A two‐part mixed effects model for cigarette purchase task data

The Cigarette Purchase Task is a behavioral economic assessment tool designed to measure the relative reinforcing efficacy of cigarette smoking across different prices. An exponential demand equation has become a standard model for analyzing purchase task data, but its utility is compromised by its inability to accommodate values of zero consumption. We propose a two‐part mixed effects model that keeps the same exponential demand equation for modeling nonzero consumption values, while providing a logistic regression for the binary outcome of zero versus nonzero consumption. Therefore, the proposed model can accommodate zero consumption values and retain the features of the exponential demand equation at the same time. As a byproduct, the logistic regression component of the proposed model provides a new demand index, the “derived breakpoint”, for the price above which a subject is more likely to be abstinent than to be smoking. We apply the proposed model to data collected at baseline from college students (N = 1,217) enrolled in a randomized clinical trial utilizing financial incentives to motivate tobacco cessation. Monte Carlo simulations showed that the proposed model provides better fits than an existing model. We note that the proposed methodology is applicable to other purchase task data, for example, drugs of abuse.     

Visual analysis of data in a multielement design

Ninety Board Certified Behavior Analysts (BCBAs) and 19 editorial board members evaluated hypothetical data presented in a multielement design. We manipulated the variability, trend, and mean shift of the data and asked the participants to determine if the data demonstrated experimental control. The results showed that variability, trend, and mean shift interacted to affect the participants’ ratings of experimental control. The level of agreement between participants was variable, but was generally lower than in previous research.     

A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

Happiness and life satisfaction in Rwanda

This study investigated predictors of happiness and life satisfaction in Rwanda. Data from the World Values Survey and gathered from 3 030 Rwandese (age ranging 16 to 90 years, mean age = 34.2, SD = 12.7; females = 50.5%) were pooled for the analysis. For the comparison, international World Values Survey data were utilised. A fixed effects multilevel regression model was used to predict happiness and life satisfaction from gender, health, socio-economic, and some subjective measures. Males had greater self-rated happiness and life satisfaction scores than females. State of health and sense of freedom of choice predicted both happiness and life satisfaction. Valuing of friends, weekly religious attendance, and national pride positively predicted happiness, whereas household’s financial satisfaction, full-time employment, high-income group, being a student, and sense of trust predicted life satisfaction. This study suggests that health status, household’s financial satisfaction and emancipative values could maximise subjective well-being in Rwanda.     

Health‐adjusted neuropsychological test norms based on 463 older Swedish car drivers

There is a need for improved normative information in particular for older persons. The present study provides neuropsychological test norms on seven cognitive tests used in a sample representing the general older driving population, when uncontrolled and controlled for physical health. A group of 463 healthy Swedish car drivers, aged 65 to 84 years, participated in a medical and neuropsychological examination. The latter included tests of visual scanning, mental shifting, visual spatial function, memory, reaction time, selective attention, and simultaneous capacity. Hierarchical regression analyses demonstrated that, when uncontrolled for health, old age was associated with significant impairment on all seven tests. Education was associated with a significant advantage for all tests except most reaction time subtests. Women outperformed men on selective attention. Controlling for health did not consistently change the associations with education, but generally weakened those with age, indicating rises in normative scores of up to 0.36 SD (residual). In terms of variance explained, impaired health predicted on average 2.5%, age 2.9%, education 2.1% and gender 0.1%. It was concluded (1) that individual regression‐based predictions of expected values have the advantage of allowing control for the impact of health on normative scores in addition to the adjustment for various demographic and performance‐related variables and (2) that health‐adjusted norms have the potential to classify functional status more accurately, to the extent that these norms diverge from norms uncontrolled for physical health.     

Happiness begets children? Evidence for a bi-directional link between well-being and number of children

The purpose of the current research was to examine the association between different facets of well-being and parenthood status. Specifically, using two longitudinal data sets, the present research explored whether individuals who possess high cognitive (Studies 1 & 2), emotional, and psychological well-being (Study 2) are more likely to subsequently become parents compared to their less happy counterparts. The results of both studies demonstrated that well-being at Time 1 positively predicted number of children at Time 2, controlling for a number of relevant variables (e.g. income, age). Additional analyses revealed that the relationship between cognitive well-being and subsequently having children was particularly strong for people who did not have any children previously. Potential mechanisms explaining how and why well-being may influence parenthood are discussed.     

Relationship between collectivism and corruption in American and Chinese books: A historical perspective

Previous research on the relationship between collectivism and corruption has not investigated their co‐variation over time. In this study, we use Google Ngram Viewer to track the frequency of words related to collectivism and corruption in American books (1800–2000) and in Chinese Books (1970–2008). The results demonstrate that a positive association between the usage of these terms during the periods in both Chinese and American books, with changes in words related to collectivism preceding changes in words related to corruption in American books. The theoretical and practical implications are also discussed.     

Secularity and Irreligion in Cross‐National Context: A Nonlinear Approach

As part of the conversation about the characteristics and scope of secularization in contemporary society, the implications of low levels of self‐defined atheism and explicit rejection of religion in nations with low levels of religious beliefs are not yet sufficiently explored. In response, this study uses aggregated data from the World Values Survey, the European Values Study, and Cross‐National Socio‐Economic and Religion Data from the Association of Religion Data Archives to investigate the relationships between nonbelief, atheism, and lack of confidence in churches or religious organizations. Regression findings show a concave curvilinear relationship between secularity (measured as the percentage of the population that does not believe in God) and irreligion (measured as (1) the percentage of self‐defined atheists in a country and (2) the percentage of individuals in a country who have no confidence at all in religious organizations). In other words, this study suggests that nonbelief is associated with an increase in self‐defined atheism and the lack of confidence in religious organizations, but that this effect wanes the more secular a society is. The findings further highlight the complexity of nonreligiosity and imply that a linear association cannot be assumed between measures of secularization, secularity, and irreligion.     

A New Multilevel CART Algorithm for Multilevel Data with Binary Outcomes

The multilevel logistic regression model (M-logit) is the standard model for modeling multilevel data with binary outcomes. However, many assumptions and restrictions should be considered when applying this model for unbiased estimation. To overcome these limitations, we proposed a multilevel CART (M-CART) algorithm which combines the M-logit and single level CART (S-CART) within the framework of the expectation-maximization. Simulation results showed that the proposed M-CART provided substantial improvements on classification accuracy, sensitivity, and specific over the M-logit, S-CART, and single level logistic regression model when modeling multilevel data with binary outcomes. This benefit of using M-CART was consistently found across different conditions of sample size, intra-class correlation, and when relationships between predictors and outcomes were nonlinear and nonadditive.     

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.