Table of Contents
ASSIGNMENT: nonexperimenmtal studies
ASSIGNMENT: nonexperimenmtal studies
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ASSIGNMENT: ANALYSIS AND INTERPRETATION IN NONEXPERIMENTAL STUDIES
Data analyses in nonexperimental studies depend on both the goal for the study and the nature of the variables in the data set. Almost any analysis may be possible and a useful presentation is not reasonable here. There are ample books and sources for details about statistical methods and their use. A few examples are given at the end of the chapter; also see the discussion on understanding quantitative data in Chapter Six.
You need to be aware of the basic distinction between descriptive and inferential statistics. Descriptive statistics involve summarizing and describing quantitative information in meaningful ways. For example, a mean, or arithmetic average, is a statistic used to describe a central value for a set of numbers. Inferential statistics are used to make conclusions beyond the data collected and to test hypotheses. Statistical tests are used to make conclusions about populations based on results from random samples or to determine the probability that results are not due to random chance.
ASSIGNMENT: nonexperimenmtal studies
Interpretation of results in nonexperimental studies should be consistent with the nature of the work, which is based on nonmanipulated variables. Therefore, conclusions about cause and effect are not appropriate in any nonexperimental study. As you read empirical articles, you should be attuned to how conclusions are discussed and be wary of causal language. Robinson, Levin, Thomas, Pituch, and Vaughn (2007) reviewed 274 empirical articles in five teaching-and-learning research journals in 1994 and 2004. They recorded causal and noncausal language use in abstracts and discussion sections. Their two main conclusions were: (1) experimental articles in teaching-and-learning declined in the ten-year span, and (2) on average, the use of causal conclusions made in nonexperimental and qualitative studies increased. They conclude by saying that “as journal readers, we have an obligation to search an article for information about how the data were collected so we are not unduly influenced by unwarranted conclusions” (Robinson et al., 2007, p. 412). Ideally, after studying this chapter you will be able to search through articles for information about how the study was conducted and use that to consider conclusions.
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Nonexperimental Quantitative Research
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SUMMARY
The goal for this chapter was to present adequate information about nonexperimental designs so that a practitioner could read the literature and have a basic understanding of methods used. Nonexperimental research is described in many ways and covers any quantitative study that does not have manipulated variables or random assignment. A topic of research interest can be modified to serve alternative purposes, and data can be collected over different time frames. The two-dimensional classification system presented here should help you categorize articles. Reading any of the articles listed in Table 4.2 that are of interest to you could be useful in understanding why it was classified according to the two dimensions given. A good place to start, with a relatively straightforward example, would be the Cassady (2001) article, which is an example of Type 3, a descriptive prospective study. A good exercise would be to find other nonexperimental studies and classify them according to the two dimensions of purpose and time of data collection.
A key to understanding published research is to identify the goal of the research, evaluate what was done in relation to that goal, and consider aspects and variables that may have been overlooked. Most important, consider the language used in published works and be skeptical if overzealous researchers present their nonexperimental results in causal terms. Regardless of what type of research is presented, be a wary consumer.
KEY TERMS
attribute variables categorical variables confounding or lurking variables correlation coefficient criterion cross-sectional research dependent variable descriptive nonexperimental research descriptive statistics experimental research explanatory nonexperimental research independent variable index
inferential statistics median nonexperimental research predictive nonexperimental research predictors prospective or longitudinal research quantitative variables quasi-experiments random assignment random sample regression line
reliability retrospective research scale scatter plot true-experiments validity variable
Analysis and Interpretation in Nonexperimental Studies 77
FURTHER READINGS AND RESOURCES
Suggested Readings
Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press.
This basic text, discussing an analysis technique often used in nonexperimental studies, is written in an understandable manner, using examples from social science research literature to develop the concepts.
Johnson, R. B., & Christensen, L. See lecture in Chapter Eleven: Nonexperimental quantitative research, based on Educational Research: Quantitative, Qualitative, and Mixed Applications. Retrieved March 13, 2008, from www.southalabama.edu/coe/bset/johnson/2lectures.htm.
Discusses steps in nonexperimental research, ways to control extraneous variables in nonexperimental research, and Johnson’s classification scheme for nonexperimental research, and provides a graphic description of controlling for a third variable.
Locke, L. F., Silverman, S. J., & Waneen, W. S. (2004). Reading and understanding research (2nd ed). Thousand Oaks: Sage.
Although this book deals with research in general, it is an easily understandable resource with good examples to help you read and understand published research articles. Aimed at consumers of research, the approach is nontechnical and user-friendly.
Lowry, R. (1999–2008). Concepts and applications of inferential statistics. Retrieved October 10, 2007, from http://faculty.vassar.edu/lowry/webtext.html.
Chapter Three of this free, full-length statistics textbook provides an introduction to linear correlation and regression using examples and diagrams. This is useful for understanding the basic analyses used with nonexperimental data.
Meltzoff, J. (1997). Critical thinking about research: Psychology and related fields. Washington, DC: American Psychological Association.
This text should help develop critical thinking skills via research by critiquing exercises of different types of research studies. It combines fundamental content with practice articles.
Trochim, W. M. The research methods knowledge base (2nd ed.). Retrieved October 20, 2006, from www.socialresearchmethods.net/kb.
Of particular use is the Language of Research part of the Foundation section, where types of relationships are clearly described, using simple examples and graphs.
