables 27
    Objectives 27
    Cross-Tabulating Data 27
    Editing a Recent Dialog 29
    More on Bar Charts 29
    Comparing Two Distributions 32
    v
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    vi
    Contents
    Scatterplots to Detect Relationships 33
    Moving On… 34
    Session 4. One-Variable Descriptive Statistics 39
    Objectives 39
    Computing One Summary Measure for a Variable 39
    Computing Additional Summary Measures 43
    A Box-and-Whiskers Plot 46
    Standardizing a Variable 47
    Moving On… 48
    Session 5. Two-Variable Descriptive Statistics 51
    Objectives 51
    Comparing Dispersion with the Coefficient of Variation 51
    Descriptive Measures for Subsamples 53
    Measures of Association: Covariance and Correlation 54
    Moving On… 57
    Session 6. Elementary Probability 61
    Objectives 61
    Simulation 61
    A Classical Example 61
    Observed Relative Frequency as Probability 63
    Handling Alphanumeric Data 65
    Moving On… 68
    Session 7. Discrete Probability Distributions 71
    Objectives 71
    An Empirical Discrete Distribution 71
    Graphing a Distribution 73
    A Theoretical Distribution: The Binomial 74
    Another Theoretical Distribution: The Poisson 76
    Moving On… 77
    Session 8. Normal Density Functions 81
    Objectives 81
    Continuous Random Variables 81
    Generating Normal Distributions 82
    Finding Areas under a Normal Curve 85
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    Contents
    vii
    Normal Curves as Models 87
    Moving On… 89
    Session 9. Sampling Distributions 93
    Objectives 93
    What Is a Sampling Distribution? 93
    Sampling from a Normal Population 94
    Central Limit Theorem 97
    Sampling Distribution of the Proportion 99
    Moving On… 100
    Session 10. Confidence Intervals 103
    Objectives 103
    The Concept of a Confidence Interval 103
    Effect of Confidence Coefficient 106
    Large Samples from a Non-normal (Known) Population 106
    Dealing with Real Data 107
    Small Samples from a Normal Population 108
    Moving On… 110
    Session 11. One-Sample Hypothesis Tests 113
    Objectives 113
    The Logic of Hypothesis Testing 113
    An Artificial Example 114
    A More Realistic Case: We Don’t Know Mu or Sigma 117
    A Small-Sample Example 119
    Moving On… 121
    Session 12. Two-Sample Hypothesis Tests 125
    Objectives 125
    Working with Two Samples 125
    Paired vs. Independent Samples 130
    Moving On… 132
    Session 13. Analysis of Variance (I) 137
    Objectives 137
    Comparing Three or More Means 137
    One-Factor Independent Measures ANOVA 138
    Where Are the Differences? 142
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    viii
    Contents
    One-Factor Repeated Measures ANOVA 144
    Where Are the Differences? 149
    Moving On… 149
    Session 14. Analysis of Variance (II) 153
    Objectives 153
    Two-Factor Independent Measures ANOVA 153
    Another Example 159
    One Last Note 161
    Moving On… 162
    Session 15. Linear Regression (I) 165
    Objectives 165
    Linear Relationships 165
    Another Example 170
    Statistical Inferences in Linear Regression 171
    An Example of a Questionable Relationship 172
    An Estimation Application 173
    A Classic Example 174
    Moving On… 175
    Session 16. Linear Regression (II) 179
    Objectives 179
    Assumptions for Least Squares Regression 179
    Examining Residuals to Check Assumptions 180
    A Time Series Example 185
    Issues in Forecasting and Prediction 187
    A Caveat about “Mindless” Regression 190
    Moving On… 191
    Session 17. Multiple Regression 195
    Objectives 195
    Going Beyond a Single Explanatory Variable 195
    Significance Testing and Goodness of Fit 201
    Residual Analysis 202
    Adding More Variables 202
    Another Example 203
    Working with Qualitative Variables 204
    A New Concern 206
    Moving On… 207
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    Contents
    ix
    Session 18. Nonlinear Models 211
    Objectives 211
    When Relationships Are Not Linear 211
    A Simple Example 212
    Some Common Transformations 213
    Another Quadratic Model 215
    A Log-Linear Model 220
    Adding More Variables 221
    Moving On… 221
    Session 19. Basic Forecasting Techniques 225
    Objectives 225
    Detecting Patterns over Time 225
    Some Illustrative Examples 226
    Forecasting Using Moving Averages 228
    Forecasting Using Trend Analysis 231
    Another Example 234
    Moving On… 234
    Session 20. Chi-Square Tests 237
    Objectives 237
    Qualitative vs. Quantitative Data 237
    Chi-Square Goodness-of-Fit Test 237
    Chi-Square Test of Independence 241
    Another Example 244
    Moving On… 245
    Session 21. Nonparametric Tests 249
    Objectives 249
    Nonparametric Methods 249
    Mann-Whitney U Test 250
    Wilcoxon Signed Ranks Test 252
    Kruskal-Wallis H Test 254
    Spearman’s Rank Order Correlation 257
    Moving On… 258
    Session 22. Tools for Quality 261
    Objectives 261
    Processes and Variation 261
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    x
    Contents
    Charting a Process Mean 262
    Charting a Process Range 265
    Another Way to Organize Data 266
    Charting a Process Proportion 268
    Pareto Charts 270
    Moving On… 272
    Appendix A. Dataset Descriptions 275
    Appendix B. Working with Files 309
    Objectives 309
    Data Files 309
    Viewer Document Files 310
    Converting Other Data Files into SPSS Data Files 311
    Index 315
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    Preface
    Quantitative Reasoning, Real Data, and Active Learning
    Most undergraduate students in the U.S. now take an
    introductory course in statistics, and many of us who teach statistics
    strive to engage students in the practice of data analysis and quantitative
    thinking about real problems. With the widespread availability of
    personal computers and statistical software, and the near-universal
    application of quantitative methods in many professions, introductory
    statistics courses now emphasize statistical reasoning more than
    computational skill development. Questions of how have given way to
    more challenging questions of why, when, and what?
    The goal of this book is to supplement an introductory
    undergraduate statistics course with a comprehensive set of self-paced
    exercises. Students can work independently, learning the software skills
    outside of class, while coming to understand the underlying statistical
    concepts and techniques. Instructors can teach statistics and statistical
    reasoning, rather than teaching algebra or software. Both students and
    teachers can devote their energies to using data analysis in ways that
    inform their understanding of the world and investigate problems that
    really matter.
    The Approach of This Book
    The book reflects the changes described above in several ways.
    First and most obviously it provides some training in the use of a
    powerful software package to relieve students of computational drudgery.
    Second, each session is designed to address a statistical issue or need,
    rather than to feature a particular command or menu in the software.
    xi
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    xii
    Preface
    Third, nearly all of the datasets in the book are real, reflecting a variety
    of disciplines and underscoring the wide applicability of statistical
    reasoning. Fourth, the sessions follow a traditional sequence, making the
    book compatible with many texts. Finally, as each session leads students
    through the techniques, it also includes thought-provoking questions
    and challenges, engaging the student in the processes of statistical
    reasoning. In designing the sessions, we kept four ideas in mind:

    Statistical reasoning, not computation, is the goal of the course.
    This book asks students questions throughout, balancing
    software instruction with reflection on the meaning of results.

    Students arrive in the course ready to engage in statistical
    reasoning. They need not slog all the way through descriptive
    techniques before encountering the concept of inference. The
    exercises invite students to think about inferences from the
    start, and the questions grow in sophistication as students
    master new material.

    Exploration of real data is preferable to artificial datasets. With
    the exception of the famous Anscombe regression dataset and
    a few simulations, all of the datasets are real. Some are very
    old and some are quite current, and they cover a wide range
    of substantive areas.

