4.7.2: Archival Data

Learning Objectives

1. Define archival data, and list some examples.
2. Describe the strength and weakness of using archival data to answer research questions.

Another unobtrusive approach to data collection involves analyzing archival data, which is information that has already been collected for some other purpose. They can include school and hospital records, newspaper and magazine articles, Internet content, television shows, and many other things. In the digital age, there is a vast amount of archival data.

An example is a study by Pelham et al. (2005) on “implicit egotism,” or the the tendency for people to prefer people, places, and things that are similar to themselves. In one study, Pelham et al. (2005) examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

The operational definitions of these records can be straightforward, or not. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider Peterson et al.'s (1988) study on the relationship between optimism and health using data that had been collected many years before for a study on adult development. In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson et al. (1988) reviewed the men’s questionnaire responses to obtain a measure of explanatory style (habitual ways of explaining bad events that happen to them). More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, Peterson et al. (1988) used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified. These events and explanations were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. Peterson et al. (1988) then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men.

This method is an example of content analysis, a family of systematic approaches to identifying patterns in complex data, such as written or verbal responses. Just as observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

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References

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14, 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55, 23–27.