Attitudes and health behavior in diverse populations: Drunk driving, alcohol use, binge eating, marijuana use, and cigarette use.

Five different health behaviors (cigarette use, alcohol use, binge eating, illicit drug use, and drunk driving) were studied prospectively in 5 different groups of subjects. Associations between attitudes toward these behaviors and the behaviors themselves were investigated over at least 2 waves of measurement. Findings revealed that attitudes predicted behavior nonspuriously in 2 instances: alcohol use and marijuana use. Attitudes did not predict drunk driving, binge eating, or smoking behaviors. Past behavior predicted attitude in the domains of binge eating and smoking, but not in the domains of alcohol use, drunk driving, or marijuana use. The results are discussed in terms of several alternative approaches that have implications for interventions that attempt to influence health behavior through attitude change. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The detection of residual serial correlation in linear mixed models

Diggle (1988) described how the empirical semi-variogram of ordinary least squares residuals can be used to suggest an appropriate serial correlation structure in stationary linear mixed models. In this paper, this approach is extended to non-stationary models which include random effects other than intercepts, and will be applied to prostate cancer data, taken from the Baltimore Longitudinal Study of Aging. A simulation study demonstrates the effectiveness of this extended variogram for improving the covariance structure of the linear mixed model used to describe the prostate data.     

Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models.

The regression models appropriate for counted data have seen little use in psychology. This article describes problems that occur when ordinary linear regression is used to analyze count data and presents 3 alternative regression models. The simplest, the Poisson regression model, is likely to be misleading unless restrictive assumptions are met because individual counts are usually more variable ("overdispersed") than is implied by the model. This model can be modified in 2 ways to accommodate this problem. In the overdispersed model, a factor can be estimated that corrects the regression model's inferential statistics. In the second alternative, the negative binomial regression model, a random term reflecting unexplained between-subject differences is included in the regression model. The authors compare the advantages of these approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models.

Marital and family researchers often study infrequent behaviors. These powerful psychological variables, such as abuse, criticism, and drug use, have important ramifications for families and society as well as for the statistical models used to study them. Most researchers continue to rely on ordinary least-squares (OLS) regression for these types of data, but estimates and inferences from OLS regression can be seriously biased for count data such as these. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Earnings, education, genetics, and environment

A major and well-recognized difficulty in estimating the effects of education on earnings is that the more educated are likely to be more able, irrespective of education. If ability also determines earnings and is not controlled, ordinary least squares will yield biased estimates of the education coefficient. In this study, we use data on identical twins to control for differences in ability that arise from genetic endowments and family environment. Not controlling for genetics and family environment may cause a large bias, up to two-thirds of the noncontrolled coefficient.     

A comparison of one-sided methods to identify significant individual outcomes in a multiple outcome setting: stepwise tests or global tests with closed testing

We compare two approaches to the identification of individual significant outcomes when a comparison of two groups involves multiple outcome variables. The approaches are all designed to control the familywise error rate (FWE) with any subset of the null hypothesis being true (in the strong sense). The first approach is initially to use a global test of the overall hypothesis that the groups are equivalent for all variables, followed by an application of the closed testing algorithm of Marcus, Peritz and Gabriel. The global tests considered here are ordinary least squares (OLS), generalized least squares (GLS), an approximation to a likelihood ratio test (ALR), and a new test based on an approximation to the most powerful similar test for simple alternatives. The second approach is that of stepwise testing, which tests the univariate hypotheses in a specific order with appropriate adjustment to the univariate p-values for multiplicity. The stepwise tests considered include both step-down and step-up tests of a general type, and likewise permutation tests that incorporate the dependence structure of the data. We illustrate the tests with two examples of birth outcomes: a comparison of cocaine-exposed new-borns to control new-borns on neurobehavioural and physical growth variables, and, in a separate study, a comparison of babies born to diabetic mothers and babies born to non-diabetic mothers on minor malformations. After describing the methods and analysing the birth outcome data, we use simulations on Gaussian data to provide guidelines for the use of these procedures in terms of power and computation.     

Structural-equation models of current drug use: Are appropriate models so simple(x)?

The simplex and common-factor models of drug use were compared using maximum-likelihood estimation of latent variable structural models in two samples: a sample of 226 high school students, using ratio-scale measures of current drug use, and a sample of 310 industrial workers and 811 college students, using ordinal-scale measures of current drug use. Latent variables of alcohol, marihuana, enhancer hard drugs, and dampener hard drugs were specified in a series of structural models. Contrary to previous findings with cumulative drug-use data, the common-factor model provided a more acceptable representation of the observed current-use data than did the simplex model in both samples. In addition, the similarity of results across both of these samples supports recent contentions by Huba and Bentler (1982) that quantitatively measured variables are not necessarily superior to qualitative, ordinal indicators in latent variable models of drug use. (49 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Constraints on general slowing: a meta-analysis using hierarchical linear models with random coefficients

General slowing (GS) theories are often tested by meta-analysis that model mean latencies of older adults as a function of mean latencies of younger adults. Ordinary least squares (OLS) regression is inappropriate for this purpose because it fails to account for the nested structure of multitask response time (RT) data. Hierarchical linear models (HLM) are an alternative method for analyzing such data. OLS analysis of data from 21 studies that used iterative cognitive tasks supported GS; however, HLM analysis demonstrated significant variance in slowing across experimental tasks and a process-specific effect by showing less slowing for memory scanning than for visual-search and mental-rotation tasks. The authors conclude that HLM is more suitable than OLS methods for meta-analyses of RT data and for testing GS theories.     

