Health Care Research and Secondary Data Analysis

Secondary Data Analysis in Health Care Research

Secondary Data Analysis,Economical Category,Analysis of Secondary Data,Question of Using Clinical Nursing,Sample Biases of Clinical Database,Caveats Needed for Data Analysis.

Secondary Data Analysis

    Secondary data analysis uses the analysis of data that the analyst was not responsible for collecting or data that was collected for a different problem from the one currently under analysis. 

    The data that are already collected and archived in some fashions are referred to as secondary information (Stewart, DW, &Kamins , 1993). Statistical meta analysis might be considered a special case of secondary analysis (see Meta-analysis).

Economical Category

    Secondary information is an inexpensive data source that facilitates the research process in several ways. It is also useful for generating hypotheses for further research. It is useful in comparing findings from different studies and examining trends. 

    Steward and Kamins (1993) point out that population data sets, such as Bureau of the Census data, may be used to compare sample to population characteristics in order to examine the representativeness of the study sample.

 Analysis of Secondary Data

    The analysis of secondary information is a useful strategy for learning the research process. The secondary data sets that have used optimum sampling techniques provide an optimum resource for students by virtue of the quality of sampling and the time and expense involved in data collection. 

    Given that students are expected to understand, explain, and defend the data set in terms of purpose, sample selection, methods, and instruments, only the real-life collection and recording of data remain unexperienced by the student. 

    A further virtue of using the analysis from secondary information while learning to do research is that it protects the pool of potential research participants and agencies for participation in studies conducted by qualified researchers.

    Every research study is conducted with a specific purpose in mind. Delimitations are specific to the original study and introduce specific types of sampling and other bias into the original study. 

    Operational definitions may not be replicable in a second study. For learning purposes, differences in the original study and data set can be handled through careful critique processes by students. However, the biases and differences that exist may be too extreme to permit a valid secondary analysis outside the practice situation.

    Archived data sets are rarely held in the form of raw data because the data is usually summarized. The summarization may or may not be appropriate for the research question under consideration for secondary analysis. To analyze such data further confounds results beyond acceptable limits.

Question of Using Clinical Nursing 

    The question of using clinical nursing data sets for secondary analysis comes with the advent of clinical nursing information systems. The use of clinical databases as research data sets must be carefully examined. One difficulty is that restricted data resources force clinicians to carefully choose which data to collect. These data are usually not identical with what the researcher needs.

Sample Biases of Clinical Database

    Beyond data restrictions another major difficulty is that the sample biases of clinical databases and research data sets for randomized control studies are different. This difference in bias of the data from clinical databases and randomized controlled trial research data sets can be exploited as a strategy for doing cross-design synthesis. 

    However, this special case aside, the issue is that of sample representativeness. The research sample is selected for a specific reason, with specific delimitations in mind, to be representative of the general population. 

    In contrast, the clinical population from which the clinical data set is drawn is representative only of that type of patient or client on whom data is being collected in that location and rarely, if ever, typical of the general population or even all persons with that clinical problem. 

    For example, patients with congestive heart failure in Alabama are not necessarily representative of patients with congestive heart failure in New England or California. The same is true of patients with congestive heart failure in a community hospital versus those in a teaching hospital in the same county.

Caveats Needed for Data Analysis

    These caveats need close evaluation of data sets to be used for secondary analysis. The information needed for such evaluation must be archived along with the data set. Such information includes study purpose; data collection details, such as who collected the data, when, and where; sampling criteria and delimitations; known biases; operational definitions; and methods of data collection.

    Traditionally, nursing has not archived research data sets of its own for use in teaching or secondary analysis. Nursing students and nurse researchers do use large government databases, but none are collected specifically by nurse researchers to answer nursing research questions. 

    This is a problem to the extent that learning takes place best when examples and experiences relate closely to daily (nursing) experience. Certainly, problems peculiar to but not exclusive to nursing research are more easily taught with examples from real life. 

    This is a problem also to the extent that nursing research data sets can, in fact, generate new knowledge, whether by reanalysis or by stimulation of further investigation and hypothesis generation.

    Sigma Theta Tau International has begun a program to archive selected research data sets of nurse researchers. The project is still in its infancy, with acquisition and dissemination policy still under study (see Data Stewardship). Descriptions of the research study will be required to fulfill criteria for data set evaluation mentioned above.