It is not always easy to collect primary data for analysis. In this case, there are various ethical and logistical constraints that may hinder the effective gathering of primary data. There are incidences when ethical considerations poses difficulties in carrying out a research process. In addition, logistical factors such as contact with the participants may have an impact on the outcome of the study. In some incidences, it is usually difficult to conduct primary research due to the magnitude of the study. Therefore, secondary data analysis can be adopted to address these challenges. In secondary data analysis, the data used can be either qualitative or quantitative. Secondary data analysis involves the use of secondary data to come up with new conclusions. In this case, the previous research findings are re-analyzed to establish findings that complement or differ from the pervious conclusions (Wienclaw, 2009).
The adoption of secondary analysis has been associated with both negative and positive attributes. Secondary analysis can be economical on the part of the researcher given that little cost will be incurred while conducting the research. In addition, the analysis of secondary data is critical in situations where there are ethical and logistical constraints in conducting primary research. It can also be noted that the collection of data for secondary analysis is faster since the data is readily available. Furthermore, it is worthy mentioning that, in most cases, the collection of data for secondary analysis is non-reactive. This is critical since it does not influence the behavior of the participants (Wienclaw, 2009).
On the other hand, there are various shortcomings associated with secondary data analysis. Here, the researcher cannot have complete confidence in the quality of data used in the analysis since the data was not initially collected by the researcher. In this case, lack of control in the way data was primarily collected presents a major disadvantage to this analysis. In addition, the challenge of the researcher getting the necessary data sets for analysis is always present. The other disadvantage is that the researcher cannot ascertain whether the sample selection criterion initially used for the secondary data was genuine and scientific. In this case, it is not easy to extrapolate the findings due to the possibility of sampling and bias errors in the data used (Wienclaw, 2009).
In secondary data analysis, various issues have to be put into consideration. Therefore, when the analysis involves the use of data that was collected using survey, the questions used in the survey should be appropriate for the secondary analysis. If the questions were not clear, an appropriate source of data should be sought. In reporting the conclusions of the secondary analysis, the experimental conditions in which the primary data were collected has to be considered. When using meta-analysis, which is another form of secondary data analysis, the researcher has to examine various results from different researchers to establish the general pattern of the results (Wienclaw, 2009).
Secondary data analysis has been used on various occasions in social science studies. For instance, this analysis has been used in the study of depression and self-attribution in victims. Therefore, it can be asserted that the use of secondary data analysis is critical in social science studies. Secondary data analysis assists the researcher in providing answers to the questions on various social problems (Wienclaw, 2009). Despite the various shortcomings associated with secondary data analysis, it can be of great value in the advancement of social science.
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