Reliability and validity of young adults' anonymous online reports of marijuana use and thoughts about use.

Marijuana is the most commonly used illicit substance among young adults in the United States, with past-month use rates estimated at 18.1% for those age 18-25 (Substance Abuse and Mental Health Services Administration, 2010b). Past-month marijuana use increased from 2009 to 2010 among both adolescents (Johnston, 2011) and young adults (SAMHSA, 2010b) in the U.S. and rates are highest among young adults compared to any other age group. Young adulthood is one of the most important developmental stages to target to understand patterns and processes associated with marijuana use. The Internet is increasingly used in survey research of substance use (Lord, Brevard, & Budman, 2011; Sumnall, Measham, Brandt, & Cole, in press), and is a promising medium to understand substance use including marijuana among young adults. Internet assessment offers a number of benefits over face-to-face interviews including broader reach; greater inclusion of low-incidence or “hidden” populations; rapid, convenient input by respondents; and reduced bias in response to sensitive, potentially stigmatizing topics including illicit substance use (Cantrell & Lupinacci, 2007; Hines, Douglas, & Mahmood, 2010; Rhodes, Bowie, & Hergenrather, 2003; Schonlau et al., 2004). These benefits are relevant for the assessment of marijuana use in American young adults, almost all of whom use the Internet (93% in a recent survey; Lenhart, Purcell, Smith, & Zickuhr, 2010). Young adults remain the age group most likely to go online and are less likely to present to traditional research settings for studies of health behavior than those in other age groups (Bost, 2005; Davies et al., 2000). As with traditional paper and pencil measures, formal study is needed to evaluate whether online measures are psychometrically sound. Web-based reports of alcohol consumption among college students have demonstrated reliability and validity when compared to face-to-face interviews (Khadjesari et al., 2009; Miller et al., 2002) or mail surveys (McCabe, Boyd, Couper, Crawford, & D’Arcy, 2002; McCabe, Diez, Boyd, Nelson, & Weitzman, 2006), and they have also been used in evaluations of Internet-based interventions aimed at reducing alcohol intake and related harm (Cunningham, Humphreys, Koski-Jannes, & Cordingley, 2005; Cunningham, Humphreys, Kypri, & van Mierlo, 2006; Koski-Jannes, Cunningham, & Tolonen, 2009; Postel, de Haan, ter Huurne, Becker, & de Jong, 2010; White et al., 2010). Previous work with college student samples has also demonstrated that online reports of marijuana use frequency are similar to those from samples recruited through the mail (McCabe et al., 2002) and marijuana use can be successfully assessed online over time (Fromme, Corbin, & Kruse, 2008; Lee, Neighbors, Hendershot, & Grossbard, 2009; Lee, Neighbors, Kilmer, & Larimer, 2010). However, little is known about the reliability and validity of online marijuana use from populations other than college students whose identity can usually be verified with enrollment statistics or student email addresses. One study assessed the prevalence of marijuana use in samples recruited both online and at a waterpipe cafe, although a formal test of validity was not conducted (Smith-Simone, Maziak, Ward, & Eissenberg, 2008). A comparison of marijuana and other substance use reports from online panelists recruited through a survey sampling company and a Dutch national panel surveyed with computer-assisted personal interviews (CAPI) found lower prevalence in the CAPI cohort compared to the online sample for all substances (weighted estimates of past-year marijuana use were 24.6 for the online sample and 11.4 for the CAPI sample among those ages 15-24), although the CAPI sample was more representative of the Dutch general population (Spijkerman, Knibbe, Knoops, Van De Mheen, & Van Den Eijnden, 2009). In addition to prevalence and frequency of marijuana use, there is a need to validate online reports of common correlates of substance use that have not been previously adapted for use with marijuana. For example, core constructs in the Transtheoretical Model of behavior change (TTM; DiClemente et al., 1991; Prochaska & DiClemente, 1983), including stage of change, decisional balance (the pros and cons of change), situational temptations to engage in a behavior, all of which have demonstrated associations with tobacco use and other health behaviors (Prochaska & DiClemente, 1983; Prochaska et al., 1994; Ramo, Hall, & Prochaska, 2011), have yet to be validated as