Missing data: prevalence and reporting practices

Results are described for a survey assessing prevalence of missing data and reporting practices in studies with missing data in a random sample of empirical research journal articles from the PsychINFO database for the year 1999, two years prior to the publication of a special section on missing data in Psychological Methods. Analysis indicates missing data problems were found in about one-third of the studies. Further, analytical methods and reporting practices varied widely for studies with missing data. One may consider these results as baseline data to assess progress as reporting standards evolve for studies with missing data. Some potential reporting standards are discussed.     

Missing data techniques for structural equation modeling

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.     

Missing data: our view of the state of the art

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.     

Missing data imputation and corrected statistics for large-scale behavioral databases

This article presents a new methodology for solving problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, and a new method of imputation for missing data is proposed. This methodology is applied to the Dutch Lexicon Project database recently published by Keuleers, Diependaele, and Brysbaert (Frontiers in Psychology, 1, 174, 2010), which allows us to conclude that this database fulfills the conditions of use of the method recently proposed by Courrieu, Brand-D’Abrescia, Peereman, Spieler, and Rey (2011) for testing item performance models. Two application programs in MATLAB code are provided for the imputation of missing data in databases and for the computation of corrected statistics to test models.     

Maximizing data quality and shortening survey time: Three-form planned missing data survey design

Simulation studies have shown the three-form planned missing data design efficiently collects high quality data while reducing participant burden. This methodology is rarely used in sport and exercise psychology. Therefore, we conducted a re-sampling study with existing sport and exercise psychology survey data to test how three-form planned missing data survey design implemented with different item distribution approaches effect constructs’ internal measurement structure and validity. Results supported the efficacy of the three-form planned missing data survey design for cross-sectional data collection. Sample sizes of at least 300 (i.e., 100 per form) are recommended for having unbiased parameter estimates. It is also recommended items be distributed across survey forms to have representation of each facet of a construct on every form, and that a select few of these items be included across all survey forms. Further guidelines for three-form surveys based upon the results of this resampling study are provided.     

2PLM下缺失数据处理方法及其比较

项目反应理论(IRT)是用于客观测量的现代教育与心理测量理论之一,广泛用于缺失数据十分常见的大尺度测验分析。IRT中两参数逻辑斯蒂克模型(2PLM)下仅有完全随机缺失机制下缺失反应和缺失能力处理的EM算法。本研究推导2PLM下缺失反应忽略的EM 算法,并提出随机缺失机制下缺失反应和缺失能力处理的EM算法和考虑能力估计和作答反应不确定性的多重借补法。研究显示：在各种缺失机制、缺失比例和测验设计下,缺失反应忽略的EM算法和多重借补法表现理想。     

Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values

Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured covariance matrix (CPM-U) and the true covariance matrix (CPM-T), multiple imputation-based generalized estimating equations (MI-GEE), and weighted generalized estimating equations (WGEE). Under the missing at random mechanism, the MI-GEE approach was always robust. The CPM-T and CPM-U methods were also able to control the error rates provided that certain minimum sample size requirements were met, whereas the WGEE was more prone to inflated error rates. In contrast, under the missing not at random mechanism, all evaluated approaches were generally invalid. Our results also indicate that the CPM methods were more powerful than the MI-GEE and WGEE methods and their superiority was often substantial. Furthermore, we note that little or no power was sacrificed by using CPM-U method in place of CPM-T, although both methods have less power in situations where some participants have incomplete data. Some aspects of the CPM-U and MI-GEE methods are illustrated using real data from 2 previously published data sets. The first data set comes from a randomized study of AIDS patients with advanced immune suppression, the second from a cohort of patients with schizotypal personality disorder enrolled in a prevention program for psychosis.     

Interval estimation for the intraclass correlation in dirichlet-multinomial data

When the underlying distribution is discrete with a limited number of categories, methods for interval estimation of the intraclass correlation which assume normality are theoretically inadequate for use. On the basis of large sample theory, this paper develops an asymptotic closed-form interval estimate of the intraclass correlation for the case where there is a natural score associated with each category. This paper employs Monte Carlo simulation to demonstrate that when the underlying intraclass correlation is large, the traditional interval estimator which assumes normality can be misleading. We find that when the number of classes is 20, the interval estimator proposed here can generally perform reasonably well in a variety of situations. This paper further notes that the proposed interval estimator is invariant with respect to a linear transformation. When the data are on a nominal scale, an extension of the proposed method to account for this case, as well as a discussion on the relationship between the intraclass correlation and a kappa-type measure defined here and on the limitation of the corresponding kappa-type estimator are given.The authors wish to thank the Editor, the Associate Editor, and the three referees for many valuable comments and suggestions to improve the clarity of this paper. The works for the first, the third, and the fourth authors were partially supported by grant #R01AR43025-01 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases.     

