Autocorrelation problems in short time series

The work of Huitema (1985) on autocorrelation in behavioral data suggests that the use of conventional statistical methods is justified. The present study restates the problem of autocorrelation by analyzing 100 baselines of small samples designs published in the Journal of Applied Behavior Analysis during 1992. The results show a negative bias in the autocorrelations, especially with very small samples. The autocorrelation values are normally distributed, and the method of Davies, Trigg, and Newbold (1977) is the most accurate in calculating the standard deviation.     

Loneliness and solitude in adolescence: A confirmatory factor analysis of alternative models

The present study tested a four-factor model of adolescent loneliness and solitude that comprises peer-related loneliness, family loneliness, negative attitude toward solitude, and positive attitude toward solitude. Nine different instruments for a total of 14 scales and derivative subscales were completed by a sample of mid-adolescents (N = 534) from Grades 10 through 12 (aged 15–18 years) in the Dutch-speaking part of Belgium. As expected, the four-factor solution provided a better fit to the data than did alternative models that comprised just a single factor, or two and three factors. Use of the Loneliness and Aloneness Scale for Children and Adolescents (LACA) is recommended, because the instrument measures all four aspects of the model. Implications for current theories on adolescent loneliness and associated phenomena, such as adolescents’ attitude toward being on their own, are briefly discussed.     

Loneliness and solitude in adolescence: A confirmatory factor analysis of alternative models

The present study tested a four-factor model of adolescent loneliness and solitude that comprises peer-related loneliness, family loneliness, negative attitude toward solitude, and positive attitude toward solitude. Nine different instruments for a total of 14 scales and derivative subscales were completed by a sample of mid-adolescents (N = 534) from Grades 10 through 12 (aged 15–18 years) in the Dutch-speaking part of Belgium. As expected, the four-factor solution provided a better fit to the data than did alternative models that comprised just a single factor, or two and three factors. Use of the Loneliness and Aloneness Scale for Children and Adolescents (LACA) is recommended, because the instrument measures all four aspects of the model. Implications for current theories on adolescent loneliness and associated phenomena, such as adolescents’ attitude toward being on their own, are briefly discussed.     

Comment on fitting MA time series by structural equation models

In a recent paper by van Buuren (1997) it is concluded that parameter estimates in pure moving-average (MA) models, obtained by software for fitting structural equation models (SEMs), are biased and inefficient. In this comment it is shown that this negative finding may be due to a particular feature of van Buuren's simulation experiment. A modified procedure for fitting MA models by means of SEM software is proposed, and some of its implications are discussed.     

Assessing alternative models of individualism and collectivism: a confirmatory factor analysis

Six alternative structural models of individualism–collectivism are reviewed and empirically compared in a confirmatory factor analysis of questionnaire data from an Australian student sample (N = 340). Central to the debate about the structure of this broad social attitude are the issues of (1) polarity (are individualism and collectivism bipolar opposites, or orthogonal factors?) and (2) dimensionality (are individualism and collectivism themselves higher‐order constructs subsuming several more specific factors and, if so, what are they?). The data from this Australian sample support a model that represents individualism and collectivism as a higher‐order bipolar factor hierarchically subsuming several bipolar reference‐group‐specific individualisms and collectivisms. Copyright © 2001 John Wiley & Sons, Ltd.     

Dynamic factor analysis of nonstationary multivariate time series

A dynamic factor model is proposed for the analysis of multivariate nonstationary time series in the time domain. The nonstationarity in the series is represented by a linear time dependent mean function. This mild form of nonstationarity is often relevant in analyzing socio-economic time series met in practice. Through the use of an extended version of Molenaar's stationary dynamic factor analysis method, the effect of nonstationarity on the latent factor series is incorporated in the dynamic nonstationary factor model (DNFM). It is shown that the estimation of the unknown parameters in this model can be easily carried out by reformulating the DNFM as a covariance structure model and adopting the ML algorithm proposed by Jöreskog. Furthermore, an empirical example is given to demonstrate the usefulness of the proposed DNFM and the analysis.     