Tobacco, alcohol, and caffeine use: A review of their interrelationships.

Reviews observational and survey studies on the simultaneous use of 2 or more of the following substances: tobacco, alcohol, or caffeine. Evidence to date indicates that, among each of the 3 possible pairs of relationships of these 3 substances, alcohol and tobacco use and tobacco and caffeine use are moderately to strongly related, and caffeine and alcohol use weakly related. These studies included the use of tobacco and alcohol by adolescents and the use of cigarettes by alcoholics. No studies examined the concurrent use of all 3 substances. The need for improved methods of assessing substance use and research examining health-risk behaviors as interrelated clusters is emphasized, and mechanisms that might account for these interrelationships are discussed. Although there are no general theoretical models that adequately account for these interrelationships, the role of several specific pharmacological or behavioral mechanisms as well as generalized individual difference factors may be postulated. (89 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A longitudinal study of preschoolers' language-based bedtime routines, sleep duration, and well-being.

To investigate the associations of caregiver-reported use of language-based bedtime routines among preschoolers at age 3 with children's nighttime sleep duration and cognitive, behavioral, and health outcomes at age 5. Furthermore, we wanted to identify whether parental or household characteristics help explain these associations. We used data on 4,274 children from birth to age 5, drawn from the Fragile Families and Child Wellbeing Study (FFCW), a longitudinal birth cohort study of at-risk children born between 1998 and 2000 in 20 U.S. cities. We used ordinary least squares (for continuous outcomes) and logistic regressions (for dichotomous outcomes) to estimate associations of language-based bedtime routines at age 3 with sleep duration and cognitive, behavioral, and health outcomes at age 5, net of a host of child and family background characteristics. After adjustment for child and family characteristics, we observed a positive association between language-based bedtime routines and both nighttime sleep duration and verbal test scores. Language-based bedtime routines were also inversely associated with behavior problems (anxious, withdrawn, and aggressive behaviors) and positively associated with better general health, yet adjustment for family background characteristics attenuated these associations. No associations were found between language-based bedtime routines and obesity. Results were confirmed using propensity score matching. This research suggests regular use of language-based bedtime routines including singing, reading, and/or storytelling at bedtime may have a lasting positive benefit for children's sleep duration and cognitive development; we find little evidence of such a benefit for child behavior or health. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Specification and estimation of causal models in social psychology: Comment on Tesser and Paulhus.

A. Tesser and D. L. Paulhus (see record 1978-31808-001) proposed a "causal model" of the relations, including reciprocal causation, among love and several other variables. The present author contends that the model is wrongly specified in several respects, and as a result the procedure used to estimate its parameters, ordinary least squares regression, produces biased and inconsistent results. Careful attention to the assumptions of a model is essential for the use of structural equation modeling techniques in any field. Existing theories in many areas of social psychology may not be strong enough to support the use of such techniques with nonexperimental data, and several alternative methods are available to attack problems like that of Tesser and Paulhus. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Expectancy models of alcohol use.

The primary goal of the present article is to compare expectancy models with competing attitude models of alcohol use. First, several methodological issues in expectancy research were addressed, to more adequately compare the theoretical models. Study 1 examined the effect of possible self-report biases on associations among expectancy constructs and alcohol use. In Studies 2 and 3, the basic distinction between general factors of positive and negative alcohol expectancies was investigated in both cross-sectional and prospective models. Alternative predictions that were based on competing expectancy and attitude theories were evaluated primarily in Study 3. Results from these studies supported the validity of the expectancy constructs and the proposed distinctions among expectancy and attitude constructs in terms of strong discriminant validity, absence of self-report bias, and differential prediction of alcohol use. Furthermore, the findings favored certain expectancy models over alternative attitude models of alcohol use, reaffirming the usefulness of the expectancy framework. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Estimating coefficients in linear models: It don't make no nevermind.

Reviews the literature and shows that under very general circumstances coefficients in multiple regression models can be replaced with equal weights with almost no loss in accuracy on the original data sample. It is then shown that these equal weights will have greater robustness than least squares regression coefficients. The implications for problems of prediction are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Academic and social motives and drinking behavior.

