According to Tabachnick and Fidell (2001), “Missing data is one of the most pervasive problems in data analysis “ (p. 58). Missing data can have serious effects on the reliability, validity and generalizability of the data (Tabachnick & Fidell, 2001). Missing data can be indicative of lack of knowledge, fatigue or sensitivity, or interpretation by the respondent of the questionnaire relevance. When the number of missing cases is small (< 5%) it is common to exclude the cases from the analysis (Tabachnick & Fidell, 2001). In the present analysis, every variable is missing at least 16% of the responses. The univariate statistics are shown below in Table 2.
Before exploratory factor analysis it must be determined if missing data is systematic (represents bias) or is ignorable. Missing data also has other important ramifications, especially in factor analysis. Factor analysis using listwise deletion should not be conducted unless the missing data is at least missing completely at random (MCAR).
