Missing Data Why consider missing data here? Because the presence of missing data complicates the computation and representation of data using numeric summaries. Reasons for missing data include 1) respondents failing to answer questions in a survey. 2) values incorrectly entered into the computer. 3) values that represent Know” or Care” or tell you” responses. “Don’t “Don’t “Won’t In SPSS parlance, a is an actual value that was put into the data not as a valid data value but missing value in order to represent the fact that a score is in fact, missing. In SPSS, an empty cell in the data editor means that the value that should have been there is missing. But in many situations, an actual value must be recorded when there is a missing response. Such values are the values” we’re dealing with here. “missing If you’re saving data as a text file for use in another program, it is often easiest to for every cell in the data editor to have something in it prior to saving. Missing values are not a terribly important issue when frequency distributions and graphs are used to summarize data because they’re just part of the summary. But when a statistic is to be computed, values that count” should not be included in the “don’t computation. The statistical package has to be told that such values are special and are not to be included in computation of statistics. Missing data are represented in SPSS in two ways. 1) Empty cells in the Data Editor window. These are called SYSTEM MISSING. 2) entered into the Data Editor window but given Value” status by you. Actual values “Missing In RCMDR , the NA symbol is used to represent missingness. In Excel, only empty cells are recognized as missing values To tell SPSS that one of the values of a variable is to be treated as a Value”, “Missing 1) Click on the View” tab at the lower left of the Data Editor window. “Variable 2) Click under in the same row as the variable for whom Missing values are to be declared. “Missing”
Missing Data Why consider missing data here? Because the presence of missing data complicates the computation and representation of data using numeric summaries. Reasons for missing data include 1) respondents failing to answer questions in a survey. 2) values incorrectly entered into the computer. 3) values that represent “Don’t Know” or “Don’t Care” or “Won’t tell you” responses. In SPSS parlance, a missing value is an actual value that was put into the data not as a valid data value but in order to represent the fact that a score is in fact, missing. In SPSS, an empty cell in the data editor means that the value that should have been there is missing. But in many situations, an actual value must be recorded when there is a missing response. Such values are the “missing values” we’re dealing with here. If you’re saving data as a text file for use in another program, it is often easiest to for every cell in the data editor to have something in it prior to saving. Missing values are not a terribly important issue when frequency distributions and graphs are used to summarize data because they’re just part of the summary. But when a statistic is to be computed, values that “don’t count” should not be included in the computation. The statistical package has to be told that such values are special and are not to be included in computation of statistics. Missing data are represented in SPSS in two ways. 1) Empty cells in the Data Editor window. These are called SYSTEM MISSING. 2) Actual values entered into the Data Editor window but given “Missing Value” status by you. In RCMDR , the NA symbol is used to represent missingness. In Excel, only empty cells are recognized as missing values To tell SPSS that one of the values of a variable is to be treated as a “Missing Value”, 1) Click on the “Variable View” tab at the lower left of the Data Editor window. 2) Click under “Missing” in the same row as the variable for whom Missing values are to be declared.