Sample Size Effects on Chi Square and Other Statistics Used in Evaluating Causal Models
Journal of Marketing ResearchPublished 1 November 1982
William O. Bearden, Subhash Sharma, Jesse E. Teel
Citations510
SJR quartileQ1
SJR score6.96
SNIP2.42
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
William O. Bearden, Subhash Sharma, Jesse E. Teel, Sample Size Effects on Chi Square and Other Statistics Used in Evaluating Causal Models, Journal of Marketing Research, Vol. 19, No. 4, Special Issue on Causal Modeling (Nov., 1982), pp. 425-430
Keywords
Computer ScienceDecision SciencesAgricultural and Biological Sciences
Journal of Marketing ResearchEvaluating Structural Equation Models with Unobservable Variables and Measurement Error
60,778 Citations1981Claes Fornell, David F. Larcker
Psychological BulletinSignificance tests and goodness of fit in the analysis of covariance structures.
18,289 Citations1980Peter M. Bentler, Douglas G. Bonett
Educational and Psychological MeasurementIntraclass Reliability Estimates: Testing Structural Assumptions
1,648 Citations1974Charles E. Werts, Robert L. Linn +1 more
PsychometrikaStructural Analysis of Covariance and Correlation Matrices
1,012 Citations1978Karl G. Jöreskog
Journal of Consumer ResearchTelevision and Interpersonal Influences on Adolescent Consumer Learning
432 Citations1979Gilbert A. Churchill, George P. Moschis
Journal of MarketingPerformance and Satisfaction in an Industrial Sales Force: An Examination of their Antecedents and Simultaneity
414 Citations1980Richard P. Bagozzi
Journal of Marketing ResearchBeyond Attitude Structure: Toward the Informational Determinants of Attitude
345 Citations1978Morris B. Holbrook
Deep Blue (University of Michigan)A Comparative analysis of two structural equation models : LISREL and PLS applied to market data
209 Citations1981Claes Fornell
Journal of the American Statistical AssociationInterpreting the Likelihood Ratio Statistic in Factor Models When Sample Size is Small
198 Citations1980John Geweke, Kenneth J. Singleton
Journal of Marketing ResearchStructural Equation Models in Experimental Research
167 Citations1977Richard P. Bagozzi
Journal of Consumer ResearchAn Experimental Investigation of Causal Relations among Cognitions, Affect, and Behavioral Intention
138 Citations1977Richard J. Lutz
Journal of Marketing ResearchUnobservable Variables in Structural Equation Models with an Application in Industrial Selling
110 Citations1979David A. Aaker, Richard P. Bagozzi
The use and evaluation of structural equation models which include unobservable variables, which permit theoretical constructs to be represented by multiple indicators, are illustrated in an industrial salesforce study.
Journal of Marketing ResearchEvaluating the Relationships among Attitude toward Business, Product Satisfaction, Experience, and Search Effort
101 Citations1979Ronald D. Anderson, Jack L. Engledow +1 more
BiometrikaMaximum likelihood in small samples: Estimation in the presence of nuisance parameters
66 Citations1980D. A. Sprott
Psychological BulletinMaximum-likelihood estimation in common factor analysis: A cautionary note.
48 Citations1980Douglas N. Jackson, David W. Chan
Journal of Marketing ResearchOn Using Response Latency to Measure Preference
45 Citations1980David A. Aaker, Richard P. Bagozzi +2 more
The role of response latency in the measurement of preferences is investigated by means of a causal modeling method and the convergent validity and predictive validity ofresponse latency measures are examined and compared with those of constant sum and paired comparison procedures.
Journal of Consumer ResearchDeterminants of Physician and Pharmacist Support of Generic Drugs
33 Citations1980William O. Bearden, J. Barry Mason
Results suggest that confidence in regulation, potential savings, and impact on drug research represent plausible determinants of physician and pharmacist support of generic drugs.