Scholars have highlighted the importance of examining and preparing the data in survey research. They note that several types of common problems or issues exist, such as missing data, item properties, negatively worded items, reliability and internal consistency of scales, inconsistent responses to similar items, restricted response patterns and so forth (Church, Waclawski, & Kraut, 1998; Nunnally &
Bernstein, 1994). This section describes the steps adopted in the current study to resolve some such problems and issues faced in the current study.
The responses to the questions in the background section of the questionnaire were examined manually for anomalies and any inconsistencies or anomalies found were resolved, either by deleting the case or recoding them as missing data. The data preparation steps explained below were applied only for the main section of the questionnaire as they were not applicable for the items in the background section.
4.7.1 Negatively worded items
Church, Waclawski, & Kraut (1998) note that often the best way to check data is by comparing a mean score for the negatively worded item with a positively worded one with a similar content or theme. However, according to them, if a negatively worded item is to be compared with a positively worded item, for analysis purposes, responses should be recoded so that they point to the same direction. It is also important to reverse code the items if the items are going to be added to arrive at the total scores on sub scales and scales. The structure of the current questionnaire had sub scales that could be added to form a total scale and, therefore, it was essential to
reverse code either the positive or the negative items to ensure that all pointed in the same direction or had the same ‗polarity‘.
In the current study there was a mix of positively and negatively worded items. It was, therefore, decided to ‗reverse code‘ all the negatively worded items so that the average scores on items could be compared and even aggregated. A high score in the current study, therefore, refers to the ‗facilitating‘ direction rather than the
‗hindering‘ direction – irrespective of the item. On the contrary, a low score refers to a ‗hindering‘ direction. The polarity of the items are given in Appendix 3.
4.7.2 Missing data
Unfortunately in most surveys some respondents do not respond to the survey fully.
This problem has been more seriously felt in online surveys than paper and pencil surveys which are administered under supervision. Church, Waclawski & Kraut (1998) recommend that it is important to delete completely blank returns and to ensure that the missing responses do not distort the results. They recommend that partially completed responses should be retained and used for analysis purposes, unless the number of completed items is less than 10 percent of the total set of questions provided. They note that less than 10 percent complete suggests a significant problem with that respondent and raises questions about the validity of those data.
The current study also had relatively large numbers of missing data, mostly toward the end part of the questionnaire. This is probably due to the fact that some of the respondents abandoned the survey after some time due to unknown factors which
might include interruptions, internet speed problems, lack of interest and so forth.
As suggested by experts, the completely blank responses which were less than 10 per cent complete were fully deleted from the database. The remaining data were retained. In the statistical analysis, missing data were deleted on an individual item wise, pair basis as far as possible. Wherever the pair wise deletion was not possible, the missing data were deleted list wise.
4.7.3 Item analysis and reliability
It is very important to analyse the items based on the various properties, such as response patterns and variance, item-scale total correlations, item-subscale total correlations, reliability analysis, etc. In this study the following methods were used to analyse the items and to ensure acceptable levels of reliability for the scales (Nunnally & Bernstein, 1994):
Response patterns of the items as indicated by frequency analysis, mean and standard deviation.
Correlation between items and subscale totals.
Correlation between items and scale total.
Cronbach coefficient alpha analysis.
The Cronbach coefficient alpha is employed as it is the most commonly used index of scale reliability. In general, scales that receive alpha scores over 0.70 are considered to be reliable, however, figures as low as 0.50 have been considered acceptable (Nunnally & Bernstein, 1994; Kaplan & Saccuzzo, 2001).
Based on the above analyses, many items were deleted from the questionnaire and database in order to achieve higher levels of reliability coefficients for the sub scales
and the total scale. These items are presented in Appendix 3 and marked as deleted.
After deleting the items, a reliability analysis was conducted again to compute the Cronbach coefficient alpha. The results before and after the item deletions are presented in Table 3, below.
Scales Before deleting the items After deleting the items Cronbach Alpha (# items) Cronbach Alpha (# items)
National level factors 0.74 (27) 0.80 (21)
Organisational – policies and
practices related factors 0.60 (27) 0.72 (19)
Organisational – HR related factors
0.66 (25) 0.74 (18)
Individual level – expatriate and
experienced staff related factors 0.70 (13) 0.72 (9)
Individual level – UAE national
related factors 0.80(15) 0.86(13)
Total scale 0.79 (107) 0.91(80)
Table 3: Reliability test: initial and final results
For the sample collected, Cronbach‘s alpha values of the six main factors greatly exceed the general requirement (> 0.70) and, therefore, the internal consistencies of each group of indicators were deemed to be high (Table 3). It may be also noted that the item analysis and deletion procedure helped to improve the internal consistency of the sub scales, as indicated by the gain in Cronbach Alpha coefficients after the deletion of the items.