    Statistical topics, rather than software features, should drive
    the design of each session. Each session features several SPSS
    functions selected for their relevance to the statistical concept
    under consideration.
    This book provides a rigorous but limited introduction to the
    software produced by SPSS, an IBM company.1 The SPSS/PASW2
    Statistics 18 system is rich in features and options; this book makes no
    attempt to “cover” the entire package. Instead, the level of coverage is
    commensurate with an introductory course. There may be many ways to
    perform a given task in SPSS; generally, we show one way. This book
    provides a “foot in the door.” Interested students and other users can
    explore the software possibilities via the extensive Help system or other
    standard SPSS documentation.
    SPSS was acquired by IBM in October 2009.
    SPSS Statistics 18 was formerly known as PASW Statistics 18, and the
    PASW name appears on several screens in the software. The book will reference
    the SPSS name only, but note that SPSS and PASW are interchangeable terms.
    1
    2
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    Preface
    xiii
    Using This Book
    We presume that this book is being used as a supplementary text
    in an introductory-level statistics course. If your courses are like ours
    (one in a psychology department, the other in a business department),
    class time is a scarce resource. Adding new material is always a
    balancing act. As such, supplementary readings and assignments must
    be carefully integrated. We suggest that instructors use the sessions in
    this book in four different ways, tailoring the approach throughout the
    term to meet the needs of the students and course.




    In-class activity: Part or all of some sessions might best be
    done together in class, with each student at a computer. The
    instructor can comment on particular points and can roam to
    offer assistance. This may be especially effective in the earliest
    sessions.
    Stand-alone assignments: In conjunction with a topic covered
    in the principal text, sessions can be assigned as independent
    out-of-class work, along with selected Moving On… questions.
    This is our most frequently-used approach. Students
    independently learn the software, re-enforce the statistical
    concepts, and come to class with questions about any
    difficulties they encountered in the lab session.
    Preparation for text-based case or problem: An instructor may
    wish to use a textbook case for a major assignment. The
    relevant session may prepare the class with the software skills
    needed to complete the case.
    Independent projects: Sessions may be assigned to prepare
    students to undertake an independent analysis project
    designed by the instructor. Many of the data files provided
    with the book contain additional variables that are never used
    within sessions. These variables may form the basis for
    original analyses or explorations.
    Solutions are available to instructors for all Moving On… and
    bold-faced questions. Instructors should consult their Cengage Learning
    sales representatives for details. A companion website is available to both
    instructors and students at www.cengage.com/statistics/carver.
    The Data Files
    As previously noted, each of the data files provided with this book
    contains real data, much of it downloaded from public sites on the World
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    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    xiv
    Preface
    Wide Web. The companion website to accompany the book contains all of
    the data files. Appendix A describes each file and its source, and provides
    detailed definitions of each variable. Many of the files include variables in
    addition to those featured in exercises and examples. These variables
    may be useful for projects or other assignments.
    The data files were chosen to represent a variety of interests and
    fields, and to illustrate specific statistical concepts or techniques. No
    doubt, each instructor will have some favorite datasets that can be used
    with these exercises. Most textbooks provide datasets as well. For some
    tips on converting other datasets for use with SPSS, see Appendix B.
    Note on Software Versions
    The sessions and screen images in this book mostly used SPSS
    Base 18 running under Windows XP. Users of other versions will notice
    minor differences with the figures and instructions in this book. Before
    starting Sessions 9−11, users of the Student Version of SPSS should be
    aware that the student version does not support the use of syntax files,
    and therefore will not be able to run the simulations in those sessions.
    We’ve provided the results of our simulation runs so that you’ll still get
    the point. Read the sessions closely and you will still be able to follow the
    discussion.
    To the Student
    This book has two goals: to help you understand the concepts
    and techniques of statistical analysis, and to teach you how to use one
    particular tool—SPSS—to perform such analysis. It can supplement but
    not replace your primary textbook or your classroom time. To get the
    maximum benefit from the book, you should take your time and work
    carefully. Read through a session before you sit down at the computer.
    Each session should require no more than about 30 minutes of computer
    time; there’s little need to rush through them.
    You’ll often see boldfaced questions interspersed through the
    computer instructions. These are intended to shift your focus from
    mouse-clicking and typing to thinking about what the answers mean,
    whether they make sense, whether they surprise or puzzle you, or how
    they relate to what you have been doing in class. Attend to these
    questions, even when you aren’t sure of their purpose.
    Each session ends with a section called Moving On…. You should
    also respond to the numbered questions in that section, as assigned by
    your instructor. Questions in the Moving On… sections are designed to
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    Preface
    xv
    challenge you. Sometimes, it is quite obvious how to proceed with your
    analysis; sometimes, you will need to think a bit before you issue your
    first command. The goal is to get you to engage in statistical thinking,
    integrating what you have learned throughout your course. There is
    much more to doing data analysis than “getting the answer,” and these
    questions provide an opportunity to do realistic analysis.
    As noted earlier, SPSS is a large and very powerful software
    package, with many capabilities. Many of the features of the program are
    beyond the scope of an introductory course, and do not figure in these
    exercises. However, if you are curious or adventurous, you should
    explore the menus and Help system. You may find a quicker, more
    intuitive, or more interesting way to approach a problem.
    Typographical Conventions
    Throughout this book, certain symbols and typefaces are used
    consistently. They are as follows:
    
