Quant: Introduction to SPSS

SPSS is at the mercy of your input. What are variables and in what ways can you enter data into SPSS? Once you have numeric data into SPSS, what steps are required to define the meanings of the numbers for SPSS? (This requires explaining the components of Variable View in SPSS.) Why is it important to SPSS that you define these meanings?

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IBM SPSS aids in the entire quantitative analytical process, which aids in gaining insights on your data, to allow for better data-driven decisions (IBM, n.d.).  SPSS allows for the quick statistical practice and analysis of the data, without getting too focused and bogged by the statistical equations (Field, 2013). SPSS allows the end user to graphically tell a story about their data by discovering hidden relationships for pattern analysis through the table, graphs, charts, and maps that are allowing pivoting (IBM, n.d).  This tool also provides high accuracy, flexibility, and advanced statistical procedures which can be made available through the guided user interface or by allowing programmable options such internal command line syntax and external programming interfaces with R, Python, Java, .NET, etc. for automating procedures (IBM, n.d.).  However, Field (2013), warned that software like SPSS, which can automate statistical equations and procedures should not be used without fully understanding the statistical theory.

Variables and how to insert them into SPSS

A variable is a measurable and observed characteristic, attribute, or object which can differ between time, space, entity, person, organization, etc. (Creswell, 2014; Field, 2013). How these variables interact with other variables helps define what type of variable they are.  There are many types of variables such as dependent variables, independent variables, intervening/mediating variables, moderating variables, control variables, confounding variables, and extraneous variables (Creswell, 2014; Field, 2013). Dependent variables measure the outcome variation and are explained and influenced by independent variables (Schumacker, 2014). Thus, the dependent variables depend on the outcomes of the independent variables (Creswell, 2014).   Independent variables which are those that can be manipulated to help explain the dependent variable’s variation (Schumacker, 2014). Thus, the independent variables are the probable cause, influence, or affect the dependent variable (Creswell, 2014).  Intervening/mediating variables stand between the independent and dependent variable as a probable causal link between the two (Creswell, 2014).  Moderating variables are a type of independent variables that influence the direction or strength between the independent and dependent variables (Cresswell, 2014). Control variables are a type of independent variable that is restricted in some way or another to help find possible influences on the dependent variable.  Confounding variables are not measured or observed, but its influences cannot be detected.  Finally, there are extraneous variables are a type of independent variable, which are not controlled in quasi-experimental research and can influence the variation of the dependent variable (Schumacker, 2014)

In SPSS, one could enter in a variable in the data editor through the “Data View” window (see Figure 1) or through the “Variable View” window (see Figure 2).  In the “Data View” data can be entered in the cells below the variable name and new variables could be added by right clicking on the top most cell and selecting “Insert Variable,” though it should be avoided (Field, 2013; Miller, n.d.).  Whereas in the “Variable View” allows the end user to not only add new variables but add defining descriptions and characteristics of the variable (Field, 2013; Miller n.d.).  Every row in “Variable View” is variable and to add a new cell just select the cell below the last variable shown and start typing the variable’s name (Field, 2013).

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Figure 1: SPSS “Data View” on a sample dataset called bodyfat.sav.

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Figure 2: SPSS “Variable View” on a sample dataset called bodyfat.sav.

Data consists of numbers.  Numbers alone do not mean a thing.  The number 3 alone doesn’t mean a thing, however, three apples, three diamonds, 3oC means something. Once numerical data has been collected and entered into SPSS, it must be defined.  It is good practice to define the data in the “Variable View” immediately after collection and population into SPSS, because as time goes on memory can fade, and if the variable is not defined it can easily be forgotten what all those numbers mean.  Thus, defining the meaning to the data through the variable view allows the end user to remember what the data in each column of SPSS is, and tells SPSS how to treat, categorize, analyze, and display the variable. In order to do that the end user would need to enter in the: name of the variable, type of variable (numeric, string, currency, date, Boolean, etc.), width of the variable (number of digits and characters in the cell), decimals (how many decimals are displayed), label (a place holder to write the full name or description of the variable), values (assign numbers for representing groups), missing (if data is missing what value should it have), columns (width of the display column), align (cell data display alignment), measure (nominal, ordinal, or scale), and the variable’s role (input, target, both, split, partition, or none, which is used for regression analysis) (Field, 2013; Miller, n.d.).

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