This longitudinal study of 1,447 first-time college students tested separate time-varying covariate models of the relations between academic and social motives/behaviors and alcohol use and related problems from senior year of high school through the end of the second year in college. Structural equation models identified small but significant inverse relations between academic motives/behaviors and alcohol use across all time points, with relations of somewhat larger magnitude between academic motives/behaviors and alcohol-related problems across all semesters other than senior year in high school. At all time points, there were much larger positive relations between social motives/behaviors and alcohol use across all semesters, with smaller but significant relations between social motives/behaviors and alcohol-related problems. Multi-group models found considerable consistency in the relations between motives/behaviors and alcohol-related outcomes across gender, race/ethnicity, and family history of alcohol problems, although academic motives/behaviors played a stronger protective role for women, and social motives were a more robust risk factor for Caucasian and Latino students and individuals with a positive family history of alcohol problems. Implications for alcohol prevention efforts among college students are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parathyroid hormone regulates the expression of the nuclear mitotic apparatus protein in the osteoblast-like cells, ROS 17/2.8

This study assessed the prevalence of tobacco and alcohol use in top-grossing American films from 1985 to 1995. The top ten money-making films for 1985 to 1995 were viewed to identify the prevalence of alcohol and tobacco use overall and by lead characters. While the use of tobacco and alcohol was stable across the study period, use of these substances was common. Most films (98%) had references that supported tobacco use and 96% had references that supported alcohol use. Discouraging the use of these substances was uncommon. Only one third of films had any references that discouraged the use of tobacco or alcohol (38% and 37%, respectively). At least one lead character used tobacco or alcohol in 46% and 79% of the films, respectively. The hazards of smoking and drinking are not reflected in the behaviors of film characters who are potential role models for youth facing the decision to smoke or drink.     

Random effects probit and logistic regression models for three-level data

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.     

Australian encephalitis in the Northern Territory: clinical and epidemiological features, 1987-1996

OBJECTIVES: The purpose of this study was to evaluate the contribution of the executive cognitive functions to self-reported and observed performance of activities of daily living and instrumental activities of daily living. DESIGN: These data were collected as part of a cross-sectional survey of community-dwelling older persons, using statistical sampling, in a two-county area of southern Colorado. SETTING: Participants were interviewed and administered measures of general cognition, depression, executive functioning, and performance of self-care and instrumental activities, either in their homes or at the study clinic. PARTICIPANTS: A total of 1158 community-dwelling persons between the ages of 60 and 99 participated. Of these, 657 were female, 501 were male, 637 were Hispanic, and 521 were non-Hispanic whites. MEASUREMENTS: Subjects were administered the Mini-Mental State Exam (MMSE), a measure of executive functioning (the Behavioral Dyscontrol Scale), the Center for Epidemiologic Studies Depression scale (CES-D), and the Structured Assessment of Independent Living Skills (SAILS, a measure of observed performance of daily tasks). They also were interviewed regarding their reported ADL and IADL status using measures from the Longitudinal Study on Aging. RESULTS: Both general mental status and executive functioning demonstrated statistically significant univariate associations with all seven functional status measures (both self-report and observed performance). In a series of ordinary least squares regression models, executive functioning was a predictor for self-reported ADLs and observed performance of complex IADL tasks such as managing money and medications. Mental status did not predict self-reported functioning but was a predictor of observed performance. Depression was a significant variable for self-report measures but not for observed performance. Executive functioning and general mental status demonstrated some degree of independence from one another. CONCLUSION: Executive functioning is an important determinant of functional status for both self-reported and observed ADLs and IADLs and should be assessed routinely when evaluating the mental status and functional abilities of older people. These results replicate and extend previous research on executive functioning among older persons by examining these factors in a large bi-ethnic community sample.     

Shared Predictors of Youthful Gambling, Substance Use, and Delinquency.

Given that gambling, alcohol misuse, other drug use, and delinquency are correlated, it is hypothesized that these problem behaviors have shared antecedents. Measures from 3 explanatory domains--sociodemographic factors (age, race, and socioeconomic status), individual factors (impulsivity and moral disengagement), and socialization factors (parental monitoring and peer delinquency)--were tested for links to problem behaviors in 2 longitudinal samples of adolescents. Black youth had lower levels of problem behaviors than Whites. Impulsivity was a significant predictor of alcohol misuse for females and delinquency for males. Moral disengagement predicted gambling for males. Parental monitoring showed a significant inverse relationship to alcohol misuse and other substance use for males. Peer delinquency showed numerous prospective paths to youth problem behaviors for both genders. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Working memory capacity moderates the predictive effects of drug-related associations on substance use.

Some theories suggest that spontaneously activated, drug-related associations in memory may have a "freer reign" in predicting drug use among individuals with lower working memory capacity. This study evaluated this hypothesis among 145 at-risk youth attending continuation high schools (CHS). This is the 1st study to evaluate this type of dual-process interaction in the prediction of drug use among a sample of at-risk adolescents. The CHS students completed assessments of drug-related memory associations, working memory capacity, and drug use. Control variables included age, gender, ethnicity, and acculturation. Robust multiple regression using least trimmed squares estimation indicated that there was a significant linear by linear interaction between working memory capacity (assessed with the self-ordered pointing task) and drug-related associations (assessed with verb generation and cue-behavior association tasks) in the prediction of alcohol and cigarette use. Consistent with dual-process cognitive theories, drug-related associations in memory predicted drug use more strongly in students with lower levels of working memory capacity. These findings add to the literature implicating the influence of dual cognitive processes in adolescent risk behaviors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)