Theories of planned behavior: Smoking

Theories of planned behavior: Smoking To examine if the Theory of Planned Behavior (TPB) predict smoking behavior, 35 data sets (N= 267,977) have been synthesized, containing 219 effect sizes between the model variables using a meta-analytic structural equation modeling approach (MASEM). Consistent with the TPBs predictions, 1) smoking behavior was related to smoking intentions (weighted mean r =.30), 2) intentions were based on attitudes (weighted mean r =.16) and subjective norms (weighted mean r =.20). Consistent with TPBs hypotheses, perceived behavioral control was related to smoking intentions (weighted mean r = -.24) and behaviors (weighted mean r =-.20) and it contribute significantly to cigarette consumption. The strength of associations, however, was influenced by studies and participants characteristics. Smoking remains the leading preventable cause of death and disease in western countries. Despite the constant reduction in smoking prevalence among adults over the last 20 years in developed countries, smoking rates have not decreased among young people, and the highest youth smoking rates can be found in Central and Eastern Europe. In an attempt to understand the psychosocial determinants of smoking initiation and maintenance, a variety of social cognitive models have been applied. One of the most influential theories predicting smoking behavior, the Theory of Planned Behavior (TPB) 1has been used both for conducting a wide range of empirical research on smoking behavior antecedents and for designing many theory-based intervention programs to reduce tobacco consumption. An increasing number of empirical studies have examined this model in relation to smoke and the variability of results suggests that a quantitative integration of this literature would prove valuable. Up to the present, various quantitative reviews of the TPB have been performed but centered in other behavioral outcomes, as exercise, 2 condom use 3 and others. Hence, the purpose of this study was to evaluate the success of TPB as a predictor of smoking behavior through meta-analytic structural equation modeling (MASEM), involving the techniques of synthesizing correlation matrices and fitting SEM as suggested by Viswesvaran and Ones. 4 The TPB, an extension of the Theory of Reasoned Action,5 incorporates both social influences and personal factors as predictors, specifying a limited number of psychological variables that can influence a behavior, namely 1) intention; 2) attitude; 3) subjective norm (SN); and 4) perceived behavioral control (PBC). 1 First, subjective norms are conceptualized as the pressure that people perceive from important others to execute a behavior. Second, peoples positive or negative evaluations of their performing a behavior are conceptualized as other predictor of intention (attitudes). Third, PBC represents ones evaluation about the easy or difficulty of adopting the behavior and it is assumed to reflect the obstacles that one encountered in past behavioral performances. Finally, attitudes, SN and PBC are proposed to influence behavior through their influence on intentions, which summarize persons motivation to act in a particular manner and indicate how hard the person is willing to try and how much time and effort he or she is willing to devote in order to perform a behavior. 6 The TPB has been applied through a relevant amount of primary studies and their predictive utility has been proved meta-analytically both for a wide range of behaviors 7, 6 and for specific health risky or health protective behaviors. 3, 2 These previous meta-analyses, however, have neither examined how useful the TPB is to predict smoking behavior, nor the overall structure of the model applied to tobacco consumption. Hence, some concerns remain relating to TPB and its utility to predict smoking behavior that deserves further examination through MASEM. Firstly, a weakness of the SN-intention relation has been found by previous meta-analysis 7 compared with attitude-intention and PBC-intention associations. It has been suggested that this lack of association indicates that intentions are influenced primarily by personal factors 6. In spite of, some primary studies finding strong beta values, ranging from .44 to .62, for attitude on smoking intention such as Hanson, 8 while others founded values near .18 or .19. 9, 10 At the same time, although researchers have theorized about the importance of PBC in this domain, regarding health-risky behaviors, the correlation between PBC and behavior had sometimes been disappointing. 3 One possible explanation is that PBC may not capture actual control. Other is that risky behaviors performed in social contexts may be more determined by risky-conducive circumstances than by personal factors. 11 Moreover, primary studies on smoking behavior have found contrasting results for PBC -behavior, such as r =.55 12 or r =.06. 13 Based on these discrepant findings, we proposed, as a first purpose of this review, to test the strength of relationships between TPB constructs applied to smoking behavior. Secondly, in order to clarify the influence of moderator variables and to provide further explanation for the variability on the effect sizes (ES) between primary studies, some studies and participants characteristics may be taken into account. Ajzen and Fishbein 5 argued that intention and behavior should be measured as close in time as possible to the behavior. In spite of that, primary studies on smoking behavior 14, 15 have found that beta values for intention- behavior association have been maintained during six months (à ¯Ã‚ Ã‚ ¢=.38), nine months (à ¯Ã‚ Ã‚ ¢=.35) and a year (à ¯Ã‚ Ã‚ ¢=.35). Thus, it is important to quantitatively review the moderator effect of time interval on strength of TPB constructs. It has been recognized that culture provides a social context that affects prevalence of certain behaviors. Moreover, some studies have compared results of TPB applied to smoking behavior by using diverse ethnic groups into the USA, such as Hanson, 8 while a great amount of primary studies have expanded their applicability to different cultural contexts. 