# This chapter reviews the general issues involving data analysis and introduces

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1 Research Skills for Psychology Majors: Everything You Need to Know to Get Started Data Preparation With SPSS This chapter reviews the general issues involving data analysis and introduces SPSS, the Statistical Package for the Social Sciences, one of the most commonly used software programs now used for data analysis. History of Data Analysis in Psychology The chapter Computers in Psychology introduced the history of psychology s relationship with computers. In that chapter, we discussed the early use of the term computer as a person who performed computations using paper and pencil or electromechanical devices such as adding machines. Statistical analyses were initially performed by these biological computers for psychological research. Using slide rules and a variety of progressively complex machines, they performed large statistical analyses, laboriously and slowly. The legacy of this hand calculation phase could be found in statistics texts up to as late as the 1980s. Older stats books presented formulae for statistical procedures in a form primarily suitable for calculation, and much effort was spent trying to come up with calculation-friendly formulae for the most complicated analyses. The problem with this approach to formulae writing was that the computational formulae were not very effective in communicating the concepts underlying the calculations. Most modern texts use conceptual or theoretical formulae to teach the concepts and software to perform the calculations. The invention of practical computers by the 1960s brought about a huge change in statistics and in how we deal with data in psychology, and these changes have not stopped yet. Early on, programmers began writing small programs in Assembly language (can you spell p-a-i-n?) and in FORTRAN to compute individual statistical tests. When the author was an undergraduate he discovered a library of FOR- TRAN procedures and was very impressed by the possibilities, compared to his slide rule. The way this worked was: a set of punch cards described the statistical program to be run (e.g., a t-test), and a set of cards contained the data on which to run it. The cards were fed into a card reader, and a while later a printout appeared on the computer center s high-speed line-printer with the results (unless you punched a card incorrectly). Eventually, the individual FORTRAN programs were collected together in packages. The package a single, large piece of software put everything together: a way to read the data, a way to transform variables (e.g., average several to make a scale score), and a standard way to tell the program which analyses to perform on which variables. Several major packages appeared: SPSS, the Statistical Package for the Social Sciences; SAS, the Statistical Analysis System; BMDP, Biomedical Data 2003 W. K. Gabrenya Jr. Version: 1.0

3 Page 3 Shows the data Shows the variable properties modules, each of which contains a set of related analyses. For example, the Base module can do all kinds of descriptive statistics, t-tests, correlations, linear regression, etc. The Advanced Statistics module adds some very exotic analyses of variance (ANOVAs). As more modules are added (purchased), the list of items in the Analyze menu grows. This modularity allows SPSS Inc. to add additional components to the program as new types of statistics are invented. It also helps the company harvest a lot of money since each module is quite expensive. Analyses are performed by choosing an item or sub-item in the Analysis menu then filling in all the details required for the analysis. All analyses present additional options that allow you to customize the analysis in various ways, but these are optional. For example, let s say that you want to calculate the grand mean of attitude toward tuition hikes for your sample. A grand mean is the average of all the people in the sample on a variable. Your analysis screen will look like this:

4 Page 4 Just as in the 1970s, when you run a SPSS analysis you are actually sending the program a set of arcane textual commands. These commands in a very strict syntax that communicates with the relevant underlying components of the program. For example, the grand mean analysis described above is actually performed by sending this command to the Descriptives component of the program: DESCRIPTIVES VARIABLES=attitude /STATISTICS=MEAN STDDEV MIN MAX. The GUI window that you used to perform this simple analysis created the command for you, and sent it to the Descriptives component of SPSS. If you click on the Paste button in an analysis window, the command syntax is saved for future use (as described in a later section). In other words, the friendly GUI is helping you construct this command syntax. The great strength of SPSS for serious data analysis is its ability to save the command syntax for reuse. Output The results of every analysis you perform, and every message that the program sends you, is sent to an output window. Results and messages accumulate (concatenate) down the window, forming a record of your actions. On the left, a window pane similar to a side-frame on a web page allows you to browse through the output items. Clicking on the name of an analysis in this pane allows you to delete it or print just that one item. Select the whole analysis Double-click to see more information Add a text box When psychologist perform analyses of complicated datasets,

