Affective asynchrony and the measurement of the affective attitude component

Theoretical models of social phobia propose that biased attention contributes to the maintenance of symptoms; however these theoretical models make opposing predictions. Specifically, whereas Rapee and Heimberg (1997) suggested the biases are characterised by hypervigilance to threat cues and difficulty disengaging attention from threat, Clark and Wells (1995) suggested that threat cues are largely avoided. Previous research has been limited by the almost exclusive reliance on behavioural response times to experimental tasks to provide an index of attentional biases. The current study evaluated the relationship between the time-course of attention and symptoms of social anxiety and depression. Forty-two young adults completed a dot-probe task with emotional faces while eye-movement data were collected. The results revealed that increased social anxiety was associated with attention to emotional (rather than neutral) faces over time as well as difficulty disengaging attention from angry expressions; some evidence was found for a relationship between heightened depressive symptoms and increased attention to fear faces.     

An analysis of recollection in the measurement of long‐term change in attitudes

In cross‐sectional research designs, the measurement of attitudinal and behavioural change has often relied upon respondents' reported recollection of the degree of change from one point in time to another. Alternatively, algebraic approaches for measuring change are often utilized in longitudinal designs with panel data. In a conceptual replication of prior work by Irving and Meyer (1995), this study undertook an examination of direct recall measures of change, as well as contemporaneous Time 1 (T1) and Time 2 (T2) measures of three attitudinal constructs (union commitment, union satisfaction, and union attitudes in general) over a 10‐year period. Based upon a sample of 195 union members, the investigation found that more proximal T2 measures of the constructs of interest were strongly related to single‐item measures of recollection of change and more distal T1 measures had no significant relationship with respondents' reported recollection of any of the attitudinal measures examined. In the interpretation of these findings, it is suggested that current attitudes provide cognitive prompts to survey‐based requests for recollection of past attitudes, which may in‐turn lead to serious reporting inaccuracies associated with many direct measures of recollection.     

Multilevel component analysis

A general framework for the exploratory component analysis of multilevel data (MLCA) is proposed. In this framework, a separate component model is specified for each group of objects at a certain level. The similarities between the groups of objects at a given level can be expressed by imposing constraints on component models of the groups using the approach adopted in simultaneous component analysis. The constraints used are based on the loading matrices and on the covariances of the component scores of each group. MLCA is related to three‐way component analysis and to currently available multilevel structural equation models. It is shown that the latter are less flexible than MLCA. The use of MLCA is illustrated by means of an empirical example.     

Item response theory and the measurement of clinical change

An instrument's sensitivity to detect individual-level change is an important consideration for both psychometric and clinical researchers. In this article, we develop a cognitive problems measure and evaluate its sensitivity to detect change from an item response theory (IRT) perspective. After illustrating assumption checking and model fit assessment, we detail 4 features of IRT modeling: (a) the scale information curve and its relation to the bandwidth of measurement precision, (b) the scale response curve and how it is used to link the latent trait metric with the raw score metric, (c) content-based versus norm-based score referencing, and (d) the level of measurement of the latent trait scale. We conclude that IRT offers an informative, alternative framework for understanding an instrument's psychometric properties and recommend that IRT analyses be considered prior to investigations of change, growth, or the effectiveness of clinical interventions.     

Examining change in maximal reliability for multiple‐component measuring instruments

A method for examining change in maximal reliability for pre‐specified sets of congeneric measures when developing a multi‐component instrument is outlined. The approach is applicable for purposes of estimation and testing of gain or loss in the maximal reliability coefficient as a consequence of adding or dropping one or more measures from a homogeneous composite with uncorrelated errors, as well as when one is concerned with optimal component choice for highest increase or correspondingly smallest drop in maximal reliability. The method is compared with a procedure for ascertaining change in unweighted sum score reliability, and implications for instrument construction and revision are discussed. The approach is illustrated with a numerical example.     

A component analysis of dry-bed training for treatment for bedwetting

The major components of Dry-Bed Training (DBT) (Azrin et al., 1974), in addition to the urine-alarm device, were identified as; (i) the waking schedule; (ii) retention control training; and (iii) positive practice and cleanliness training. Combinations of these components yielded six treatment groups. Together with an alarm-only group and a DBT group from a previous study (Bollard and Nettelbeck, 1981) these groups were compared for effectiveness in arresting bedwetting. Bedwetting frequency was reduced slightly below levels achieved with alarm-only treatment, by the addition of either retention control training, or positive practice and cleanliness training. A more substantial decrease in bedwetting resulted from the addition of the waking schedule, although none of the effects of the single components was statistically significant. The effects of the three components were cumulative, so that the more components added to the alarm-only procedure, the better the therapeutic response. Thus, the combination of waking, positive practice and cleanliness training with the alarm and the combination of all three components with the alarm (i.e. the complete DBT programme) did result in a significant reduction in bedwetting frequency compared with the alarm-only procedure. The practical and theoretical implications of these results are discussed.     

Generalized structured component analysis

We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method avoids the principal limitation of partial least squares (i.e., the lack of a global optimization procedure) while fully retaining all the advantages of partial least squares (e.g., less restricted distributional assumptions and no improper solutions). The method is also versatile enough to capture complex relationships among variables, including higher-order components and multi-group comparisons. A straightforward estimation algorithm is developed to minimize the criterion.The work reported in this paper was supported by Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the second author. We wish to thank Richard Bagozzi for permitting us to use his organizational identification data and Wynne Chin for providing PLS-Graph 3.0.     

An analysis of the visual component in recognition memory for verbal stimuli

Forty Ss were given a continuous recognition memory test in which each word was presented twice, either in the same print or in different print on the two occasions. The results showed that (a) recognition performance was facilitated to a small but statistically significant extent in the same-print condition and that (b) Ss could reliably report first presentation print for recognized items for at least 1^1/2 rain. In a second experiment, the stimuli used were nonsense strings of. from five to seven letters instead of words. This manipulation increased the same-print advantage in recognition but reduced Ss’ ability to report first print form. The results indicate that information about the physical features of verbal stimuli is retained in a visual code that is partially’ or wholly independent of the verbal code for the same stimuli. The results are inconsistent with the conclusion that the visual code is stored only as a dependent attribute of the verbal code in memory.     

Loglinear Rasch models for the analysis of stability and change

Loglinear unidimensional and multidimensional Rasch models are considered for the analysis of repeated observations of polytomous indicators with ordered response categories. Reparameterizations and parameter restrictions are provided which facilitate specification of a variety of hypotheses about latent processes of change. Models of purely quantitative change in latent traits are proposed as well as models including structural change. A conditional likelihood ratio test is presented for the comparison of unidimensional and multiple scales Rasch models. In the context of longitudinal research, this renders possible the statistical test of homogeneity of change against subject-specific change in latent traits. Applications to two empirical data sets illustrate the use of the models.The author is greatly indebted to Ulf Böckenholt, Rolf Langeheine, and several anonymous reviewers for many helpful suggestions.     

Bayesian generalized structured component analysis

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.     

A component analysis of action events

Summary Three experiments are reported, that examine the basis of the recall superiority of subject-performed tasks (SPTs) over verbal-memory tasks (VTs), and the interitem variability for SPTs. In Experiments 1 and 2 a component analysis of SPTs is undertaken in order to explore the importance of (a) involvement of external objects, (b) multimodality, and (c) enactment, for the superior memory performance for SPTs over that for VTs. In Experiment 3 the role of retrieval support, in terms of a high degree of match between the encoding and retrieval situations, was investigated in order to find out why some SPTs are easier to recall than others. The results indicate that it is difficult to separate out a single component as the most critical one for the superiority of SPT recall over VT recall, and that a high degree of match between encoding and retrieval conditions improves SPT recall. It is suggested that, in order to explain the large differences in memorability between SPTs and VTs, it is sufficient to consider the more supportive encoding situation for SPTs, but to explain differences in recallability between various SPTs, the compatibility between encoding and test has to be taken into consideration.     

A component analysis of a parent‐conducted multi‐component treatment for food selectivity

Treatment packages including differential reinforcement of alternative (DRA) behavior and escape prevention in the form of a non‐removal of the spoon procedure have been shown to successfully increase food consumption. However, when these treatment components are introduced simultaneously, the treatment component(s) responsible for behavior change cannot be determined. The purpose of this study was to conduct a sequential component analysis of the following treatment components: Bite fading, manipulation of reinforcer magnitude, and escape prevention. For two participants, food consumption did not increase until after escape prevention was introduced. For one participant, increased food consumption was observed after the magnitude of reinforcement was increased; therefore, escape prevention was not necessary. Results were maintained at a 12‐week follow‐up for all participants. Copyright © 2010 John Wiley & Sons, Ltd.     

Multiple visualisation in data analysis: a ViSta application for principal component analysis

Multiple visualisation (MV) is a statistic graphical method barely applied in data analysis practice, even though it provides interesting features for this purpose. This paper: (1) describes the application of the MV graphical method; (2) presents a number of rules related to the design of an MV; (3) introduces a general outline for developing MVs and shows how MV may be implemented in the ViSta statistical system; (4) illustrates this strategy by means of an example of MV oriented to principal component analysis; and, finally, (5) discusses some limitations of using and developing MVs.     

Test-retest reliability of the emotional stroop task: examining the paradox of measurement change

The Emotional Stroop (ES) task (I. H. Gotlib & C. D. McCann, 1984) has been proposed as an experimental measure to assess the processing of emotion or the bias in attention of emotion-laden information. However, study results have not been consistent. To further examine its reliability for empirical research, the authors of this study administered the ES task to 33 participants on 2 separate occasions separated by 1 week. Results indicated that retest reliabilities for reaction times (RTs) derived from the 3 separate emotion conditions (manic, neutral, and depressive) across the 1 week interval were very high. However, consistent with previous research, the reliabilities were very low for the interference indices (manic and depressive). These low reliabilities reflect the very high intercorrelation between the RTs derived from the 3 conditions. The authors concluded that a better indicator of the reliability for this task is the individual RTs from each emotion condition.     

A longitudinal study of cardiac component analysis

The purpose of this study was to examine the reliability of a physiological event over several weeks. The physiology analyzed was the electrical event of the heart. Two operationally defined components of the typical heart response were examined. The technique gave very reliable data within a given day. Moreover, when the data for 20 weeks were added, the overall standard deviation was very small, but there were unpredictable physiological variations from day to day. The data suggest that comparisons between psychological tasks using physiological events as the dependent measure are best done in close temporal contiguity.