Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
Hostname: page-component-768ffcd9cc-8zwnf Total loading time: 0.272 Render date: 2022-12-04T05:23:59.465Z Has data issue: true Feature Flags: { "useRatesEcommerce": false } hasContentIssue true
  • English
Political Analysis Political Analysis

Article contents

Item Similarity in Scale Analysis

Published online by Cambridge University Press:  04 January 2017

Marco R. Steenbergen
Marco R. Steenbergen*
Affiliation:
University of North Carolina at Chapel Hill
Get access Rights & Permissions[Opens in a new window]

Abstract

A statistic—the similarity coefficient—is developed for assessing the property that a set of scale items measures one and only one construct. This statistic is rooted in an explicit measurement model and is flexible enough to be used in exploratory scale analyses, even in small samples. Methods for analyzing similarity coefficients are described and illustrated in analyses of Stimson's (1991) policy mood data and Markus' (1990) popular individualism items. The Appendix discusses the statistical properties of similarity coefficients.

Type
Research Article
Information
Political Analysis , Volume 8 , Issue 3 , 23 March 2000 , pp. 261 - 283
Copyright
Copyright © 2000 by the Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aleamoni, Lawrence M. 1973. “Effects of Size of Sample on Eigenvalues, Observed Communalities, and Factor Loadings.” Journal of Applied Psychology 58: 266–269.CrossRefGoogle Scholar
Anderson, James C., and Gerbing, David W. 1982. “Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement.” Journal of Marketing Research 19: 453–460.CrossRefGoogle Scholar
Anderson, James C., and Gerbing, David W. 1984. “The Effect of Sampling Error on Convergence, Improper Solutions and Goodness-of-Fit Indices for Maximum Likelihood Confirmatory Factor Analysis.” Psychometrika 49: 155–173.CrossRefGoogle Scholar
Anderson, James C., and Gerbing, David W. 1988. “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach.” Psychological Bulletin 103: 411–423.CrossRefGoogle Scholar
Andrews, Frank M. 1984. “Construct Validity and Error Components of Survey Measures: A Structural Modeling Approach.” Public Opinion Quarterly 48: 409–442.CrossRefGoogle Scholar
Asher, Herbert B. 1992. Presidential Elections and American Politics: Voters, Candidates, and Campaigns Since 1952, 5th ed. Pacific Grove, CA: Brooks Cole.Google Scholar
Benson, Jeri, and Fleishman, John A. 1994. “The Robustness of Maximum Likelihood and Distribution-Free Estimators to Non-Normality in Confirmatory Factor Analysis.” Quality & Quantity 28: 117–136.CrossRefGoogle Scholar
Best, Samuel J. 1999. “The Sampling Problem in Measuring Policy Mood: An Alternative Solution.” Journal of Politics 61: 721–740.CrossRefGoogle Scholar
Bollen, Kenneth A. 1989. Structural Equations with Latent Variables. New York: Wiley.Google Scholar
Boomsma, Anne. 1982. “The Robustness of LISREL against Small Sample Sizes in Factor Analysis Models.” In Systems Under Indirect Observation: Causality, Structure, Prediction, ed. Jöreskog, Karl G. and Wold, Herman. Amsterdam: North-Holland.Google Scholar
Boomsma, Anne. 1985. “Nonconvergence, Improper Solutions and Starting Values in LISREL Maximum Likelihood Estimation.” Psychometrika 50: 229–242.CrossRefGoogle Scholar
Burt, Ronald S. 1976. “Interpretational Confounding of Unobserved Variables in Structural Equation Models.” Sociological Methods & Research 5: 3–49.CrossRefGoogle Scholar
Cook, Thomas D., and Campbell, Donald T. 1979. Quasi-Experimental Design & Analysis: Issues for Field Settings. Boston: Houghton Mifflin.Google Scholar
Crocker, Linda, and Algina, James. 1986. Introduction to Classical and Modern Test Theory. Fort Worth, TX: Harcourt Brace Jovanovich.Google Scholar
DeVellis, Robert F. 1991. Scale Development: Theory and Applications. Newbury Park, CA: Sage.Google Scholar
Dolan, Conor V. 1994. “Factor Analysis of Variables with 2, 3, 5 and 7 Response Categories: A Comparison of Categorical Variables Estimators Using Simulated Data.” British Journal of Mathematical and Statistical Psychology 47: 309–326.CrossRefGoogle Scholar
Feldman, Stanley. 1999. “Economic Values and Inequality.” In Measurement of Political Attitudes, eds. Robinson, John P., Shaver, Phillip R., and Wrightsman, Lawrence S. San Diego, CA: Academic Press.Google Scholar
Fornell, Claes, and Rust, Roland T. 1989. “Incorporating Prior Theory in Covariance Structure Analysis: A Bayesian Approach.” Psychometrika 54: 249–259.CrossRefGoogle Scholar
