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Negative Case
In qualitative analysis, data are usually grouped to form patterns (identified as constructs) with the expectation that there will be some degree of VARIATION within those patterns. However, either through the process of purposeful searching or by happenstance, it is possible to come across a case that does not fit within the pattern, however broadly the construct is defined. This case is usually referred to as a “negative case” because it seems contrary to the general pattern. For example, suppose one was studying care-givers of people with Alzheimer's disease, and one of the constructs identified from the study is “emotional distress” (Khurana, 1995). All of the caregivers who are interviewed express some degree of emotional distress. However, as data gathering continues, the researcher happens upon a participant who expresses little, if any, distress. Coming across this one participant does not necessarily negate the general pattern, but the case does provide additional insight into the construct of “emotional distress.”
Negative cases can serve as comparative cases enabling analysts to explain more clearly what a CONCEPT is and what it is not in terms of its properties (i.e., identify the boundaries of a concept). Negative cases can help delineate important conditions pertaining to a concept, for instance, enabling the researcher to explain why some people who are care-givers are more or less likely to develop emotional distress. They also enable analysts to extend or modify the original construct, possibly adding to its explanatory power. Negative cases can also lend a degree of VALIDITY to a study by demonstrating that the analyst is willing to consider alternatives and has indeed searched for other possible explanations (Miles & Huberman, 1994). Because social science research deals with human beings, one can expect that there will always be exceptions to every identified pattern (Patton, 1990).
Does one always discover negative cases when conducting research? The answer is that whether or not one discovers the negative case depends on how hard and how long one looks. At the same time, there are no set rules for how long one should continue the search for the “negative case” (Glaser & Strauss, 1967). There are always constraints on a research project in terms of time, money, and access to a population. However, a researcher should make a concerted effort to find the exceptions to the pattern and include reference to the search process when writing up the research; then, when and if such a case is discovered, he or she should bring the additional insights and alternative explanations into the discussion of findings. This will not only add to the credibility to the research but also enhance understanding of the phenomenon under investigation, benefiting participants and users of the research.
References
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- Analysis of Variance
- Association and Correlation
- Association
- Association Model
- Asymmetric Measures
- Biserial Correlation
- Canonical Correlation Analysis
- Correlation
- Correspondence Analysis
- Intraclass Correlation
- Multiple Correlation
- Part Correlation
- Partial Correlation
- Pearson's Correlation Coefficient
- Semipartial Correlation
- Simple Correlation (Regression)
- Spearman Correlation Coefficient
- Strength of Association
- Symmetric Measures
- Basic Qualitative Research
- Basic Statistics
- F Ratio
- N(n)
- t-Test
- X¯
- Y Variable
- z-Test
- Alternative Hypothesis
- Average
- Bar Graph
- Bell-Shaped Curve
- Bimodal
- Case
- Causal Modeling
- Cell
- Covariance
- Cumulative Frequency Polygon
- Data
- Dependent Variable
- Dispersion
- Exploratory Data Analysis
- Frequency Distribution
- Histogram
- Hypothesis
- Independent Variable
- Measures of Central Tendency
- Median
- Null Hypothesis
- Pie Chart
- Regression
- Standard Deviation
- Statistic
- Causal Modeling
- DISCOURSE/CONVERSATION ANALYSIS
- Econometrics
- Epistemology
- Ethnography
- Evaluation
- Event History Analysis
- Experimental Design
- Factor Analysis and Related Techniques
- Feminist Methodology
- Generalized Linear Models
- HISTORICAL/COMPARATIVE
- Interviewing in Qualitative Research
- Latent Variable Model
- LIFE HISTORY/BIOGRAPHY
- LOG-LINEAR MODELS (CATEGORICAL DEPENDENT VARIABLES)
- Longitudinal Analysis
- Mathematics and Formal Models
- Measurement Level
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- Multiple Regression
- Qualitative Data Analysis
- Sampling in Qualitative Research
- Sampling in Surveys
- Scaling
- Significance Testing
- Simple Regression
- Survey Design
- Time Series
- ARIMA
- Box-Jenkins Modeling
- Cointegration
- Detrending
- Durbin-Watson Statistic
- Error Correction Models
- Forecasting
- Granger Causality
- Interrupted Time-Series Design
- Intervention Analysis
- Lag Structure
- Moving Average
- Periodicity
- Serial Correlation
- Spectral Analysis
- Time-Series Cross-Section (TSCS) Models
- Time-Series Data (Analysis/Design)
- Trend Analysis
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