Sarah S Tonkin1, Trevor F Williams1, Leonard J Simms1, Stephen T Tiffany1, Martin C Mahoney2,3, Robert A Schnoll4, Paul M Cinciripini5, Larry W Hawk1. 1. Department of Psychology, University at Buffalo, State University of New York, Buffalo, NY. 2. Department of Internal Medicine, Roswell Park Cancer Institute, Buffalo, NY. 3. Department of Social and Preventive Medicine, Department of Community Health and Health Behavior, University at Buffalo, State University of New York, Buffalo, NY. 4. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA. 5. Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX.
Abstract
INTRODUCTION: Assessment of withdrawal symptoms, treatment mechanisms, and side effects is central to understanding and improving smoking cessation interventions. Though each domain is typically assessed separately with widely used questionnaires to separately assess each domain (eg, Minnesota Nicotine Withdrawal Scale = withdrawal; Questionnaire of Smoking Urges-Brief = craving; Positive and Negative Affect Schedule = affect; symptom checklist = side effects), there are substantial problems with this implicit "one questionnaire equals one construct" measurement model, including item overlap across questionnaires. This study sought to clarify the number and nature of constructs assessed during smoking cessation by developing an explicit measurement model. METHODS: Two subsamples were randomly created from 1246 smokers in a clinical trial. Exploratory and confirmatory factor analyses were conducted to identify and select a model that best represented the data. Measurement invariance was assessed to determine if the factors and their content were consistent prior to and during the quit. Improvement in construct overlap within this model was compared against the implicit measurement model using correlational analyses. RESULTS: A 5-factor measurement model composed of negative affect, somatic symptoms, sleep problems, positive affect, and craving fits the data well prior to and during quitting. All factor content except somatic symptoms was consistent over time. Correlational analyses indicated that the 5-factor model attenuated construct overlap compared to the implicit model. CONCLUSIONS: The models generated from data-driven approaches (eg, the 5-factor model) reduced overlap and better represented the constructs underlying these measures. This approach created distinct, stable constructs that span over measures of side effects and potential treatment mechanisms. IMPLICATIONS: This study demonstrated that measures assessing treatment mechanisms, withdrawal symptoms, and side effects contain problematic overlap that reduces the clarity of these key constructs. The use of data-driven approaches showed that these measures do not map on to their posited latent constructs (eg, the Minnesota Nicotine Withdrawal Scale does not yield a withdrawal factor). Rather, these measures form distinct, basic processes that may represent more meaningful constructs for future research on cessation and treatment. Assessments designed to individually examine these processes may improve the study of treatment mechanisms.
INTRODUCTION: Assessment of withdrawal symptoms, treatment mechanisms, and side effects is central to understanding and improving smoking cessation interventions. Though each domain is typically assessed separately with widely used questionnaires to separately assess each domain (eg, Minnesota Nicotine Withdrawal Scale = withdrawal; Questionnaire of Smoking Urges-Brief = craving; Positive and Negative Affect Schedule = affect; symptom checklist = side effects), there are substantial problems with this implicit "one questionnaire equals one construct" measurement model, including item overlap across questionnaires. This study sought to clarify the number and nature of constructs assessed during smoking cessation by developing an explicit measurement model. METHODS: Two subsamples were randomly created from 1246 smokers in a clinical trial. Exploratory and confirmatory factor analyses were conducted to identify and select a model that best represented the data. Measurement invariance was assessed to determine if the factors and their content were consistent prior to and during the quit. Improvement in construct overlap within this model was compared against the implicit measurement model using correlational analyses. RESULTS: A 5-factor measurement model composed of negative affect, somatic symptoms, sleep problems, positive affect, and craving fits the data well prior to and during quitting. All factor content except somatic symptoms was consistent over time. Correlational analyses indicated that the 5-factor model attenuated construct overlap compared to the implicit model. CONCLUSIONS: The models generated from data-driven approaches (eg, the 5-factor model) reduced overlap and better represented the constructs underlying these measures. This approach created distinct, stable constructs that span over measures of side effects and potential treatment mechanisms. IMPLICATIONS: This study demonstrated that measures assessing treatment mechanisms, withdrawal symptoms, and side effects contain problematic overlap that reduces the clarity of these key constructs. The use of data-driven approaches showed that these measures do not map on to their posited latent constructs (eg, the Minnesota Nicotine Withdrawal Scale does not yield a withdrawal factor). Rather, these measures form distinct, basic processes that may represent more meaningful constructs for future research on cessation and treatment. Assessments designed to individually examine these processes may improve the study of treatment mechanisms.
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