Visual analysis of single-case time series: Effects of variability, serial dependence, and magnitude of intervention effects

Visual analysis is the dominant method of analysis for single-case time series. The literature assumes that visual analysts will be conservative judges. We show that previous research into visual analysis has not adequately examined false alarm and miss rates or the effect of serial dependence. In order to measure false alarm and miss rates while varying serial dependence, amount of random variability, and effect size, 37 students undertaking a postgraduate course in single-case design and analysis were required to assess the presence of an intervention effect in each of 27 AB charts constructed using a first-order autoregressive model. Three levels of effect size and three levels of variability, representative of values found in published charts, were combined with autocorrelation coefficients of 0, 0.3 and 0.6 in a factorial design. False alarm rates were surprisingly high (16% to 84%). Positive autocorrelation and increased random variation both significantly increased the false alarm rates and interacted in a nonlinear fashion. Miss rates were relatively low (0% to 22%) and were not significantly affected by the design parameters. Thus, visual analysts were not conservative, and serial dependence did influence judgment.     

A cognitive multivariate time series and its analysis

The method of analyzing a multivariate time series, by factoring a matrix polynomial of linear transfer function coefficients, is briefly outlined and shown to be potentially applicable to cognitive experiments where the inputs are numerically predictable and the outputs suitably quantified. An experiment on sequences of 90 figures, judged in terms of similarity and of the probability of exceeding or not exceeding some size, was analyzed individually for 12 subjects and their dynamic characteristics expressed in pole zero diagrams. Some dynamic components neglected by classical time-independent psychological methods are identified.     

The use of factor analysis in the statistical analysis of multiple time series

Factor analysis has been proposed and used as a method of statistical analysis of several measurements made on one individual repeatedly over time. This paper discusses some difficulties in applying factor analysis to multiple time series and attempts to indicate to what extent such methods can accomplish the goals of time series analysis. Some other methods are suggested.Research sponsored by contract AF41(657)-214 between the USAF School of Aerospace Medicine and Teachers College, Columbia University.     

Crisis intervention and the prevention of institutionalization: An interrupted time series analysis

In the context of the community mental health movement in Illinois, the evolution and development of a crisis intervention program aimed at avoiding state hospitalization and bringing more appropriate and efficacious resources to bear on the difficulties of the individual and/or family is described. This intervention program is characterized by a more active-seeking style of delivering mental health services. With the novel application of interrupted time series analysis to both the targeted and matched nonequivalent control communities the efficacy of this more active intervention in reducing the number of state hospital administrations diagnosed as "mentally ill" is highlighted. The cost/benefit to the taxpayer is also discussed.     

Temporal control in rats: analysis of nonlocalized effects from short interfood intervals.

The present experiment analyzed temporal control of postreinforcement pause duration during within-session changes in the criterion for reinforcement (interfood interval, IFI). Analysis of interval-by-interval changes in the pause revealed localized and nonlocalized effects from short intervals that caused specific changes in performance. In Phase 1, rats were presented with five consecutive 15-s IFIs intercalated into a series of 60-s IFIs. The 15-s set decreased the pause in adjacent and more remote 60-s intervals. In Phase 2, two sets of 15-s intervals were intercalated. The spacing between the two sets varied so that 0, 5, 10, or 15 60-s IFIs separated the sets. The postreinforcement pause tracked all changes in the IFI duration, and the localized effect from a short set extended beyond the next interval to the next few 60-s IFIs. Effects from one set, however, did not combine with a second set: Changes in the pause after two sets were the same regardless of the spacing between sets.     

Sequential effects in two-choice reaction time: Subjective expectancy and automatic after- effect at short response-stimulus intervals

Between three serial two-choice reaction-time tasks, the response-stimulus interval (RSI), stimulus-response compatibility, and practice were varied in order to examine two determinants of sequential effects — subjective expectancy and automatic aftereffect. It appears that subjective expectancy is absent when the RSI is below a critical minimum. In an incompatible task, however, this minimum is greater. This is interpreted in support of the single-channel hypothesis: the subject only builds up expectancies when the “central processor” is unoccupied. The automatic aftereffect increases as the RSI decreases. The decay of the aftereffect seems to take place mainly during the RSI and only to a minor degree during the reaction process. Normally, a strong aftereffect operates in a general way, but after extensive practice it becomes stimulus specific. A model is presented, which assumes that only at the initial stages of practice is the unused “neural pathway” inhibited each time the stimulus is an alternation.