    Menu h Sub-menu h Command The mouse icon indicates an
    action you take at the computer, using the mouse or keyboard.
    The bold type lists menu selections for you to make.
    Dialog box headings are in this typeface.
    Dialog box choices, variable names, and items you should type appear in
    this typeface.
    File names (e.g., Colleges) appear in this typeface.
     A box like this contains an instruction requiring special care or information
    about something that may work differently on your computer system.
    Bold italics in the text indicate a question that you should
    answer as you write up your experiences in a session.
    Acknowledgments
    Like most authors, we owe many debts of gratitude for this book.
    This project enjoyed the support of Stonehill College through the annual
    Summer Grants and the Stonehill Undergraduate Research Experience
    (SURE) programs. As the SURE scholar in the preparation of the first
    edition of the book, Jason Boyd contributed in myriad ways, consistently
    doing reliable, thoughtful, and excellent work. He tested every session,
    prepared instructors’ solutions, researched datasets, critiqued sessions
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    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    xvi
    Preface
    from a student perspective, and tied up loose ends. His contributions
    and collegiality were invaluable.
    For the previous edition we enlisted the help of two very able
    students, Jennifer Karp and Elizabeth Wendt. Their care and affable
    approach to the project has made all the difference.
    Many colleagues and students suggested or provided datasets.
    Student contributors were Jennifer Axon, Stephanie Duggan, Debra
    Elliott, Tara O’Brien, Erin Ruell, and Benjamin White. A big thank you
    goes out to our students in Introduction to Statistics and Quantitative
    Analysis for Business for pilot-testing many of the sessions and for
    providing useful feedback about them.
    We thank our Stonehill colleagues Ken Branco, Lincoln Craton,
    Roger Denome, Jim Kenneally, and Bonnie Klentz for suggesting or
    sharing data, and colleagues from other institutions who supported our
    work: Chris France, Roger Johnson, Stephen Nissenbaum, Mark
    Popovksy, and Alan Reifman. Thanks also to the many individuals and
    organizations granting permission to use published data for these
    sessions; they are all identified in Appendix A.
    Over the years working with Cengage Learning, we have enjoyed
    the guidance and encouragement of Richard Stratton, Curt Hinrichs,
    Carolyn Crockett, Molly Taylor, Dan Seibert, Catherine Ronquillo,
    Jennifer Risden, Ann Day, Sarah Kaminskis, and Seema Atwal. We also
    thank Paul Baum at California State University, Northridge and to
    Dennis Jowaisas at Oklahoma City University, two reviewers whose
    constructive suggestions improved the quality of the first edition.
    W
    W
    W
    Finally, we thank our families.
    I want to thank my husband, Justin, for his
    unwavering support of my professional work, and our
    daughters, Hanna and Sara, for providing an enjoyable
    distraction from this project.
    JGN
    The Carver home team has been fabulous, as always.
    To Donna, my partner and counsel; to Sam and Ben, my
    cheering section and assistants. Thanks for the time, space,
    and encouragement.
    RHC
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    About the Authors
    Robert H. Carver is Professor of Business Administration at
    Stonehill College in Easton, Massachusetts and an Adjunct Professor at
    the International School of Business at Brandeis University, and has
    received awards for teaching excellence at both institutions. He teaches
    courses in applied statistics, research methods, information systems,
    strategic management, and business and society. He holds an A.B. from
    Amherst College and a Ph.D. in Public Policy Studies from the University
    of Michigan. He is the author of Doing Data Analysis with Minitab 14
    (Cengage Learning), and articles in Case Studies in Business, Industry
    and Government Statistics; Publius; The Journal of Statistics Education;
    The Journal of Business Ethics; PS: Political Science & Politics; Public
    Administration Review; Public Productivity Review; and The Journal of
    Consumer Marketing.
    Jane Gradwohl Nash is Professor of Psychology at Stonehill
    College. She earned her B.A. from Grinnell College and her Ph.D. from
    Ohio University. She enjoys teaching courses in the areas of statistics,
    cognitive psychology, and general psychology. Her research interests are
    in the area of knowledge structure and knowledge change (learning) and
    more recently, social cognition. She is the author of articles that have
    appeared in the Journal of Educational Psychology; Organizational
    Behavior and Human Decision Processes; Computer Science Education;
    Headache; Journal of Chemical Education; Research in the Teaching of
    English; and Written Communication.
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    Session 1
    A First Look at SPSS Statistics 18
    Objectives
    In this session, you will learn to do the following:
    • Launch and exit the program
    • Enter quantitative and qualitative data in a data file
    • Create and print a graph
    • Get Help
    • Save your work to a disk
    Launching SPSS/PASW Statistics 18
    Before starting this session, you should know how to run a
    program within the various Windows operating systems. All the
    instructions in this manual presume basic familiarity with the Windows
    environment.
     Check with your instructor for specific instructions about running the
    program on your school’s system. Your instructor will also tell you where
    to find the software and its related files.
    Click on the start button at the lower left of your screen, and
    among the programs, find SPSS Inc and select PASW Statistics 18
    PASW Statistics 18. Depending on how the program was installed, you
    may also have a shortcut icon on your desktop.
    On the next page is an image of the screen you will see when the
    software is ready. First you will see a menu dialog box listing several
    options; behind it is the Data Editor, which is used to display the data
    that you will analyze using the program. Later you will encounter the
    Output Viewer window that displays the results of your analysis. Each
    1
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    2
    Session 1 ΠA First Look at SPSS Statistics 18
    window has a unique purpose, to be made clear in due course. It’s
    important at the outset to know there are several windows with different
    functions.
    At any point in your session, only one window is selected,
    meaning that mouse actions and keystrokes will affect that window
    alone. When you start, there’s a special start-up window. For now, click
    Cancel and the Data Editor will be selected.
    Since the software operates upon data, we generally start by
    placing data into the Editor, either from the keyboard or from a stored
    disk file. The Data Editor looks much like a spreadsheet. Cells may
    contain numbers or text, but unlike a spreadsheet, they never contain
    formulas. Except for the top row, which is reserved for variable names,
    rows are numbered consecutively. Each variable in your dataset will
    occupy one column of the data file, and each row represents one
    observation. For example, if you have a sample of fifty observations on
    two variables, your worksheet will contain two columns and fifty rows.
    The menu bar across the top of the screen identifies broad
    categories of SPSS’ features. There are two ways to issue commands in
    SPSS: choose commands from the menu or icon bars, or type them
    directly into a Syntax Editor. This book always refers you to the menus
    and icons. You can do no harm by clicking on a menu and reading the
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    Entering Data into the Data Editor
    3
    choices available, and you should expect to spend some time exploring
    your choices in this way.
    Entering Data into the Data Editor
    For most of the sessions in this book, you will start by accessing
    data already stored on a disk. For small datasets or class assignments,
    though, it will often make sense simply to type in the data yourself. For
    this session, you will transfer the data displayed below into the Data
    Editor.
    In this first session, our goal is simple: to create a small data file,
    and then use the software to construct two graphs using the data. This is
    typical of the tasks you will perform throughout the book.
    The coach of a high school swim team runs a practice for 10
    swimmers, and records their times (in seconds) on a piece of paper.1
    Each swimmer is practicing the 50-meter freestyle event, and the boys on
    the team assert that they did better than the girls. The coach wants to
    analyze these results to see what the facts are. He codes gender with as F
    (female) for the girls and M (male) for the boys.
    Swimmer
    Sara
    Jason
    Joanna
    Donna
    Phil
    Hanna
    Sam
    Ben
    Abby
    Justin
    Gender
    F
    M
    F
    F
    M
    F
    M
    M
    F
    M
    Time
    29.34
    30.98
    29.78
    34.16
    39.66
    44.38
    34.80
    40.71
    37.03
    32.81
    The first step in entering the data into the Data Editor is to define
    three variables: Swimmer, Gender, and Time. Creating a variable
    requires us to name it, specify the type of data (qualitative, quantitative,
    number of decimal places, etc.) and assign labels to the variable and data
    values if we wish.
    1 Nearly every dataset in this book is real. For the sake of starting
    modestly, we have taken a minor liberty in this session. This example is actually
    extracted from a dataset you will use later in the book. The full dataset appears
    in two forms: Swimmer and Swimmer2.
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    4
    Session 1 ΠA First Look at SPSS Statistics 18
    
    Move your cursor to the bottom of the Data Editor, where you will
    see a tab labeled Variable View. Click on that tab. A different grid
    appears, with these column headings (widen the window to see all
    columns):
    For each variable we create, we need to specify all or most of the
    attributes described by these column headings.
    
    Move your cursor into the first empty cell in Row 1 (under Name)
    and type the variable name Swimmer. Press Enter (or Tab).
    
    Now click within the Type column, and a small gray button
    marked with three dots will appear; click on it and you’ll
    see this dialog box. Numeric is the default variable type.
    
    Click on the circle labeled String in the lower left corner of the
    dialog box. The names of the swimmers constitute a nominal or
    categorical variable, represented by a “string” of characters rather
    than a number. Click OK.
    Notice that the Measure column (far right column) now reads
    Nominal, because you chose String as the variable type.
    In SPSS, each variable may carry a descriptive label to help
    identify its meaning. Additionally, as we’ll soon see, we can also label
    individual values of a variable. Here’s how we add the variable label:
    
    Move the cursor into the Label column, and type Name of
    Swimmer. As you type, notice that the column gets wider. This
    completes the definition of our first variable.
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    Entering Data into the Data Editor
    
    Now let’s create a variable to represent gender. Move to the first
    column of row 2, and name the new variable Gender.
    
    Like Name, Gender is also a nominal scale variable, so we will
    proceed as in the prior step. Change the variable type from
    Numeric to String, and reduce the width of the variable from 8
    characters down to 1.
    5
     Throughout the book, we’ll often ask you to carry out a step on your own
    after previously demonstrating the technique in the previous example. In
    this way you will eventually build facility with these skills.
     Label this variable Sex of swimmer.
    
    Now we can assign text labels to our coded values. In the Values
    column, click on the word None and then click the gray box with
    three dots. This opens the Value Labels dialog box (completed
    version shown here). Type F in the Value box and type Female in
    the Value Label box. Click Add.
    