6 Page 6 Numeric vs. other kinds of data Decimal places to display Long name of variable to appear in Output window Labels to display with values of variable in Output window (1=male, etc.) Number to be entered when data are missing Level of measurement: Nominal, ordinal, scale (interval, ratio) Creating Variables Use the variables pane to create variables and set their properties (see figure). The process is intuitively simple and is best learned by hands-on experimentation. Some issues to consider: 1. Variable names can only be 8 characters long, and you cannot control upper vs. lower case. Use variable names that make some sense, then use Variables Labels to add more information that will appear in the Output window. 2. Everything (almost) is numeric in SPSS, including variables that you might normally think of as text (alpha). For example, gender is coded with numbers rather than male and female or M and F. Use the Value Labels column to indicate what the numbers refer to, e.g., 1 indicates Male and 2 indicates Female. It is very important to set up these Value Labels so you don t commit an error interpreting your results later on. 3. Set missing values to impossible values (e.g., 9 for gender). You might need to use one missing value code for just plain missing, one for couldn t figure out the answer, and still another for subject said a nasty thing. 4. Decimal places are set for display on the data pane only and don t affect the underlying values. For integers, set this column to 0 decimal places. Computing New Variables The dataset component of SPSS does a lot of things besides hold on to your data. Most importantly, it allows you to transform your data in many way. For example, if you performed a survey study in which you measured attitudes toward tuition hikes on a five item scale, you might want to average the five items together to produce a single attitude score. In SPSS, this sort of math is done using a COM- PUTE utility in the dataset component of the program. This and other data transformation procedures in SPSS are similar in style and capability to Microsoft Excel.

7 Page 7 Like Excel, new variables are added as new columns in the dataset window. Use the Transform menu to create new variables based on existing ones. The two most commonly used items in this menu are Compute and Recode. Compute performs Excel-like calculations using basic math (Q1 + Q2) or built-in functions. Recode allows you to change the values of a variable. Using Compute Like Excel, the Compute function allows you to create new variables from existing ones either manually, by typing in a formula, or by using built-in functions. The figure shows the computation of the mean of questions 1-5 to produce the new variable attitude. When OK is clicked, the new variable is added to the dataset. Syntax: COMPUTE attitude = MEAN(q1,q2,q3,q4,q5). Type the name of the variable you want to create here Variables that go into functions are separated by commas Double-click on a variable to enter it in the formula Click on Paste to place the finished formula into the Syntax window for future use. Double-click on a function to place it in the formula field

8 Page 8 Using Recode Recode is often used to create a new variable that rearranged the values of an existing variable by assigned them new values. The most common use of the function is to reduce the number of response categories of a continuous variable. For example, if Question 1 in the attitudes toward tuition hike survey where Higher tuition is good because it allows the faculty to drive Beamers and the response scale were a 5-point Likert scale: Strongly agree Agree Not sure Disagree Strongly disagree Perhaps you would like to combine Strongly agree and Somewhat agree so you can make the statement, nn% of students agreed or strongly agreed that raising tuition is good idea so that their teachers can drive BMWs. A new variable in which Strongly agree and Somewhat agree were combined would allow you to directly obtain the value nn%. Another common use is to dichotomize a variable. Dichotomize means break the variable into two values, usually high and low by splitting it up, usually at the median (midpoint). For example, the average of the five questions on this survey, attitude, could be dichotomized at the median to form a new variable on which all the respondents would be either favorable or unfavorable. Why would you do this? Perhaps you also assessed parental income in your survey, and you expected a gender difference in attitudes. You might then want to know the mean income of student broken down by gender and by attitude toward the tuition hike. By dichotomizing attitude, you have a 2x2 design (gender x attitude), and it would be easy to compute the four means in this design. The Recode procedure is tricky and you must follow a series of steps very carefully to get it right. (See figures.) 1. Choose Transform -> Recode -> Into Different Variable 2. Double click on the existing variable that will form the basis for the new one 3. Type the new variable name in the appropriate field 4. Click on Old and New Values 5. Create the recode scheme a. Choose the range of values of the existing variable to be replaced by a value in the new variable (several possible ways to do this) b. Enter the value of the new variable to which this range should be recoded c. Click on Add. A new line appears in the Old -> New field d. Repeat (a) and (b) as needed e. Click on Continue to return to the original Recode screen 6. Click on Change in original Recode screen 7. Click on OK

9 Page 9 6. This appears after you complete (2) and (3) and click on Change 3. Type new variable name here. 2. Double-click on the existing variable from which you will create the new one 5b. Value of new variable goes here 5a. There are several ways to indicate the values of the old variable that you want to recode into the values of the new one. 5c. Clicking on Add adds a new line in this field After going through all of this, you may appreciate why some people prefer the old-fashioned, syntax-based way of doing things: RECODE attitude (1 thru 3.6=1) (3.7 thru 7=2) INTO att_d. VARIABLE LABELS att_d dichotomized attitude.

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