Missing sights: consequences for visual cognitive development

The effects of early-onset blindness on the development of the visual system have been explained traditionally by the stabilization of transient connections through Hebbian competition. Although many of the findings from congenital cataract and congenital blindness are consistent with that view, there is inconsistent evidence from studies of visual cognition in children treated for visual deprivation from cataract, case reports of recovery of vision in adults, and studies of visual reorganization after late-onset blindness. Collectively, the data from congenital cataract and congenital blindness indicate that early visual experience sets up the infrastructure for later learning involving both the dorsal ("where") and ventral ("what") streams. Nevertheless, there is surprising residual plasticity in adulthood that can be revealed if vision is lost either temporarily or permanently. This has important implications for understanding the role of early visual experience in shaping visual cognitive development.     

Statistical analysis of binary relational data: Parameter estimation

Binary interaction data, measuring the presence or absence of a relation between pairs of actors in a “dyadic interaction situation,” are commonly gathered to study the social structure of the group of actors. Recent developments have made the statistical analysis of such data statistically easier and more substantively sophisticated. These developments allow researchers to simultaneously study several sociometric structural properties, such as reciprocity, differential popularity, and equivalence of actors. Building on this research, we review the stochastic models responsible for this breakthrough, and discuss methods for estimating expected values and model parameters. Throughout, we also highlight recent advances designed to incorporate nodal or actor attribute data into the relational data analysis. We conclude with an example illustrating these ideas based on conversational activities among actors in a group of eight people.     

Depicting the Missing: Prospective and Retrospective Person Memory for Age Progressed Images

One approach that has been used to help recover missing children is forensic age progression. In forensic age progression, outdated photographs of missing children are aged to provide an estimate of the current appearance of the child. We examined the effectiveness of age progressed images in the context of both prospective person memory and retrospective person memory. Memory for outdated and age progressed images did not significantly differ. Moreover, there was some indication that age progressed images were associated with a more conservative response bias. The results failed to demonstrate an advantage for age progressed pictures. Copyright © 2011 John Wiley & Sons, Ltd.     

The confluence model: Parameter estimation for six divergent data sets on family factors and intelligence

Theoretical predictions, based on the confluence model, were made for data from six national surveys of intellectual performance. All six surveys relate intellectual performance scores to family configuration variables. Widely divergent patterns of relationships between the major family factors, such as birth order and family size, and intellectual performance scores characterize the six data sets. Nevertheless, the confluence model was capable of accurate prediction in all cases when all three parameters were estimated. Little accuracy was lost using only two parameters. Variations in the estimated parameter values that reflected the variations in patterns of effects could be meaningfully interpreted for their psychological significance.     

Low-cost equipment for schedule generation III: Variable ratio and ancillary equipment

Circuits are described that extend earlier designs for schedule-generating equipment using complementary metal oxide semiconductor (CMOS) logic. Specifically, the circuits consist of a variable-ratio generator and a series of circuits providing ancillary equipment, including a simple elapsed-timer meter, a logic probe, a variety of logic functions, a pulse generator, and circuits for interfacing. All circuits are compatible with those previously described.     

Magnitude estimation of conceptual data dimensions for use in sonification

Sonifications must match listener expectancies about representing data with sound. Three experiments showed the utility of magnitude estimation for this. In Experiment 1, 67 undergraduates judged the sizes of visual stimuli and the temperature, pressure, velocity, size, or dollars they represented. Similarly, in Experiment 2, 132 listeners judged the pitch or tempo of sounds and the data they represented. In both experiments, polarity and scaling preference depended on the conceptual data dimension. In Experiment 3, 60 listeners matched auditory graphs to data created with the results of Experiment 2, providing initial validation of scaling slopes. Magnitude estimation is proposed as a design tool in the development of data sonifications, with the level of polarity preference agreement predicting mapping effectiveness.     

Between the analytical and the critical: implications for theorizing the self

This article considers some of the implications of the critical standpoint in 'general' psychology in view of Jungian assumptions about the development of the self. The first part introduces the critical standpoint, which encompasses a spectrum of postmodern psychologies sharing a critique of the discipline's 'mainstream' positivist approach, an interest in human lives as existing in culture and historical time, and sensitivity to the dialogical unfolding of the self. Social constructionism and specifically the theory of positioning, which posits the self as a discursive production, are described. The second part illustrates the contrast between the social constructionist and Jungian viewpoints with two possible interpretations of a four-year-old boy's fantasies. Although these viewpoints are diametrically opposed, the possibility of a conceptual 'middle ground' between them is indicated here with particular reference to Bakhtin's understanding of the dialogical act.