    Then type M in Value, and Male in Value Label. Click Add, and
    then click OK.
    Finally, we’ll create a scale variable in this dataset: Time.
    
    Begin as you have done twice now, by naming the third variable
    Time. You may leave Type, Width, and Decimals as they are, since
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    6
    Session 1 ΠA First Look at SPSS Statistics 18
    Time is a numeric variable and the default setting of 8 spaces
    wide with two decimal places is appropriate here.2
    
    Label this variable “Practice time (secs).”
    
    Switch to the Data View by clicking the appropriate tab in the
    lower left of your screen.
    Follow the directions below, using the data table found on page 3.
    If you make a mistake, just return to the cell and retype the entry.
    
    Move the cursor to the first cell below Swimmer, and type Sara;
    then press Enter. In the next cell, and type Jason. When you’ve
    completed the names, move to the top cell under Gender, and go
    on. When you are finished, the Data Editor should look like this:
    
    In the View menu at the top of your screen, select Value Labels;
    do you see the effect in the Data Editor? Return to the View menu
    and click Value Labels again. You can toggle labels on and off in
    this way.
    Saving a Data File
    It is wise to save all of your work in a disk file. SPSS distinguishes
    between two types of files—output and data—that one might want to
    2 When we create a numeric variable, we specify the maximum length of
    the variable and the number of decimal places. For example, the data type
    “Numeric 8.2” refers to a number eight characters long, of which the final two
    places follow the decimal point: e.g., 12345.78.
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    Creating a Bar Chart
    7
    save. At this point, we’ve created a data file and ought to save it on a
    disk. Let’s call the data file Swim.
     Check with your instructor to see if you can save the data file on a hard drive
    or network drive in your system. On your own computer, it is wise to establish
    a folder to hold your work related to this book.
    
    On the File menu, choose Save As…. In the Save in box, select the
    destination directory that chosen (in our example, we’re saving it
    to the Desktop). Then, next to File Name, type swim. Click Save.
    A new output Viewer window will open, with an entry that
    confirms you’ve saved your data file.
    Creating a Bar Chart
    With the data entered and saved, we can begin to look for an
    answer for the coach. We’ll first use a bar graph to display the average
    time for the males in comparison to the females. In SPSS, we’ll use the
    Chart Builder to generate graphs.
    
    Click on Graphs in the menu bar, and choose Chart Builder….
    You will see an information window noting that variables must be
    specified as we did earlier. Close the window and you’ll find the
    dialog box shown at the top of the next page.
     From now on in this book, we’ll abbreviate menu selections with the name of
    the menu and the submenu or command. The command you just gave would be
    Graphs h Chart Builder…
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    8
    Session 1 ΠA First Look at SPSS Statistics 18
    The Chart Builder shows a list of graph types and allows us to
    specify which variable(s) to summarize as well as many options. This is
    true for many commands; we’ll typically use the default options early in
    this book, moving to other choices as you become more familiar with
    statistics and with SPSS.
    2. Drag the Simple Bar chart
    icon to the Preview area.
    1.In the Gallery of chart
    types, we’ll first select Bar
    
    In the lower left of the dialog, note that Bar chart is the default
    option. There are basic types of bar chart here, symbolized by the
    icons in the lower center of the dialog. The first of these icons
    represents a simple bar chart; drag it to the Preview area.
    The Preview area of the Chart Builder displays a prototype of the
    graph we are starting to build. In our graph, we’ll want to display two
    bars to represent the average practice times of the girls and the boys. To
    do this, we’ll place sex on the horizontal axis and average practice time
    on the vertical. In the Chart Builder, This is easily accomplished by
    dragging the variables to the axes.
    Notice that the three variables are initially listed by description
    and name on the left side of the dialog box, along with special symbols:
    Nominal variable (qualitative)
    Scale variable (quantitative)
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    Creating a Bar Chart
    9
    
    In the upper left of the dialog, highlight Sex of swimmer and drag it
    to the horizontal axis within the preview.
    
    Similarly, click and drag Practice time to the vertical axis. In the
    preview, note that the axis is now labeled Mean Practice Time. By
    default, SPSS suggests summarizing this quantitative variable.
    
    It is good practice to place a title on graphs. In the lower portion
    of the dialog, click the tab marked Titles/Footnotes. Check the
    Title 1 box. In the Content area of the Element Properties dialog,
    type a title (we’ve chosen “Comparison of Female & Male Practice
    Times”). Then click Apply at the bottom of the Element Properties
    dialog and OK at the bottom of the Chart Builder dialog.
    You will now see a new window appear, containing a bar chart
    (see next page). This is the output Viewer, and contains two “panes.” On
    the left is the Outline pane, which displays an outline of all of your
    output. The Content pane, on the right, contains the output itself.
    Also, notice the menu bar at the top of the Viewer window. It is
    very similar to the one in the Data Editor, with some minor differences.
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    10
    Session 1 ΠA First Look at SPSS Statistics 18
    In general, we can perform statistical analysis from either window. Later,
    we’ll learn some data manipulation commands that can only be given
    from the Data Editor.
    This is the
    Contents pane
    This is the
    Outline pane
    Now look at the chart. The height of each bar corresponds to the
    simple average time of the males and females. What does the chart tell
    you about the original question: Did the males or females have a
    better practice that day?
    There is much more to a set of data than its average. Let’s look at
    another graph that can give us a feel for how the swimmers did
    individually and collectively. This graph is called a box-and-whiskers plot
    (or boxplot), and displays how the swimmers’ times were spread out.
    Boxplots are fully discussed in Session 4, but we’ll take a first look now.
    You may issue this command either from the Data Editor or the Viewer.
    
    Graphs h Chart Builder… The dialog reopens where we last left it,
    with the Titles tab foremost. Return to the Gallery tab and choose
    Boxplot from the gallery, dragging Simple Boxplot to the preview.
    Notice that the earlier selections still apply; our choice of
    variables is unchanged. This is often a very helpful feature of the Chart
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    Saving an Output File
    11
    Builder: we can explore different graphing alternatives without needing to
    redo all prior steps. Go ahead and click OK.
    The boxplot shows results for the males and females. There are
    two boxes, and each has “whiskers” extending above and below the box.
    In this case, the whiskers extended from the shortest to the longest time.
    The outline of the box reflects the middle three times, and the line
    through the middle of the box represents the median value for the
    swimmers.3
    Looking now at the boxplot, what impression do you have of
    the practice times for the male and female swimmers? How does
    this compare to your impression from the first graph?
    Saving an Output File
    At this point, we have the Viewer open with some output and the
    Data Editor with a data file. We have saved the data, but have not yet
    saved the output on a disk. This can sometimes be confusing for new
    users—the raw data files are maintained separately from the results we
    generate during a working session.
    3 The median of a set of points is the middle value when the observations
    are ranked from smallest to largest. With only five swimmers of each gender, the
    median values are just the time recorded for the third female and the third male.
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    12
    Session 1 ΠA First Look at SPSS Statistics 18
    
    File h Save As… In this dialog box, assign a name to the file (such
    as Session 1). This new file will save both the Outline and Content
    panes of the Viewer.
    Getting Help
    You may have noticed the Help button in the dialog boxes. SPSS
    features an extensive on-line Help system. If you aren’t sure what a term
    in the dialog box means, or how to interpret the results of a command,
    click on Help. You can also search for help on a variety of topics via the
    Help menu at the top of your screen. As you work your way through the
    sessions in this book, Help may often be valuable. Spend some time
    experimenting with it before you genuinely need it.
    Printing in SPSS
    Now that you have created some graphs, let’s print them. Be sure
    that no part of the outline is highlighted; if it is, click once in a clear area
    of the Outline pane. If a portion of the outline is selected, only that
    portion will print.
     Check with your instructor about any special considerations in selecting a
    printer or issuing a print command. Every system works differently in this
    matter.
    