they relate to marijuana use. The thoughts about abstinence measure that assesses desire, perceived success, difficulty, and abstinence goals in relation to tobacco, alcohol, and opiate use also has not been adapted for use in study of marijuana behavior (Hall, Havassy, & Wasserman, 1990, 1991; Prochaska et al., 2004). Marijuana use may be different because it’s addictive potential is often questioned (Budney, 2006). It is important to determine if the relationships to substance use, stage of change, and other thoughts about use hold true in an online sample of young adult marijuana users. Previous work in our research group demonstrated self-reported tobacco use and thoughts about using tobacco were generally reliable and valid as assessed in an anonymous online survey with young adult smokers throughout the U.S. (Ramo et al., 2011). The present study extended these findings in a separate, larger sample by examining the psychometric properties of marijuana use self-reported anonymously online by young adults who use both tobacco and marijuana. Measures evaluated included both commonly administered measures of marijuana use and cravings where psychometric properties have already been established in paper-and-pencil formats, as well as measures of tobacco and other substance use that were adapted specifically to assess marijuana use cognitions. Thus, this study provided an analysis of associations across multiple measures of marijuana-related constructs in young adults. To assess reliability of self-reported marijuana use, we evaluated agreement among three single item measures assessing the prevalence of past 30-day marijuana use for the entire sample and by key demographic subgroups (i.e., gender, ethnicity, and age). We also evaluated agreement between two multi-item measures of marijuana use frequency. With multi-item measures, we evaluated internal-consistency reliability. Construct validity and concurrent criterion validity were evaluated for past 30-day marijuana use frequency and stage of change for marijuana use. Construct validity is the extent to which a construct can be operationalized through demonstrating relations with constructs that should be similar (convergent validity) and non-relations with constructs that should be dissimilar (divergent validity). Concurrent criterion validity is a construct’s ability to distinguish between groups that it should be able to distinguish at the same point in time. Based on previous findings in the literature, we hypothesized the following: Convergent validity: Frequency of past 30-day marijuana use would be associated with greater marijuana dependence symptoms (Adamson et al., 2010; Adamson & Sellman, 2003), craving (Heishman, Singleton, & Liguori, 2001), and temptations (Myers, Stice, & Wagner, 1999); lower desire to quit using marijuana; lower quitting-related efficacy; greater expected difficulty for quitting marijuana; more pros and fewer cons of using marijuana (Elliott, Carey, & Scott-Sheldon, 2011); and greater tobacco and marijuana interaction expectancies (Rohsenow, Colby, Martin, & Monti, 2005); Divergent validity: Past 30-day marijuana use would be unrelated to thoughts about tobacco use (Ramo, Prochaska, & Myers, 2010); Concurrent criterion validity for marijuana use: Frequency of past 30-day marijuana use would be greater among those in an earlier stage of change (precontemplation and contemplation) compared to those ready to change (preparation), and among those with a goal of non-abstinence compared to abstinence (DiClemente et al., 1991; Prochaska et al., 2004). Frequency was also expected to be greater among men than women and among those who were unemployed compared to employed (SAMHSA, 2010b). Although the data on ethnic differences in marijuana use frequency have been inconclusive, we also examined ethnicity as a correlate of marijuana use frequency among past month users, expecting that Asian-American young adults would have lower frequency than other ethnicities (Ellickson, Martino, & Collins, 2004; SAMHSA, 2004). Validity for stage of change for marijuana use: Consistent with the TTM and prior research on tobacco use (Prochaska et al., 2004; Ramo et al., 2011), we hypothesized that participants’ marijuana use characteristics, desire to quit, expected success with quitting, anticipated difficulty with staying quit, abstinence goals, perceived pros and cons of smoking, temptation, and craving to use marijuana would vary as a function of stage of change. Finally, we explored whether nicotine and marijuana interaction expectancies would vary as a function of marijuana stage of change.