NEW: Logging into ASUAD domain

1. NEW: Logging into ASUAD domain • “Returning” ASU Students will no longer be able to log into the ASU.EDU (Kerberos Realm) domain. • All Students must now log into the ASUAD domain in order to access ASU Resources on the computer • Use pull-down arrow to select ASUAD • If you can’t log into the ASUAD, then you will have to reset your password.

2.  SPSS WORKSHOP  Please Log onto Your Computer Station User Name: type in your user ID Password: type in your password Domain: ASUAD If you have trouble signing in: Then try signing in with westguest account User Name: westguest Password: asuwest Domain: (this computer) Sometime after today’s class, please go to technopolis (basement of library) to get your user ID fixed.

3. Open up your Internet Explorer http://www.west.asu.edu/StatLab • At the end of the URL address, type in StatLab, then hit enter. • Click on Workshops • Click on Data Used for SPSS Workshops • Click on Res_Stat_COE_501.sav • Please save it onto your Desktop. • Do not open data, but close it • Close the internet browser.

4. Brief Introduction to Statistics with SPSS Roger Berger, Mathematical Sciences & Applied Computing Rico Rivera Josh Fox Statistics Laboratory http://www.west.asu.edu/StatLab

5. Workshop Overview • Workshop Objectives (p 1) • Understand Basic Organization of SPSS • How to input raw data • How raw data file is edited in variable view • Introduce descriptive statistics • Correlation Analyses • Table of Contents (p 2)

6. SPSS Accessibility (p. 3) • Network access • Technopolis (basement of library) • Desktop (hard drive) installed • Computer Classrooms • Stat Lab

7. Accessing the Statistic Laboratory • Location: CLCC 107 • Phone: (602) 543-6117 • Website: http://www.west.asu.edu/StatLab • Operating hours: See website (changes on a weekly basis) • When using the lab, students sign in at the front counter

8. Support for Statistics (p. 4) • Function of Stat Lab (also pp. 13-15) • The Stat Lab staff assists students with aspects of statistical software. • The Stat Lab is not set up to provide one-on-one tutorial service for students on a regular basis. • However, we have and frequently do answer general statistic questions. • Statistics Tutoring • Learning Enhancement Center • TRiO (SSS) program

9. Overview of the Research Process Page 5 DISCUSSION SECTION Interpretation Were the hypotheses supported? Why or why not? Any limitations? What are the Implications? Suggestions for further research? RESULTS SECTION Data Analysis Descriptive Statistics Inferential Statistics METHODS SECTION Measures or Instruments Validity - What did we measure? Reliability – How precise was the measure METHODS SECTION Participant Characteristics Who did you sample? Procedures How did we do the observations? Also look at APA outline (pp 14-16) AT THE END OF LITERATURE REVIEW OR INTRODUCTION Based on theory & literature review, what is (are) the question(s) you want to address? What are respondents’ attitude toward research and statistics? Are attitudes toward research and statistics related? Do males and females have different attitudes? Is there a relationship between age and attitudes? Statistics Lab CLCC 107 www.west.asu.edu/StatLab (602) 543-6117

10. Procedures for Data Analysis (p. 6) Data  StatPak  Output  Interpretation • Collect & organize data • Input & edit the data • Analyze data or create graphs • State results and interpret

11. Please fill out the Research & Statistical Attitude Survey(Remove the last page of your handout) • Response • Strongly Disagree • Disagree • Agree • Strongly Agree • Sex • Male • Female • Age • Under 25 • 25 – 40 • Over 40 • Response • Strongly Disagree = 1 • Disagree = 2 • Agree = 3 • Strongly Agree = 4 • Sex • Male = 1 • Female = 2 • Age • Under 25 = 1 • 25 – 40 = 2 • Over 40 = 3 • Having ID column will help you keep track of cases • What do the first 5 questions (items) measure? • What do items 6 through 10 measure?

12. Let’s Launch SPSS (p. 7) • Menu Bar • Data & Transform are used to manipulate the data in data editor (i.e., spread sheet) • Analyze and Graphs are used to create output • Tabs (bottom left hand corner of data editor) • Data View • Columns: Variables (e.g., questions on survey) • Rows: Cases (e.g., survey) • Variable View • How we edit the variables • Give variables their names, labels, etc • Enter you survey into Data View

13. Editing the raw data • Variable View • Tab (located lower left hand corner) • Pages 10 – 11 of your handout

14. Save the data set to your desktop. • Call it “edited data” & your initials • E.g., editeddataJF • Now we are going to open up a new data set. • File > open > Data • Navigate to your desktop • Open up the data file that you downloaded from our website.