16, 15, 10 These studies have revealed contradictory results, such as for Puerto-Ricans and non-Hispanic whites, SN was not found as a significant predictor of intention, 8 while it was significant for African-American teenagers, or beta values for SN-behavior ranging from à ¯Ã‚ Ã‚ ¢=.20 for UK samples 17 to à ¯Ã‚ Ã‚ ¢ =.43 for Netherlanders students. 18 Hence, because of cultural differences with respect to the SN-outcomes association, there is a need to meta-analytically examine the moderator effect of culture. Ajzen and Fishbein 5 and Ajzen 19 also recommended scale correspondence of measures for intention to properly predict behavior. However, meta-analysis on TPB applied to exercise behavior have found that only 50% of examined studies had scale correspondence, 20 and that ES was the strongest for the intention-behavior association when studies had scale correspondence. 2 Based on these previous findings, we contend that a thorough examination of moderator effect of scale correspondence on strength of smoking intention and behavior relationships is needed. Research indicates that teenage years are associated with heightened sensitivity to SN 6 and differences have been found in previous meta-analyses between age groups regarding their intention -exercise behavior association. 2 At the same time, only one study has tested gender differences applying TPB to cigarette smoking, 13 founding that the model fitted better among female students. Despite the fact that no consistent evidence has been found relating to the moderator effect of age and gender on the TPB constructs association, we state that an exploratory analysis would be advisable. Thirdly, while previous studies on TPB on smoking behavior had used stepwise regression analyses, more recent ones apply SEM or path-analyses. When all TPB relationships were tested simultaneously, same patterns would change. For instance, after controlling the influence of intention, the PBC- behavior association would turn to negligible values (à ¯Ã‚ Ã‚ ¢=.05), such as Albarracà ­n et al 3 proved for condom use. Moreover, based on the fruitful results of meta-analysis obtained in many research domains, 3, 21, 22, 23, 24, 25 it can be beneficial to use meta-analytic structural equation modeling techniques (MASEM) in testing causal models, such as some authors suggested. 4, 26 Based on these methodological and conceptual issues, the main objective of this meta-analysis was threefold. The first objective was to test the strength of the relationships between the TPB constructs with the smoking behavior. Specifically, we hypothesized: (1) large ES for intention-behavior, PBC-intention, PBC-behavior, and attitude -intention; (2) moderate ES for SN- intention; (3) larger ES for intention-behavior than for PBC-behavior and (4) larger ES for PBC-intention and SN-intention than for attitude-intention. The second purpose was to test the influence of moderator variables on the relationships between the TPB constructs. Specifically, we proposed (5) larger ES for attitude- behavior, PBC- behavior, SN-behavior, and intention-behavior when measures have been taken simultaneously; (6) larger ES when the time interval was shorter; (7) the largest ES for SN-intention and SN- behavior when participants belong to a collectivist culture, coded as Others into the category orig in of the sample; (8) larger ES for attitude- intention, SN-intention, PBC-intention and intention -behavior when constructs have been measured with scale correspondence; and (9) mean age of the sample, percentage of males and year of publication would moderate the relationships among TPB constructs. The third purpose was to test the predictive utility of TPB on smoking behavior through MASEM analyses. Specifically, we hypothesized that: (10) intention and PBC will predict smoking behavior; (11) attitude, PBC, and SN will predict intention and (12) intention will be a stronger predictor of behavior than PBC. Method Literature search In order to locate relevant studies, we conducted a computerized bibliographic search of the PsycInfo, MedLine, ERIC, using the terms smoke, smoking behavior, nicotine, tobacco consumption, and TPB as keywords. We also conducted a manual search of journals that regularly published smoking behavior research. Descendent searches have been conducted based on the references section of retrieved studies specifically previous TPB meta-analyses including multiple behavioral outcomes- and some authors have been contacted to obtain unpublished papers. This processes resulted in 52 studies retrieved in full text to further screening. Inclusion and exclusion criteria A study was considered for this meta-analysis if it met the following inclusion criteria: (1) the study had to report quantitative research on TPB applied to smoking behavior; (2) the study had to report a Pearson correlation coefficient between TPB constructs or data that enable us to calculate ES. Upon closer examination of the remaining 52 studies, a total of 27 studies were included which provided an amount of 35 independent