Analyzing Missing DataMissing data and data analysisTools for evaluating missing dataKey issues in missing data analysisProblem 1Identifying the number of cases in the data setRequest frequency distributionsCompleting the specification for frequenciesNumber of missing cases for each variableAnswering the problemProblem 2Create a variable that counts missing dataEnter specifications for new variableSlide 14Slide 15Complete specifications for new variableThe nmiss variable in the data editorA frequency distribution for nmissSlide 19The frequency distributionSlide 21Problem 3Compute valid/missing dichotomous variablesSlide 24Slide 25Slide 26Change the value for missing dataChange the value for valid dataComplete the value specificationsComplete the recode specificationsThe dichotomous variableFiltering cases with excessive missing variablesEnter specifications for selecting casesSlide 34Complete the specifications for selecting casesCases excluded from further analysesCorrelating the dichotomous variablesSpecifications for correlationsThe correlation matrixSlide 40Slide 41Slide 42Using scriptsUsing a script for missing dataOpen the data set in SPSSInvoke the scriptSelect the missing data scriptThe script dialogComplete the specificationsThe script finishesOutput from the scriptSW388R7Data Analysis & Computers IISlide 1Analyzing Missing DataIntroductionProblemsUsing ScriptsSW388R7Data Analysis & Computers IISlide 2Missing data and data analysisMissing data is a problem in multivariate data because a case will be excluded from the analysis if it is missing data for any variable included in the analysis.If our sample is large, we may be able to allow cases to be excluded.If our sample is small, we will try to use a substitution method so that we can retain enough cases to have sufficient power to detect effects.In either case, we need to make certain that we understand the potential impact that missing data may have on our analysis.SW388R7Data Analysis & Computers IISlide 3Tools for evaluating missing dataSPSS has a specific package for evaluating missing data, but it is included under the UT license.In place of this package, we will first examine missing data using SPSS statistics and procedures.After studying the standard SPSS procedures that we can use to examine missing data, we will use an SPSS script that will produce the output needed for missing data analysis without requiring us to issue all of the SPSS commands individually.SW388R7Data Analysis & Computers IISlide 4Key issues in missing data analysisWe will focus on three key issues for evaluating missing data:The number of cases missing per variableThe number of variables missing per caseThe pattern of correlations among variables created to represent missing and valid data.Further analysis may be required depending on the problems identified in these analyses.SW388R7Data Analysis & Computers IISlide 5Problem 1SW388R7Data Analysis & Computers IISlide 6Identifying the number of cases in the data setThis problem wants to know if a variable is missing data for half or more of the cases.Our first task is to identify the number of cases that meets that criterion.If we scroll to the bottom of the data set, we see than there are 270 cases in the data set. 270 ÷ 2 = 135. If any variable included in the analysis has 135 or more missing cases, the answer to the problem will be true.SW388R7Data Analysis & Computers IISlide 7Request frequency distributionsWe will use the output for frequency distributions to find the number of missing cases for each variable.Select the Frequencies… | Descriptive Statistics command from the Analyze menu.SW388R7Data Analysis & Computers IISlide 8Completing the specification for frequenciesSecond, click on the OK button to complete the request for statistical output.First, move the five variables included in the problem statement to the list box for variables.SW388R7Data Analysis & Computers IISlide 9Number of missing cases for each variableIn the table of statistics at the top of the Frequencies output, there is a table detailing the number of missing cases for each variable in the analysis.SW388R7Data Analysis & Computers IISlide 10Answering the problemWith 270 subjects in the data set, variables missing data for 135 or more cases would correctly be characterized as missing data for half or more of the cases in the data set.One variable was incorrectly characterized as missing half or more of the 270 cases: "self-employment" [wrkslf] was missing data for 20 of the 270 cases (7.4%). None of the variables in this analysis was missing cases for half or more of the 270 cases in the data set. False is the correct answer.SW388R7Data Analysis & Computers IISlide 11Problem 2SW388R7Data Analysis & Computers IISlide 12Create a variable that counts missing dataWe want to know how many of the five variables in the analysis had missing data for each case in the data set.We will create a variable containing this information that uses an SPSS function to count the number of variables with missing data. To compute a new variable, select the Compute… command from the Transform menu.SW388R7Data Analysis & Computers IISlide 13Enter specifications for new variableThird, click on the up arrow button to move the NMISS function into the Numeric Expression text box.First, type in the name for the new variable nmiss in the Target variable text box.Second, scroll down the list of functions and highlight the NMISS function.SW388R7Data Analysis & Computers IISlide 14Enter specifications for new variableThe NMISS function is moved into the Numeric Expression text box.Second, click on the right arrow button to move the variable name into the function arguments.To add the list of variables to count missing data for, we first highlight the first variable to include in the function, wrkstat.SW388R7Data Analysis & Computers IISlide 15Enter specifications for new variableFirst, before we add another variable to the function, we type a comma to separate the names of the variables.Third, click on the right arrow button to move the variable name into the function arguments.Second, to add the next variable we highlight the second variable to include in the function, hrs1.SW388R7Data Analysis & Computers IISlide 16Complete specifications for new variableContinue adding variables to function until all of the variables specified in the problem have been added.Be sure to type a comma between the