15. What are the Levels of Measurement? (supplemental information: p. 17) • Ratio (e.g., lengths, weights, volumes, capacities, rates): • Different categories • Rankable categories • Constant equal-sized Intervals • Absolute Zero (physical significance) (e.g., temperature in Kelvin, time) • Interval (temperature on Celsius and Fahrenheit, age): • Different categories • Rankable categories • Constant equal-sized Intervals (can be expressed numerically) • Ordinal (e.g.., dominance hierarchy): • Different Categories • Categories are rankable • Nominal (a.k.a. attributes) • Different Categories (e.g.., sex, species, phylum, location)

16. Descriptive Statistics • Presentation of entire distribution • Frequency Distribution: organized tabulation of the number (or percentage) of individuals in each category on the scale of measurement • Can be presented in a table or in a graph • Measures of Central Tendency • Identifies a single score that represents an entire data set • Best example of average or most typical score • Measures of Variability (Dispersion) • Provides a description of how spread out the scores are in a distribution • Provides a measure of how accurately a single score selected from a distribution represents the entire set

17. Presentation of entire distribution of a variable

18. Central Tendency and Dispersion

19. Central Tendency and Dispersion (p 17)

20. Back to SPSS • Create table frequency & a chart (p 18) • Create measures of central tendency (p 19) • Create measures of dispersion (p 19)

21. How can we summarize the distribution of the below variable? Gender • Male • Female What is the level of measurement? (see page 17, 1st slide) Nominal Should we use a bar graph or a histogram? (see page 17, 2nd slide) Bar Graph

22. Presentation of an Nominal Variable • Click Analyze > click Descriptive Statistics > then click Frequencies. • You should see a dialog box called Frequencies. • On the left side you should see a list of variables and on the right you should see a Variable(s) box. • In the variable list (left side) look for a variable either called “Gender” or sex. • Select this variable by clicking on it, then click ►to place the variable in the Variable(s) box. • Make sure that there is a check marknext to Display frequency tables. • Click Charts. • You will see a dialog box called Frequencies: Charts • Click Bar Charts. Click Continue. • Click OK.

23. Frequency and Percentage Distribution of Gender (N=96)

24. We want to know the central tendency for the below variable. • Age of respondent 18 – 89. Actual age in years What is the level of measurement? Which measure of central tendency should we use?

25. Central Tendency & Variability for Interval & Ratio Variables (p 19) • Click Analyze > click Descriptive Statistics > then click Descriptive. • You will see a dialog box called Descriptives. • Select the variable • one is called “AGE”. • Click Options. You will see a dialog box called Descriptives: Options • By default Mean,Std Deviation, Minimum, & Maximum should be selected • click Continue. • Click OK.

26. Mean & Standard Deviation of Age

27. Summary of what we have discussed. • Presentation of the variable distributions • Central tendency • Variability (dispersion)

28. Inferential Statistics (p 20) • Techniques that allow us to study samples and then make generalizations about the population from which they were selected. • Sample: a set of individuals selected from a population, usually intended to represent the population in a research study. • Population: The collection of all individuals (cases) in which the researcher is interested.

29. Inferential Statistics • Statistical Significance (probability) • Probability is used to predict what kind of samples are likely to be obtained from a population. • Probability establishes a connection between populations and samples • Relying on this connection, inferences of populations can based on sample statistics

30. Linear Relationships • Science involves a search for relationships between variables. For example, is there is a relationship between the amount of rainfall and crop growth? • Sometimes, it is reasonable to expect a consistent, orderly relation between two variables: As X changes, Y also changes in a predictable way.

31. Linear Relationships with SPSS • Two options • Regression Analysis Analyze > Regression > Linear … • Correlation Coefficient (simpler) Analyze > Correlate > Bivariate … • Scatter Plot Graphs > Scatter > Simple ...

32. Correlation

33. Scatter Plot Example of a Positive Relationship

34. Statistical Functions(supplemental information) • Table on pages 22-23 • Statistic • Function/description • SPSS procedures • APA Outline of Empirical Research Paper(pp 14-16) • Backing up your files (p 26) • Resources posted on our website • Developing Questionnaires • Oral Interview Guidelines

35. Thanks for having us  Do keep the “Research and Statistics Attitude Survey” you filled out. Please close SPSS. Please fill out and turn in the “SPSS Workshop Evaluation” when you leave the room Do not forget to log off your computer Have a good night. 