    File h Print… This command will print the Contents pane of the
    Viewer. Click OK.
    Quitting SPSS
    When you have completed your work, it is important to exit the
    program properly. Virtually all Windows programs follow the same
    method of quitting.
    
    File h Exit You will generally see a message asking if you wish to
    save changes. Since we saved everything earlier, click No.
    That’s all there is to it. Later sessions will explain menus and
    commands in greater detail. This session is intended as a first look; you
    will return to these commands and others at a later time.
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    Session 2
    Tables and Graphs for One Variable
    Objectives
    In this session, you will learn to do the following:
    • Retrieve data stored in a SPSS data file
    • Explore data with a Stem-and-Leaf display
    • Create and customize a histogram
    • Create a frequency distribution
    • Print output from the Viewer window
    • Create a bar chart
    Opening a Data File
    In the previous session, you created a SPSS data file by entering
    data into the Data Editor. In this lab, you’ll use several data files that are
    available on your disk. This session begins with some data about traffic
    accidents in the United States. Our goal is to get a sense of how
    prevalent fatal accidents were in 2005.
     NOTE:
    The location of SPSS files depends on the configuration of your
    computer system. Check with your instructor.
    
    Choose File h Open h Data… A dialog box like the one shown on
    the next page will open. In the Look in: box, select the appropriate
    directory for your system or network, and you will see a list of
    available worksheet files. Select the one named States. (This file
    name may appear as States.sav on your screen, but it’s the same
    file.)
    13
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    14
    Session 2 Š Tables and Graphs for One Variable
    Click on States.sav
    Click Open, and the Data Editor will show the data from the
    States file. Using the scroll bars at the bottom and right side of the
    screen, move around the worksheet, just to look at the data. Move the
    cursor to the row containing variable names (e.g. state, MaleDr, FemDr,
    etc.) Notice that the variable labels appear as the cursor passes each
    variable name. Consult Appendix A for a full description of the data files.
    Exploring the Data
    SPSS offers several tools for exploring data, all found in the
    Explore command. To start, we’ll use the Stem-and-Leaf plot to look at
    the number of people killed in automobile accidents in 2005.
    
    Analyze h Descriptive Statistics h Explore… We want to select
    Number of fatalities in accidents in 2005 [accfat2005]. As shown in this
    dialog box, the variable names appear to be truncated.
    1. Highlight this variable and click once
    2. Click on arrow to
    select the variable
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    Exploring the Data
    
    15
    You can increase the size of a dialog box by placing the cursor on
    any edge and dragging the box out. Try it now to make it easier to
    find the variable of interest here. Once you select the variable,
    click OK.
    Many SPSS dialog boxes show a list of variables, as this one does.
    Here the variables are listed in the same order as in the Data Editor. In
    other dialog boxes, they may be listed alphabetically by variable label.
    When you move your cursor into the list, the entire label becomes
    visible. The variable name appears in square brackets after the label.
    This book often refers to variables by name, rather than by label. If you
    cannot find the variable you are looking for, consult Appendix A.
    By default, the Explore command reports on the extent of missing
    data, generates a table of descriptive statistics, creates a stem-and-leaf
    plot, and constructs a box-and-whiskers plot. The descriptive statistics
    and boxplot are treated later in Session 4.
    The first item in the Viewer window summarizes how many
    observations we have in the dataset; here there are 51 “cases,” or
    observations, in all. For every one of the 50 states plus the District of
    Columbia, we have a valid data value, and there is no missing data.
    Below that is a table of descriptive statistics. For now, we bypass
    these figures, and look at the Stem-and-Leaf plot, shown on the next
    page and explained below.
    In this output, there are three columns of information,
    representing frequency, stems, and leaves. Looking at the notes at the
    bottom of the plot, we find that each stem line represents a 1000’s digit,
    and each leaf represents 1 state.
    Note that the first five rows have a 0 stem. The first row
    represents states between 0 and 199 fatalities while the second row
    represents states with 200 to 299 fatalities, and so on. Thus, in the first
    row of output we find that 11 states had between 0 and 199 automobile
    accident fatalities in 2005. There are four “0-leaves” in that first row;
    these represent four states that had fewer than 100 fatalities that year.
    The seven “1-leaves” (highlighted below) represent seven states with
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    16
    Session 2 Š Tables and Graphs for One Variable
    between 100 and 199 fatalities. Moving down the plot, the row with a
    stem of 1 and leaves of 6 and 7 indicate that one state had fatalities in
    the 1600s and one state had fatalities in the 1700s. Finally, in the last
    row, we find 3 states that had at least 3504 fatalities, and that these are
    considered extreme values.
    There are 5 rows with a
    stem of 0. Leaves in the
    first row are values under
    200; the second row is for
    values 200-399, etc.
    Each stem is a
    1000’s digit (e.g. 2
    stands for 2000)
    Let’s take a close look at the first row of output to review what it
    means.
    Frequency
    Stem &
    11.00
    0 .
    Leaf
    00001111111
    11 states had fewer
    than 200 fatalities.
    These 7 states had
    between 100 and 199
    fatalities.
    The Stem-and-Leaf plot helps us to represent a body of data in a
    comprehensible way, and permits us to get a feel for the “shape” of the
    distribution. It can help us to develop a meaningful frequency
    distribution, and provides a crude visual display of the data. For a better
    visual display, we turn to a histogram.
    Creating a Histogram
    In the first session, we created a bar graph and boxplots. In this
    session, we’ll begin by making a histogram.
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    Creating a Histogram
    17
    
    Graphs h Chart Builder…. Under the Choose From menu,
    select Histogram. Four choices of histograms will now appear.
    Drag the first histogram (simple) to the preview area. As
    shown below, select AccFat2005 by clicking on it and dragging
    it to the X axis. By default, histograms have frequency on the
    Y axis so this part of the graph is all set.
    
    Click on the Titles/Footnotes tab, select Title 1, and type a title
    for this graph (e.g., 2005 Traffic Fatalities) in the space marked
    Content within the Element Properties window. Click Apply.
    Now place your name on your graph by selecting Footnote 1,
    typing in the content box, and clicking Apply. Your histogram
    will appear in the Viewer window after you click OK.
    Click here to add a title
    The horizontal axis represents a number of fatalities, and the vertical
    represents the number of states reporting that many cases. The
    histogram provides a visual sense of the frequency distribution. Notice
    that the vast majority of the states appear on the left end of the graph.
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    18
    Session 2 Š Tables and Graphs for One Variable
    Outlier
    How would you describe the shape of this distribution?
    Compare this histogram to the Stem-and-Leaf plot. What important
    differences, if any, do you see?
    Also notice the short bars at the extreme right end of the graph.
    What state do you think might lie furthest to the right? Look in the
    Data Editor to find that outlier.
    In this histogram, SPSS determined the number of bars, which
    affects the apparent shape of the distribution. Using the Chart Editor we
    can change the number of bars as follows:
    
    Double click anywhere on your histogram which will open up the
    Chart Editor (see next page).
    
    Now double click on the bars of the histogram. A Properties dialog
    box will appear. Under the Binning tab, choose Custom for the X
    axis. Type in 24 as the number of intervals as shown in the
    illustration on the next page. Click Apply and you’ve changed the
    number of intervals in your histogram.
    
    You can experiment with other numbers of bars as well. When
    you are satisfied, close the Chart Editor by clicking on the r
    button in the upper right corner.
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    Creating a Histogram
    19
    How does this compare to your first histogram? Which
    graph better summarizes the dataset? Explain.
    We would expect more populous states to have more fatalities
    than smaller states. As such, it might make more sense to think in terms
    of the proportion of the population killed in accidents in each state. In
    our dataset, we have a variable called Traffic fatalities per 100,000 pop, 2005
    [RateFat].
    