Gerbing, David W., and Anderson, James C. 1988. “An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment.” Journal of Marketing Research 25: 186–192.Google Scholar
Gorsuch, Richard L. 1983. Factor Analysis. Hillsdale, NJ: Erlbaum.Google Scholar
Guilford, J. Paul. 1954. Psychometric Methods, 2nd ed. New York: McGraw-Hill.Google Scholar
Gulliksen, Harold. 1987. Theory of Mental Tests. Hillsdale, NJ: Erlbaum.Google Scholar
Hollis, Michael, and Muthén, Bengt O. 1987. “Structural Covariance Models with Categorical Data: An Illustration Involving the Measurement of Political Attitudes and Belief Systems.” Paper presented at the annual meeting of the American Political Science Association, Chicago.Google Scholar
Hunter, John E. 1973. “Methods for Reordering the Correlation Matrix to Facilitate Visual Inspection and Preliminary Cluster Analysis.” Journal of Educational Measurement 10: 51–61.Google Scholar
Hunter, John E., and Gerbing, David W. 1982. “Unidimensional Measurement, Second Order Factor Analysis, and Causal Models.” Research in Organizational Behavior 4: 267–320.Google Scholar
Hunter, John E., Gerbing, David W., and Boster, Franklin J. 1982. “Machiavellian Beliefs and Personality: Construct Invalidity of the Machiavellianism Dimension.” Journal of Personality and Social Psychology 43: 1293–1305.CrossRefGoogle Scholar
Jöreskog, Karl G. 1974. “Analyzing Psychological Data by Structural Analysis of Covariance Matrices.” In Contemporary Developments in Mathematical Psychology, Volume II. Measurement, Psychophysics, and Neural Information Processing, eds. Krantz, David H., Atkinson, Richard C., Duncan Luce, R., and Suppes, Patrick. San Francisco: W. H. Freeman.Google Scholar
Jöreskog, Karl G. 1990. “New Developments in LISREL: Analysis of Ordinal Variables Using Polychoric Correlations and Weighted Least Squares.” Quality & Quantity 24: 387–404.Google Scholar
Jöreskog, Karl G., and Sörbom, Dag. 1988. PRELIS: A Program for Multivariate Data Screening and Data Summarization. Scientific Software.Google Scholar
Lehmann, Erich L. 1983. Theory of Point Estimation. New York: Wiley.CrossRefGoogle Scholar
Lord, Frederic M., and Novick, Melvin R. 1968. Statistical Theories of Mental Test Scores. Reading, MA: Addison-Wesley.Google Scholar
Markus, Gregory. 1990. “Measuring Popular Individualism,” Mimeo.Google Scholar
McDonald, Roderick P. 1999. Test Theory: A Unified Treatment. Mahwah, NJ: Erlbaum.Google Scholar
Saris, Willem E., and Hartman, Harm. 1990. “Common Factors Can Always Be Found but Can They Also Be Rejected?” Quality & Quantity 24: 471–490.CrossRefGoogle Scholar
Saris, Willem E., and Satorra, Albert. 1993. “Power Evaluations in Structural Equation Models.” In Testing Structural Equation Models, eds. Bollen, Kenneth A., and Scott Long, J. Newbury Park, CA: Sage.Google Scholar
Saris, Willem E., and Van Den Putte, Bas. 1989. “True Score or Factor Models: A Secondary Analysis of the ALBUS-Test-Retest Data.” Sociological Methods and Research 17: 123–157.CrossRefGoogle Scholar
Scherpenzeel, Annette C., and Saris, Willem E. 1997. “The Validity and Reliability of Survey Questions: A Meta-Analysis of MTMM Studies.” Sociological Methods & Research 25: 341–383.Google Scholar
Spector, Paul E. 1992. Summated Rating Scale Construction: An Introduction, Sage University Paper Series on Quantitative Applications in the Social Sciences 07-082. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Steenbergen, Marco R. 1999. UniSim: A Program for Assessing Scale Unidimensionality. Chapel Hill: University of North Carolina.Google Scholar
Stimson, James A. 1991. Public Opinion in America: Moods, Cycles and Swings. Boulder, CO: Westview.Google Scholar
Suen, Hoi K. 1990. Principles of Test Theories. Hillsdale, NJ: Erlbaum.Google Scholar
Torgerson, Warren S. 1958. Theory and Methods of Scaling. Malabar, FL: Krieger.Google Scholar
Tryon, Robert C. 1939. Cluster Analysis: Correlation Profile and Orthometric Analysis for the Isolation of Unities in Mind and Personality. Ann Arbor, MI: Edwards Brother.Google Scholar
Tucker, Ledyard R. 1940. “The Role of Correlated Factors in Factor Analysis.” Psychometrika 5: 141–152.CrossRefGoogle Scholar
Wolins, Leroy. 1995. “A Monte Carlo Study of Constrained Factor Analysis Using Maximum Likelihood and Unweighted Least Squares.” Educational and Psychological Measurement 55: 545–557.Google Scholar
12
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Item Similarity in Scale Analysis
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Item Similarity in Scale Analysis
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Item Similarity in Scale Analysis
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *