A Prospective Study of Maternal Exposure to Smoking during Pregnancy and Behavioral Development in the Child

Prenatal smoke exposure (PSE) is a risk factor for adverse outcomes in the offspring, including those affecting psychological development. However, it is uncertain whether these associations are the direct result of PSE or other confounding factors. The aim of this study was to examine the possible relationship between PSE and behavioral development in children at 7.5 years of age, considering several prenatal, neonatal and postnatal covariates. A cohort of 266 mother-child pairs was followed from the first trimester of pregnancy until the children reached 7.5 years of age. PSE was assessed using a questionnaire from prenatal clinical records and corroborated by plasma cotinine determinations in the first and second trimesters and in the cord. Mother-child pairs were classified into one of four groups: unexposed, exposed to passive smoking, first trimester active smoking only and active smoking throughout pregnancy. Child behavior was assessed using the Child Behavior Checklist for ages 6–18 and the Childhood Autism Spectrum Test. In multiple linear regression models, smoking during pregnancy was associated with higher scores in affective problems (β?=?0.298; p?=?0.004). No significant associations were found between smoking during pregnancy and externalizing problems. Findings indicate that PSE is negatively associated with behavioral development in childhood.

     

FACTOR: A computer program to fit the exploratory factor analysis model

Exploratory factor analysis (EFA) is one of the most widely used statistical procedures in psychological research. It is a classic technique, but statistical research into EFA is still quite active, and various new developments and methods have been presented in recent years. The authors of the most popular statistical packages, however, do not seem very interested in incorporating these new advances. We present the program FACTOR, which was designed as a general, user-friendly program for computing EFA. It implements traditional procedures and indices and incorporates the benefits of some more recent developments. Two of the traditional procedures implemented are polychoric correlations and parallel analysis, the latter of which is considered to be one of the best methods for determining the number of factors or components to be retained. Good examples of the most recent developments implemented in our program are (1) minimum rank factor analysis, which is the only factor method that allows one to compute the proportion of variance explained by each factor, and (2) the simplimax rotation method, which has proved to be the most powerful rotation method available. Of these methods, only polychoric correlations are available in some commercial programs. A copy of the software, a demo, and a short manual can be obtained free of charge from the first author.     

NFACOM: A new program for relating solutions in exploratory factor analysis

A new version of the program FACOM (comparison of exploratory factor analysis solutions) is presented. The new version (NFACOM) greatly extends the possibilities of the previous versions. It is now possible to use congruence as well as least squares comparisons in both orthogonal and oblique situations. What is more, n solutions, not just two, can be compared. Recently developed algorithms have been implemented; others, which have proved not to be theoretically well founded, have been omitted.     

A General Approach for Fitting Pure Exploratory Bifactor Models

This article proposes a procedure for fitting a pure exploratory bifactor solution in which the general factor is orthogonal to the group factors, but the loadings on the group factors can satisfy any orthogonal or oblique rotation criterion. The proposal combines orthogonal Procrustes rotations with analytical rotations and consists of a sequence of four steps. The basic input is a semispecified target matrix that can be (a) defined by the user, (b) obtained by using Schmid-Leiman orthogonalization, or (c) automatically built from a conventional unrestricted solution based on a prescribed number of factors. The relevance of the proposal and its advantages over existing procedures is discussed and assessed via simulation. Its feasibility in practice is illustrated with two empirical examples in the personality domain.     

A Pearson-Type-VII item response model for assessing person fluctuation

Using Lumsden’s Thurstonian fluctuation model as a starting point, this paper attempts to develop a unidimensional item response theory model intended for binary personality items. Under some additional assumptions, a new model is obtained in which the item characteristic curves are defined by a cumulative Pearson-Type-VII distribution, and the person response curves are two-parameter normal ogives. Procedures for fitting the new model are proposed. Furthermore, the relations between individual fluctuation and scalability are discussed, and a scalability index based on the new model is proposed. All the developments in this paper are illustrated using two empirical examples.     

Item characteristics that predict response latency in personality measures

This study assesses some item characteristics that are expected to affect response latencies in the computerized administration of a personality questionnaire. First, some previous empirical results are discussed, and a theoretical framework from which predictions can be made is proposed. Second, the predictions are empirically assessed using two data-sets, one of them based on binary items, and the other one based on graded-response items. Some of the results obtained are new, whereas the remaining agree with previous empirical results. The implications of the present results in applied research, and particularly for the use of latencies, is discussed.     

Psychometric properties of Spanish version of the Mini-Mental Adjustment to Cancer Scale

Background/ObjectiveThe aim of the study was to examine the factor structure and psychometric properties of the Spanish version of the Mini-Mental Adjustment to Cancer Scale (Mini-MAC) in a large sample of patients with non-metastatic, resected cancer.MethodsProspective, observational, multicenter study for which 914 patients were recruited from 15 Spanish hospitals. Exploratory and confirmatory factor analyses, validity and reliability analyses were conducted.ResultsFactor-analytic results indicated a 4-factor structure of the Spanish version of the Mini-MAC. Three subscales have psychometric properties similar to those of Helplessness, Anxious preoccupation, and Cognitive avoidance of the original the Mini-MAC. The Fighting spirit and the Fatalism subscales were combined on the Positive attitude scale. The four factor-derived scale scores exhibited acceptable accuracy for individual measurement purposes, as well as stability over time in test-retest assessments at 6 months. Validity assessments found meaningful relations between the derived scale scores, and Brief Symptom Inventory depression and anxiety scores and Functional Assessment of Chronic Illness Therapy spiritual well-being scores.ConclusionsThe Spanish version of the Mini-MAC provides reliable and valid measures for patients with non-metastatic, resected cancer, and results corroborate the instrument’s cross-cultural validity.     

Factor-analytic Procedures for Assessing Response Pattern Scalability

This paper proposes procedures for assessing the fit of a psychometric model at the level of the individual respondent. The procedures are intended for personality measures made up of Likert-type items, which, in applied research, are usually analyzed by means of factor analysis. Two scalability indices are proposed, which can be considered as factor-analytic counterparts of the lo and lz IRT-based person-fit indices. The present indices can be derived both as likelihood-based measures and as residual measures, and this makes it possible to relate them with previous methodologies such as Bollen's residual analysis and Lanning's scalability index. The indices are intended to be used mainly as exploratory devices, and a factor-analytic version of the person response curve is also proposed as a tool for obtaining further information about the possible causes of misfit. The behavior of the procedures is assessed by means of simulation studies. Finally, an empirical example in personality measurement shows how the procedures can be used in applied research.     

A Factor-Analytic Model for Assessing Individual Differences in Response Scale Usage

This article proposes a factor-analytic model, intended for graded-response or continuous-response personality and attitude items, which includes an additional multiplicative person parameter that models the individual's response mapping process. The model, which is a modification of Spearman's (1904) factor analysis (FA) model, is parameterized as both an FA model and an item response theory (IRT) model and is fully developed to the extent that it can be used in applications. Procedures for (a) calibrating the items and assessing data fit, (b) obtaining individual estimates of both person parameters, (c) determining measurement precision, and (d) assessing differential predictability are proposed and discussed. The potential advantages of the proposal, its practical relevance, and its relations with other approaches are also discussed. Its functioning is assessed with a simulation study and 3 empirical examples in the personality domain.