    Use the Chart Builder to construct a histogram for the variable
    Ratefat. Note that you can replace Accfat2005 with Ratefat by
    dragging the new variable into the horizontal axis position.
    
    In the Element Properties box, you will see Edit Properties and then
    choose Title 1. Notice that the title of the previous graph is still
    there. Replace it with a new title, click Apply, and OK.
    How would you describe the shape of this distribution?
    What was the approximate average rate of fatalities per 100,000
    residents in 2005? Is there an outlier in this analysis? In which
    states are traffic fatalities most prevalent?
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    20
    Session 2 Š Tables and Graphs for One Variable
    
    Now, return to the Chart Builder. In the Element Properties box,
    under Statistics, choose Cumulative Count; click Apply and OK.
    A cumulative histogram displays cumulative frequency. As you
    read along the horizontal axis from left to right, the height of the bars
    represents the number of states experiencing a rate less than or equal to
    the value on the horizontal axis. Compare the results of this graph to the
    prior graph. About how many states had traffic fatality rates of less
    than 20 fatalities per 100,000 population?
    Frequency Distributions
    Let’s look at some questions concerning qualitative data. Switch
    from the Viewer window back to the Data Editor window.
    
    File h Open h Data… Choose the data file Census2000. SPSS
    allows you to work with multiple data files, but you may wish to
    close States.
    This file contains a random sample of 1270 Massachusetts
    residents, with their responses to selected questions on the 2000 United
    States Decennial Census. One question on the census form asked how
    they commute to work. In our dataset, the relevant variable is called
    Means of Transportation to Work [TRVMNS]. This is a categorical, or nominal,
    variable. The Bureau of Census has assigned the following code numbers
    to represent the various categories:
    Value
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    Meaning
    n/a, not a worker or in the labor force
    Car, Truck, or Van
    Bus or trolley bus
    Streetcar or trolley car
    Subway or elevated
    Railroad
    Ferryboat
    Taxicab
    Motorcycle
    Bicycle
    Walked
    Worked at Home
    Other Method
    To see how many people in the sample used each method, we can
    have SPSS generate a simple frequency distribution.
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    Frequency Distributions
    
    21
    Analyze h Descriptive Statistics h Frequencies… Select the
    variable Means of Transportation to Work [TRVMNS] and click OK.
    In the Viewer window, you should now see this:
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    22
    Session 2 Š Tables and Graphs for One Variable
    Among people who work, which means of transportation is
    the most common? The least common? Be careful: the most common
    response was “not working” at all.
    Another Bar Chart
    To graph this distribution, we should make a bar chart.
    
    Graphs h Chart Builder … Choose Bar. Select the first bar graph
    option (simple) by dragging it to the preview area. Drag the
    TRVMNS variable to the X axis. Place a title and your name on
    the graph and click OK.
    The bar chart and frequency distribution should contain the
    same information. Do they? Comment on the relative merits of
    using a frequency table versus a bar chart to display the data.
    Printing Session Output
    Sometimes you will want to print all or part of a Viewer window.
    Before printing your session, be sure you have typed your name into the
    output. To print the entire session, click anywhere in the Contents pane
    of the Viewer window (be sure not to select a portion of the output), and
    then choose File h Print. To print part of a Viewer window, do this:
    
    In the Outline pane of the Viewer window (the left side of the
    screen), locate the first item of the output that you want to print.
    Position the cursor on the name of that item, and click the left
    mouse button.
    
    Using the scroll bars (if necessary), move the cursor to the end of
    the portion you want to print. Then press Shift on the keyboard
    and click the left mouse button. You’ll see your selection
    highlighted, as shown here.
    
    File h Print… Notice that the Selection button is already marked,
    meaning that you’ll print a selection of the output within the
    Contents pane. Click OK.
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    Moving On…
    23
    Outline pane
    Only the highlighted
    portions will print
    Contents
    pane
    Moving On…
    Using the skills you have practiced in this session, now answer
    the following questions. In each case, provide an appropriate graph or
    table to justify your answer, and explain how you drew your conclusion.
    1. (Census2000 file) Note that the TRVMNS variable includes the
    responses of people who don’t have jobs. Among those who do
    have jobs, what proportion use some type of public
    transportation (bus, subway, or railroad)?
    For the following questions, you will need to use the files States,
    Marathon, AIDS, BP, and Nielsen (see Appendix A for detailed file
    descriptions). You may be able to use several approaches or commands
    to answer the question; think about which approach seems best to you.
    States
    2. The variable named BAC2004 refers to the legal blood alcohol
    threshold for driving while intoxicated. All states set the
    threshold at either .08 or .10. About what percentage of states
    use the .08 standard?
    3. The variable called Inc2004 is the median per capita income
    for state residents in 2004. Did residents of all states earn
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    24
    Session 2 Š Tables and Graphs for One Variable
    about the same amount of income? What seems to be a
    typical amount? How much variation is there across states?
    4. The variable called mileage is the average number of miles
    driven per year by a state’s drivers. With the help of a Stemand-Leaf plot, locate (in the Data Editor) two states where
    drivers lead the nation in miles driven; what are they?
    Marathon
    This file contains the finish times for the wheelchair racers in the
    100th Boston Marathon.
    5. The variable Country is a three-letter abbreviation for the
    home country of the racer. Not surprisingly, most racers were
    from the USA. What country had the second highest number
    of racers?
    6. Use a cumulative histogram to determine approximately what
    percentage of wheelchair racers completed the 26-mile course
    in less than 2 hours, 10 minutes (130 minutes).
    7. How would you characterize the shape of the histogram of the
    variable Minutes? (Experiment with different numbers of
    intervals in this graph.)
    AIDS
    This file contains data related to the incidence of AIDS around
    the world.
    8. How would you characterize the shape of the distribution of
    the number of adults living with HIV/AIDS in 2005? Are there
    any outlying countries? If so, what are they?
    9. Consider the 2003 infection rate (%). Compare the shape of
    this distribution to the shape of the distribution in the
    previous question.
    BP
    This file contains data about blood pressure and other vital signs
    for subjects after various physical and mental activities.
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    Moving On…
    25
    10. The variable sbprest is the subject’s systolic blood pressure at
    rest. How would you describe the shape of the distribution of
    systolic blood pressure for these subjects?
    11. Using a cumulative histogram, approximately what percent of
    subjects had systolic pressure of less than 140?
    12. The variable dbprest is the subject’s diastolic blood pressure at
    rest. How would you describe the shape of the distribution of
    diastolic blood pressure for these subjects?
    13. Using a cumulative histogram, approximately what percent of
    subjects had diastolic pressure of less than 80?
    Nielsen
    This file contains the Nielsen ratings for the 20 most heavily
    watched television programs for the week ending September 24, 2007.
    14. Which of the networks reported had the most programs in the
    top 10? Which had the fewest?
    15. Approximately what percentage of the programs enjoyed
    ratings in excess of 11.5?
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    Session 3
    Tables and Graphs for Two Variables
    Objectives
    In this session, you will learn to do the following:
    • Cross-tabulate two variables
    • Create several bar charts comparing two variables
    • Create a histogram for two variables
    • Create an XY scatterplot for two quantitative variables
    Cross-Tabulating Data
    The prior session dealt with displays of a single variable. This
    session covers some techniques for creating displays that compare two
    variables. Our first example considers two qualitative variables. The
    example involves the Census data that you saw in the last session, and
    in particular addresses the question: “Do men and women use the same
    methods to get to work?” Since sex and means of transportation are
    both categorical data, our first approach will be a joint frequency table,
    also known as a cross-tabulation.
    
    Open the Census file by selecting File h Open h Data…, and
    choosing Census2000.
    
