Entry
Reader's guide
Entries A-Z
Method Variance
Method variance refers to the effects the method of measurement has on the things being measured. According to classical test theory, the observed variance among participants in a study on a variable can be attributable to the underlying TRUE SCORE or construct of interest plus RANDOM ERROR. Method variance is an additional source of variance attributable to the method of assessment. Campbell and Fiske (1959) gave examples of apparatus effects with Skinner boxes used to condition rats and format effects with psychological scales.
It has been widely assumed in the absence of solid evidence that when the same method is used to assess different variables, method effects (also called monomethod bias) will produce a certain level of SPURIOUS covariation among those variables. Many researchers, for example, are suspicious of survey studies in which all variables are contained in the same questionnaire, assuming that by being in the same questionnaire, there is a shared method that produces spurious correlation among items. Research on method variance has cast doubt on such assumptions. For example, Spector (1987) was unable to find evidence to support that method effects are widespread in organizational research. One of his arguments was that if method variance produced spurious correlations, why were so many variables in questionnaire studies unrelated? Williams and Brown (1994) conducted analyses showing that in most cases, the existence of variance due to method would serve to attenuate rather than inflate relations among variables.
Spector and Brannick (1995) noted that the traditional view that a method (e.g., questionnaire) would produce method effects across all variables was an over simplification. They discussed how constructs and methods interacted, so that certain methods used to assess certain variables might produce common variance. Furthermore, they suggested that it was not methods themselves but rather features of methods that were important. For example, SOCIAL DESIRABILITY is a well-known potential BIAS of self-reports about issues that are personally sensitive or threatening (e.g., psychological adjustment), but it does not bias reports of nonsensitive issues. If the method is a self-report and the construct is sensitive, a certain amount of variance will be attributable to social desirability. With such constructs, it may be necessary to find a procedure that can control these effects.
References
...
- 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
- Measurement Testing and Classification
- Multilevel Analysis
- 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
Get a 30 day FREE TRIAL
-
Watch videos from a variety of sources bringing classroom topics to life
-
Read modern, diverse business cases
-
Explore hundreds of books and reference titles
Sage Recommends
We found other relevant content for you on other Sage platforms.
Have you created a personal profile? Login or create a profile so that you can save clips, playlists and searches