by DAN CALLOWAY
Published 24 July 2010
Member of the ACM
WEAVERVILLE, NC - For this article, I chose the belief about what constitutes good data when approaching the process of conducting research. Selecting good data when conducting research is always something that the researcher should strive for when (1) Planning the research design; (2) Collecting and recording the data; (3) Cleaning and sorting the data; (4) Analyzing, interpreting, and drawing inferences from the data; and (5) Writing up the research report. However, prior experiences can influence the research design we ultimately choose and can bias our interpretations regardless of which form of research we choose, whether it is quantitative, qualitative, or mixed-method.
What constitutes good vs. bad data is really a matter to be decided by the researcher and will differ from researcher to researcher based on one’s biases, theoretical predispositions, and preferences. It is important that the researcher exercises reflexivity or the process of self-reflection to take a look at these elements when planning, collecting, sorting, analyzing & interpreting, drawing inferences from the data, and writing up the final report (Kleinsasser, 2000).
When planning the type of research design, all researchers hope they have good data and that they have a valid reason for conducting the research. Kleinsasser (2000) stresses the importance that researchers determine their place in the setting, context, and social phenomenon that s/he hopes to better understand and that they choose the proper means for a critical examination of the entire research process.
Planning and Research Design
Vogt (2007) indicates that research design is a plan for collecting evidence that can be used to answer a research question, and suggests that there are at least seven types of research design: (1) document analysis, (2) secondary analysis of data, (3) naturalistic observation, (4) surveys, (5) interviews, (6) experiments and quasi-experiments, and (7) participant observation. Throughout the planning and design phase of conducting research, the researcher should remember that validity criteria are met, in part, by good data.
Collecting and Recording the Data
Vogt (2007) indicates that collecting and recording data involves the handling of the data and represents how the data will be counted, sorted, and how the variables involved will be identified and what level of measurement will be involved. According to Vogt, there are two types of data variables: quantitative and qualitative; and there are four levels of measurement: nominal, ordinal, interval, and ratio. Our beliefs in what is good data can have an impact on how we classify it and how we choose to measure the variables we identify whether they be qualitative or quantitative.
Cleaning and Sorting the Data
According to Vogt (2007) cleaning and sorting the data involves attempting to reduce to the greatest extent possible the measurement error involved but there is no such thing as a perfect measurement and, at best, good measurement techniques can only reduce error; that is, they are set up in a manner that one can estimate the degree of error there is likely to be.
Analyzing, Interpreting, and Drawing Inferences
Vogt (2007) states that statistical analysis is the process of taking a look at the data in order to make sense of it and to figure out what it tells us as researchers. Our biases and preconceived notions of what the data should tell us rather than what the data actually reveals to us can skew the results.
Writing the Report
The researcher writes up the report based on the data s/he has collected, the analysis of the evidence that the data has revealed, and the support the evidence produces for the research question the researcher is investigating. Our biases and prejudices in our belief system regarding the data can have a direct influence on the way we portray and report the findings to others in our written reports on our analysis and the inferences we make regarding the analysis of the data.
References:
Kleinsasser, A. M. (2000). Researchers, reflexivity, and good data: Writing to unlearn. Theory into Practice, 39(3), 155-162.
Vogt, W. P. (2007). Quantitative Research Methods for Professionals (Custom., p. 334). Boston: Pearson Education, Inc.
Dan Calloway
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