    Analyze h Descriptive Statistics h Crosstabs… In the dialog box
    (next page), select the variables Means of transportation to work
    [TRVMNS] and Sex [sex], and click OK. You’ll find the crosstabulation in the Viewer window. Who makes greater use of
    cars, trucks, or vans: Men or women? Explain your
    reasoning.
    27
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    28
    Session 3 ΠTables and Graphs for Two Variables
    The results of the Crosstabs command are not mysterious. The
    case processing summary indicates that there were 1270 cases, with no
    missing data. In the crosstab itself, the rows of the table represent the
    various means of transportation, and the columns refer to males and
    females. Thus, for instance, 243 women commuted in a car, truck, or
    van.
    Simply looking at the frequencies could be misleading, since the
    sample does not have equal numbers of men and women. It might be
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    Editing a Recent Dialog
    29
    more helpful to compare the percentage of men commuting in this way to
    the percentage of women doing so. Even percentages can be misleading if
    the samples are small. Here, fortunately, we have a large sample. Later
    we’ll learn to evaluate sample information more critically with an eye
    toward sample size.
    The cross-tabulation function can easily convert the frequencies
    to relative frequencies. We could do this by returning to the Crosstabs
    dialog box following the same menus as before, or by taking a slightly
    different path.
    Editing a Recent Dialog
    Often, we’ll want to repeat a command using different variables or
    options. For quick access to a recent command, SPSS provides a special
    button on the toolbar below the menus. Click on the Dialog Recall
    button (shown to the right), and you’ll see a list of recently issued
    commands. Crosstabs will be at the top of the list; click on
    Crosstabs, and the last dialog box will reappear.
    
    To answer the question posed above, we want the values in each
    cell to reflect frequencies relative to the number of women and
    men, so we want to divide each by the total of each respective
    column. To do so, click on the button marked Cells, check Column
    Percentages, click Continue, and then click OK. Based on this
    table, would you say that men or women are more likely to
    commute by car, truck, or van?
    
    Now try asking for Row Percentages (click on Dialog Recall). What
    do these numbers represent?
    More on Bar Charts
    We can also use a bar chart to analyze the relationship between
    two variables. Let’s look at the relationship between two qualitative
    variables in the student survey: gender and seat belt usage. Students
    were asked how frequently they wear seat belts when driving: Never,
    Sometimes, Usually, and Always. What do you think the students said?
    Do you think males and females responded similarly? We will create a
    bar chart to help answer these questions.
    
    In the Data Editor, open the file called Student.
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    30
    Session 3 ΠTables and Graphs for Two Variables
    
    Graphs h Chart Builder… We used this command in the prior
    session. From the Gallery choices, choose Bar. Then drag the
    second bar graph icon (clustered) to the preview area. We must
    specify a variable for the horizontal axis, and may optionally
    specify other variables.
    
    Drag Frequency of seat belt usage [belt] to the horizontal axis. If we
    were to click OK now, we would see the total number of students
    who gave each response. But we are interested in the comparison
    of responses by men and women.
    
    Drag Gender to the Cluster on X box. Click OK.
    We want to cluster the
    bars by Gender.
    The Cluster setting
    creates side-by-side
    bars for males and
    females
    Look closely at the bar chart that you have just created. What
    can you say about the seat belt habits of these students?
    In this bar chart, the order of axis categories is alphabetical. With
    this ordinal variable, it would be more logical to have the categories
    sequenced by frequency: Never, Sometimes, Usually, and Always. We can
    change the order of the categories either by opening the Chart Editor or
    by recalling the Chart Builder dialog. Return to the prior Chart Builder
    dialog.
    
    Under Element Properties box on the right, select X-axis1 (Bar1).
    Under Categories, use the up and down arrows to place the order
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    More on Bar Charts
    31
    of categories on the horizontal axis
    in the following order: Never,
    Sometimes, Usually, Always.
    
    Click Apply in Element Properties
    and then OK in the main Chart
    Builder dialog. The resulting graph
    should be clearer to read and
    interpret.
    This graph uses clustered bars to
    compare the responses of the men and the
    women. A clustered bar graph highlights
    the differences in belt use by men and
    women, but it’s hard to tell how many
    students are in each usage category. A stacked bar chart is a useful
    alternative.
    
    Select the dialog recall icon as we did previously and choose Chart
    Builder. Drag the third bar graph icon (stacked) to the preview
    area. The horizontal axis variable (frequency of seat belt usage) will
    stay the same. However, you will need to drag Gender to the Stack
    box
    
    Arrange the categories by frequency as done previously.
    Here are the clustered and stacked versions of this graph. Do
    they show different information? What impressions would a viewer
    draw from these graphs?
    Stacked
    Clustered
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    32
    Session 3 ΠTables and Graphs for Two Variables
    We can also analyze a quantitative variable in a bar chart. Let’s
    compare the grade point averages (GPA) of the men and women in the
    student survey. We might compare the averages of the two groups.
    
    Graphs h Chart Builder… Choose Bar and drag the first bar graph
    icon (simple) to the preview area.
    
    Drag Current GPA [gpa] to the vertical axis and Gender to the
    horizontal axis. Click OK
    The bars in the graph represent the mean, or average, of the GPA
    variable. How do the average GPAs of males and females compare?
    Comparing Two Distributions
    The bar chart compared the mean GPAs for men and women.
    How do the whole distributions compare? As a review, we begin by
    looking at the distribution of GPAs for all students.
    
    Graphs h Chart Builder… Choose Histogram and drag the first
    histogram icon (simple) to the preview area. Select Current GPA
    [gpa] as the variable, and click OK. You’ll see the graph shown
    here. How do you describe the shape of this distribution?
    Let’s compare the distribution of grades for male and female
    students. We’ll create two side-by-side histograms, using the same
    vertical and horizontal scales:
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    Scatterplots to Detect Relationships
    33
    
    Click the Dialog Recall button, and choose Chart Builder. We need
    to indicate that the graph should distinguish between the GPAs
    for women and men.
    
    Click on the Groups/Points ID tab. Select Columns Panel Variable
    then drag Gender into the Panel box, and click OK.
    What does this graph show about the GPAs of these
    students? In what ways are they different? What do they have in
    common? What reasons might explain the patterns you see?
    Scatterplots to Detect Relationships
    The prior example involved a quantitative and a qualitative
    variable. Sometimes, we might suspect a connection between two
    quantitative variables. In the student data, for example, we might think
    that taller students generally weigh more than shorter ones. We can
    create a scatterplot or XY graph to investigate.
    
    Graphs h Chart Builder… From the gallery choices, choose
    Scatter/Dot. Then drag the first scatter graph icon (simple) to the
    preview area. Select Weight in pounds [wt] as the y, or vertical axis
    variable, and Height in inches [ht] as the x variable. Click OK.
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    34
    Session 3 ΠTables and Graphs for Two Variables
    Look at the scatterplot, reproduced here. Describe what you see
    in the scatterplot. By eye, approximate the range of weights of
    students who are 5’2” (or 62 inches) tall. Roughly how much more
    do 6’2” students weigh?
    
