Analyzing missing data презентация

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Презентации» Информатика» Analyzing missing data
Analyzing Missing Data
 Introduction
 Problems
 Using ScriptsMissing data and data analysis
 Missing data is a problem inTools for evaluating missing data
 SPSS has a specific package forKey issues in missing data analysis
 We will focus on threeProblem 1
 1. Based on a missing data analysis for theIdentifying the number of cases in the data setRequest frequency distributionsCompleting the specification for frequenciesNumber of missing cases for each variableProblem 2
 2. Based on a missing data analysis for theCreate a variable that counts missing dataEnter specifications for new variableEnter specifications for new variableEnter specifications for new variableComplete specifications for new variableThe nmiss variable in the data editorA frequency distribution for nmissCompleting the specification for frequenciesThe frequency distributionAnswering the problemProblem 3
 3. Based on a missing data analysis for theCompute valid/missing dichotomous variablesEnter specifications for new variableEnter specifications for new variableEnter specifications for new variableChange 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 casesEnter specifications for selecting casesComplete the specifications for selecting casesCases excluded from further analysesCorrelating the dichotomous variablesSpecifications for correlationsThe correlation matrixThe correlation matrixThe correlation matrixUsing scripts
 The process of evaluating missing data requires numerous SPSSUsing a script for missing data
 The script “MissingDataCheck.sbs” will produceOpen the data set in SPSSInvoke the scriptSelect the missing data scriptThe script dialogComplete the specificationsThe script finishesOutput from the script



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Analyzing Missing Data Introduction Problems Using Scripts


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Missing 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.

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Tools 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.

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Key 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.

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Problem 1 1. Based on a missing data analysis for the variables "employment status," "number of hours worked in the past week," "self employment," "governmental employment," and "occupational prestige score" in the dataset GSS2000.sav, is the following statement true, false, or an incorrect application of a statistic? The variables "number of hours worked in the past week" and "employment status" are missing data for more than half of the cases in the data set and should be examined carefully before deciding how to handle missing data. 1. True 2. True with caution 3. False 4. Incorrect application of a statistic

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Identifying the number of cases in the data set

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Request frequency distributions

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Completing the specification for frequencies

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Number of missing cases for each variable

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Problem 2 2. Based on a missing data analysis for the variables "employment status," "number of hours worked in the past week," "self employment," "governmental employment," and "occupational prestige score" in the dataset GSS2000.sav, is the following statement true, false, or an incorrect application of a statistic? 14 cases are missing data for more than half of the variables in the analysis and should be examined carefully before deciding how to handle missing data. 1. True 2. True with caution 3. False 4. Incorrect application of a statistic

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Create a variable that counts missing data

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Enter specifications for new variable

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Enter specifications for new variable

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Enter specifications for new variable

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Complete specifications for new variable

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The nmiss variable in the data editor

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A frequency distribution for nmiss

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Completing the specification for frequencies

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The frequency distribution

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Answering the problem

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Problem 3 3. Based on a missing data analysis for the variables "employment status," "number of hours worked in the past week," "self employment," "governmental employment," and "occupational prestige score" in the dataset GSS2000.sav, is the following statement true, false, or an incorrect application of a statistic? Use 0.01 as the level of significance. After excluding cases with missing data for more than half of the variables from the analysis if necessary, the presence of statistically significant correlations in the matrix of dichotomous missing/valid variables suggests that the missing data pattern may not be random. 1. True 2. True with caution 3. False 4. Incorrect application of a statistic

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Compute valid/missing dichotomous variables

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Enter specifications for new variable

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Enter specifications for new variable

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Enter specifications for new variable

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Change the value for missing data

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Change the value for valid data

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Complete the value specifications

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Complete the recode specifications

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The dichotomous variable

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Filtering cases with excessive missing variables

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Enter specifications for selecting cases

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Enter specifications for selecting cases

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Complete the specifications for selecting cases

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Cases excluded from further analyses

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Correlating the dichotomous variables

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Specifications for correlations

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The correlation matrix

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The correlation matrix

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The correlation matrix

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Using scripts The process of evaluating missing data requires numerous SPSS procedures and outputs that are time consuming to produce. These procedures can be automated by creating an SPSS script. A script is a program that executes a sequence of SPSS commands. Thought writing scripts is not part of this course, we can take advantage of scripts that I use to reduce the burdensome tasks of evaluating missing data.

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Using a script for missing data The script “MissingDataCheck.sbs” will produce all of the output we have used for evaluating missing data, as well as other outputs described in the textbook. Navigate to the link “SPSS Scripts and Syntax” on the course web page. Download the script file “MissingDataCheck.exe” to your computer and install it, following the directions on the web page.

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Open the data set in SPSS

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Invoke the script

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Select the missing data script

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The script dialog

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Complete the specifications

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The script finishes

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Output from the script


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