We want to work on a project relating to organisational psychology where we wish to collect data for different constructs at different time waves (unlike longitudinal study). Can somebody tell why it's important and/or suggest some relevant literature?
If you are collecting self-report data, I believe one of the main reasons for collecting data in waves or at different time periods for different constructs is to try and alleviate common method variance (bias). There are a lot of articles on common method variance, but one of the main complaints with getting self report data is that if the survey is long respondents will get fatigued and start putting answers without reading the items. This lowers the reliability and validity of your data. By spreading it out you might have a better chance of not tiring our the respondent at any one sitting and lessen the effects of fatigue.
Of course, spreading it out across too many administrations is difficult on the researcher and also increases the possibility of burnout from the respondent if they have to do several data collection sessions. I think if you have a large number of scales for various constructs, you might consider doing it across two administrations. Another option is to limit the number of constructs you use in any one study so the measure(s) you use are not burdensome.
Common method variance is a "common" complaint about self-report research, but here are a couple of references to read up on it:
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544.
Brannick, M. T., Chan, D., Conway, J. M., Lance, C. E., & Spector, P. E. (2010). What is method variance and how can we cope with it? A panel discussion. Organizational Research Methods, 13(3), 407-420. doi:10.1177/1094428109360993
Hope this helps.
cross-sectional studies are more likely to have higher levels of common method variance resulting in inflated correlations. And constructs collected in different time can reduce common method variance.
Cross verification of data standardizing and reduce the data variance at the same time data accuracy can be improved
Thank you so much dear C. Brian Flynn for your guidance and telling the right articles. Thanks Cuiping Ma, Vivek Anand, and Nur Suhaili Ramli for your valuable answers and comments.
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