    We can easily incorporate a third variable into this graph.
    Recall the Chart Builder and drag the second scatterplot icon
    (grouped) to the preview area. Drag Gender to the box marked
    Set Color in the preview area. Click OK.
    In what ways is this graph different from the first
    scatterplot? What additional information does it convey? What
    generalizations can you make about the heights and weights of
    men and women? Which points might we consider to be outliers?
    Moving On…
    Create the tables and graphs described below. Refer to Appendix
    A for complete data descriptions. Be sure to title each graph,
    including your name. Print the completed graphs.
    Student
    1. Generate side-by-side histograms of the distribution of
    heights, separating men and women. Comment on the
    similarities and differences between the two groups.
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    Moving On…
    35
    2. Do the same for students’ weights.
    Bev
    3. Using the Interactive Bar Chart command, display the mean of
    Revenue per Employee, by SIC category. Which beverage
    industry generates the highest average revenue by employee?
    4. Make a similar comparison of Inventory Turnover averages.
    How might you explain the pattern you see?
    SlavDiet
    In Time on the Cross: The Economics of American Negro Slavery, by
    Robert William Fogel and Stanley Engerman, the diets of slaves and the
    general population are compared.
    5. Create two bar charts summing up the calories consumed by
    each group, by food type. How did the diets of slaves compare
    to the rest of the population, according to these data? [NOTE:
    you want the bars to represent the sum of calories]
    Galileo
    In the 16th century, Galileo conducted a series of famous
    experiments concerning gravity and projectiles. In one experiment, he
    released a ball to roll down a ramp. He then measured the total
    horizontal distance which the ball traveled until it came to a stop. The
    data from that experiment occupy the first two columns of the data file.
    In a second experiment, a horizontal shelf was added to the base
    of the ramp, so that the ball rolled directly onto the shelf from the ramp.
    Galileo recorded the vertical height and horizontal travel for this
    apparatus as well, which are in the third and fourth column of the file.1
    6. Construct a scatterplot for the first experiment, with release
    height on the x axis and horizontal distance on the y axis.
    Describe the relationship between x and y.
    7. Do the same for the second experiment.
    1 Sources: Drake, Stillman. Galileo at Work, (Chicago: University of
    Chicago Press, 1978); Dickey, David A. and Arnold, J. Tim “Teaching Statistics
    with Data of Historic Significance,” Journal of Statistics Education, v.3, no. 1,
    1995.
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    36
    Session 3 ΠTables and Graphs for Two Variables
    AIDS
    8. Construct a bar chart that displays the mean adult infection
    rate in 2003, by World Health Organization region. Which
    region of the world had the highest incidence of HIV/AIDS in
    2003?
    Mendel
    Gregor Mendel’s early work laid the foundations for modern
    genetics. In one series of experiments with several generations of pea
    plants, his theory predicted the relative frequency of four possible
    combinations of color and texture of peas.
    9. Construct bar charts of both the actual experimental
    (observed) results and the predicted frequencies for the peas.
    Comment on the similarities and differences between what
    Mendel’s theory predicted, and what his experiments showed.
    Salem
    In 1692, twenty persons were executed in connection with the
    famous witchcraft trials in Salem, Massachusetts. At the center of the
    controversy was Rev. Samuel Parris, minister of the parish at Salem
    Village. The teenage girls who began the cycle of accusations often
    gathered at his home, and he spoke out against witchcraft. This data file
    represents a list of all residents who paid taxes to the parish in 1692. In
    1695, many villagers signed a petition supporting Rev. Parris.
    10. Construct a crosstab of proParris status and the accuser
    variable. (Hint: Compute row or column percents, using the
    Cells button.) Based on the crosstab, is there any indication
    that accusers were more or less likely than nonaccusers to
    support Rev. Parris? Explain.
    11. Construct a crosstab of proParris status and the defend
    variable. Based on the crosstab, is there any indication that
    defenders were more or less likely than nondefenders to
    support Rev. Parris? Explain.
    12. Create a chart showing the mean (average) taxes paid, by
    accused status. Did one group tend to pay higher taxes than
    the other? If so, which group paid more?
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    Moving On…
    37
    Impeach
    This file contains the results of the U.S. Senate votes in the
    impeachment trial of President Clinton in 1999.
    13. The variable called conserv is a rating scale indicating how
    conservative a senator is (0 = very liberal, 100 = very
    conservative). Use a bar chart to compare the mean ratings of
    those who cast 0, 1, or 2 votes to convict the President.
    Comment on any pattern you see.
    14. The variable called Clint96 indicates the percentage of the
    popular vote cast for President Clinton in the senator’s home
    state in the 1996 election. Use a bar chart to compare the
    mean percentages for those senators who cast 0, 1, or 2 votes
    to convict the President. Comment on any pattern you see.
    GSS2004
    These questions were selected from the 2004 General Social
    Survey. For each, construct a crosstab and discuss any possible
    relationship indicated by your analysis.
    15. Does a person’s political outlook (liberal vs. conservative)
    appear to vary by their highest educational degree?
    16. One question asks respondents if they consider themselves
    happily married. Did women and men tend to respond
    similarly? Did responses to this question tend to vary by
    region of the country?
    17. One question asks respondents about how frequently they
    have sex. Did men and women respond similarly?
    18. How does attendance at religious services vary by region of
    the country?
    GSS942004
    This file contains responses to a series of General Social Survey
    questions from 1994 and 2004. Respondents were different in the two
    years. Use a bar chart to display the percentages of responses to the
    following questions, comparing the 1994 and 2004 results. Comment on
    the changes, if any, you see in the ten-year comparison.
    19. Should marijuana be legalized?
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    38
    Session 3 ΠTables and Graphs for Two Variables
    20. Should abortion be allowed if a woman wants one for any
    reason?
    21. Should colleges permit racists to teach?
    22. Are you afraid to walk in your neighborhood at night?
    States
    23. Use a scatterplot to explore the relationship between the
    number of fatal injury accidents in a state and the population
    of the state in 2005. Comment on the pattern, if any, in the
    scatterplot.
    24. Use a scatterplot to explore the relationship between the
    number of fatal injury accidents in a state and the mileage
    driven within the state in 2005. Comment on the pattern, if
    any, in the scatterplot.
    Nielsen
    25. Chart the mean (average) rating by network. Comment on
    how well each network did that week. (Refer to your work in
    Session 2.)
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    Session 4
    One-Variable Descriptive Statistics
    Objectives
    In this session, you will learn to do the following:
    • Compute measures of central tendency and dispersion for a
    variable
    • Create a box-and-whiskers plot for a single variable
    • Compute z-scores for all values of a variable
    Computing One Summary Measure for a Variable
    There are several measures of central tendency (mean, median,
    and mode) and of dispersion (range, variance, standard deviation, etc.)
    for a single variable. You can use SPSS to compute these measures. We’ll
    start with the mode of an ordinal variable.
    
    Open the data file called Student. The variables in this file are
    student responses to a first-day-of-class survey.
    One variable in the file is called Drive. This variable represents
    students’ responses to the question, “How would you rate yourself as a
    driver?” The answer codes are as follows:
    1 = Below average
    2 = Average
    3 = Above Average
    We’ll begin by creating a frequency distribution:
    39
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    40
    Session 4 ΠOne-Variable Descriptive Statistics
    
    Analyze h Descriptive Statistics h Frequencies… Scroll down the
    list of variables until you find How do you rate your driving? [drive].
    Select the variable, and click OK. Look at the results. What was
    the modal response? What strikes you about this frequency
    distribution? How many students are in the “middle”? Is
    there anything peculiar about these students’ view of
    “average”?
    1. Highlight this variable
    2. Click here to select
    the variable
    Frequencies
    Statistics
    How do you rate your driving?
    N
    Valid
    218
    Missing
    1
    One student did
    not answer
    How do you rate your driving?
    Valid
    Missing
    Total
    Below Average
    Average
    Above Average
    Total
    System
    Frequency
    8
    106
    104
    218
    1
    219
    Percent
    3.7
    48.4
    47.5
    99.5
    .5
    100.0
    Valid Percent
    3.7
    48.6
    47.7
    100.0
    Cumulative
    Percent
    3.7
    52.3
    100.0
    What does each column above tell you?
    Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
    Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
    Computing One Summary Measure for a Variable
    41
    Drive is a qualitative variable with three possible values. Some
    categorical variables have only two values, and are known as binary
    variables. Gender, for instance, is binary. In this dataset, there are tw…

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