samples (N= 267,977) and 219 ES. A total amount of 25 studies were excluded. Reasons for elimination have been that TPB construct measures were not included (8 studies), i.e.: 27, or that the studies were focused on smoking cessation instead of on smoking behavior (17 studies), i.e.: 28, 29. Only one dissertation has been included and no unpublished papers have been obtained. The studies that focused on smoking cessation have been excluded because the outcome variable in the model-smoking behavior versus smoking cessation-differs substantially. These studies will be used to conduct a separate meta-analysis on smoking cessation. All the included studies are marked with an asterisk in the reference section. Coding of studies The study characteristics coded were: year of publication, origin of the sample, scale correspondence, and time interval between TPB measures. The subject characteristics coded were: the number or participants, mean age of the sample, and gender (as percentage of men in the sample). We consider relevant to code how smoking behavior was assessed (i.e., objective vs. self-report.) but we could find only one study which used objective measures, as CO (carbon monoxide) tests. 30 Following the procedures of Symons and Hausenblas, 2 the time interval between intention and behavior was examined by classifying the studies as: (1) short (less than or equal to six months), (2) medium (greater than six months and less than or equal to one year), (3) large (greater that one year). Regarding scale correspondence, we examined the method section of each study in search of the detailed information. Such as Symons and Hausenblas suggested 2, scale correspondence has been fulfilled when the same magni tude, frequencies or response formats are used to assess the constructs. If intention and behavior were measured exactly with the same items, we considered that scale equivalence was present. If intention was measured with a broader redaction (i.e.: How certain are you that you could resist smoking this term?) while behavior was assessed by a more detailed item (i.e.: How many cigarettes did you smoke per day?), or by asking participants to classify themselves as non-smoker/current-smoker, we considered that scale correspondence has not been fulfilled. In order to ensure accuracy, the studies were coded by two authors independently, reaching an intercoder agreement of 90%. The level of agreement reached was highly satisfactory and inconsistencies were solved by consensus. Some decisions about independence of the samples were taken. If the same study design was carried out in multiple but independent samples (i. e, boys and girls, asthmatic and no-asthmatic students, African-American, Puerto Rican and Non-Hispanic white teenagers) results were entered into the meta-analysis as independent samples. 8, 18, 13 In other cases, only one ES per study has been considered. Data analysis We followed Hedges and Oldkins 31 meta-analytic fixed effects procedures to estimate weighted mean correlations. In these procedures, correlations were converted using Fishers r to z transformations and weighted by N 3, the inverse of which is the variance of z, in analyses. Using Cohens criteria, 32 ES values of .10, .30 and .50 were considered small, moderate and large effects, respectively. Graphical procedures were used to explore the skewness of data. When an extreme value was detected, analyses were carried both including and excluding the outlier. Next, we tested the homogeneity of the ES (Q statistics) and we analyzed the influence of moderator variables using categorical model (ANOVA analogous) and weighted regression analyses (fixed-effect model). One problem in the interpretation of meta-analytic results is the potential bias of the mean ES due to sampling error or to systematic omission of studies that are hard to locate. According to Orwin, 33 the tolerance index of nul l results should be calculated and there must be more than 300 unpublished studies (and not recovered by the meta-analyst) for the results to be annulled. However, this statement should be qualified because the index by categories yields small values in some of these categories. Therefore, we can conclude that publication bias is not very likely to threaten the results severely. MASEM analyses Meta-analytic structural equation modeling, which involves the techniques of synthesizing correlation matrices and fitting SEM, is usually done by applying meta-analytic techniques on a series of correlation matrices to create a pooled correlation matrix, which then can be analyzed using SEM, as suggested Viswesvaran and Ones. 4 However, these procedures have received criticism by Becker (1992) and more recently by Cheung and Chan. 26 Despite some problems, the major advantage of these univariate approaches are their ease of application in applied contexts. Based on these recommendations, we used Viswesvaran and Ones procedure to test the strength of the association among the TPB constructs with smoking behavior. The complete weighted correlation matrix was 5 x 5 and it was submitted to SEM analyses. The predicted model was fitted assuming the harmonic mean (N= 239) as sample size, 4 and it was estimated with unweighted least squares procedures. The proposed model, according to TPB l iterature, had three exogenous latent variables and two endogenous ones, such as depicted Figure 1. Besides chi-square, we reported Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Normative Fit Index (NFI), and Root Mean Squared Residual (RMR) as fitness indices. It is typically assumed that GFI, AGFI, and NFI >=.90, RMR values