发展心理学研究中个体定向的理论与方法

长期以来, 发展心理学研究主要采取变量定向的方法。近年来, 随着发展心理学理论的发展和人类发展个体差异的科学事实的积累, 个体定向的方法得以发展并且成为发展心理学研究的主要方法思路之一。个体定向的方法关注作为整体的个体, 旨在确定有意义的同质性个体类别或亚组。个体定向的方法包括一系列的基本理论原则, 具体数据分析方法包括传统聚类分析、基于模型的聚类方法以及配置频次分析等。发展心理学研究需综合运用个体定向的方法与变量定向的方法。     

What Carries a Mediation Process? Configural Analysis of Mediation

Mediation is a process that links a predictor and a criterion via a mediator variable. Mediation can be full or partial. This well-established definition operates at the level of variables even if they are categorical. In this article, two new approaches to the analysis of mediation are proposed. Both of these approaches focus on the analysis of categorical variables. The first involves mediation analysis at the level of configurations instead of variables. Thus, mediation can be incorporated into the arsenal of methods of analysis for person-oriented research. Second, it is proposed that Configural Frequency Analysis (CFA) can be used for both exploration and confirmation of mediation relationships among categorical variables. The implications of using CFA are first that mediation hypotheses can be tested at the level of individual configurations instead of variables. Second, this approach leaves the door open for different types of mediation processes to exist within the same set. Using a data example, it is illustrated that aggregate-level analysis can overlook mediation processes that operate at the level of individual configurations.     

Personality patterns of physicians in person-oriented and technique-oriented specialties

This study investigated differences in personality patterns between person-oriented and technique-oriented physicians. It tested an integrative framework by converting the scores on the Personality Research Form (PRF) to the Big-Five factors and built a predictive model of group membership in clinical specialty area. PRF scores from 238 physicians (153 person-oriented and 85 technique-oriented) were used for this retrospective study. Significant personality differences between group means existed for seven of the traits on the PRF and two Big-Five factors. Results of a stepwise discriminant analysis indicated that two PRF traits and one Big-Five factor had the greatest ability to discriminate between person-oriented and technique-oriented physicians. Findings of this study provide support for establishing personality profiles of physicians interested in person-oriented versus technique-oriented specialties. Examining personality differences among male and female physicians is discussed as an area for future research.     

Longitudinal qualitative research and interpretative phenomenological analysis: philosophical connections and practical considerations

Qualitative longitudinal research designs are commonly considered a sensible orientation to exploring concepts of time and change. However, as a stand-alone method, qualitative longitudinal research has been criticized for its lack of philosophical and theoretical foundation (Hermanowicz 2013). This article explores the key tenets of qualitative longitudinal research and aligns with the notion that qualitative longitudinal designs be fused with other qualitative methods of data collection and analysis to enhance experimental trustworthiness (Calman, Brunton & Molassiotis 2013). Specifically, this article posits that a longitudinal design aligns well with the epistemological and ontological foundations of the interpretive phenomenological analysis (IPA) method to highlight longitudinal IPA as an emerging and distinctive qualitative methodology (Smith, Flowers & Larkin 2009). Practical considerations for conducting longitudinal IPA are also provided.     

The use of configural frequency analysis for explorative data analysis

Configural frequency analysis (CFA) is a widely used method of explorative data analysis. It tries to detect patterns in the data that occur significantly more or significantly less often than expected by chance. Patterns which occur more often than expected by chance are called CFA types, while those which occur less often than expected by chance are called CFA antitypes. The patterns detected are used to generate knowledge about the mechanisms underlying the data. We investigate the ability of CFA to detect adequate types and antitypes in a number of simulation studies. The basic idea of these studies is to predefine sets of types and antitypes and a mechanism which uses them to create a simulated data set. This simulated data set is then analysed with CFA and the detected types and antitypes are compared to the predefined ones. The predefined types and antitypes together with the method to generate the data are called a data generation model. The results of the simulation studies show that CFA can be used in quite different research contexts to detect structural dependencies in observed data. In addition, we can learn from these simulation studies how much data is necessary to enable CFA to reconstruct the predefined types and antitypes with sufficient accuracy. For one of the data generation models investigated, implicitly underlying knowledge space theory, it was shown that zero‐order CFA can be used to reconstruct the predefined types (which can be interpreted in this context as knowledge states) with sufficient accuracy. Theoretical considerations show that first‐order CFA cannot be used for this data generation model. Thus, it is wrong to consider first‐order CFA, as is done in many publications, as the standard or even only method of CFA.     

Mediation and Causality at the Individual Level

Within a person-oriented research paradigm the focus is on individuals characterized by patterns of information that are regarded as indivisible wholes. It is then not sufficient to carry out standard variable-oriented mediation analysis. The procedure suggested by von Eye, Mun, and Mair (2009) for pattern-oriented mediation analysis is much better aligned to this person-oriented framework. An important new feature in their approach is that it can detect mediator configurations that prohibit predictor and outcome connections at a pattern level. Two extensions of their procedure are suggested, namely (1) the use of cluster analysis to arrive at the categories and (2) the use of other models for estimating the expected frequencies. It is pointed out that in their context a functional relations perspective might be more relevant than the standard causality perspective.

Combining Methodologies in the Construct Validation Process: An Empirical Illustration

Confirmatory Factor Analysis (CFA) has received considerable support as a methodology for assessing construct validity. As with other methodologies, however, numerous problems can be encountered when using CFA to assess construct validity. Given the limitations of the CFA, Multitrait-Multimethod (MTMM), and analysis of variance (ANOVA) methodologies, a set of guidelines was developed. The guidelines recommend that the characteristics of the data set be used to determine how the three alternative construct validation methodologies can be used in combination. Examples from the research literature are discussed in order to demonstrate the application of these guidelines.     

Factor structure of the construct of adaptive behavior in children with and without intellectual disability

Although the presence of significant limitations in adaptive behavior constitutes one of the three necessary criteria for diagnosing intellectual disability, adaptive behavior structure has always been the subject of considerable controversy among researchers. The main goal of this study is to extend previous research results that provide further support to a multidimensional structure of conceptual, social, and practical skills compared to the unidimensional structure. One-factor and 3-correlated factors models as measured by 15 observable indicators were analyzed by means of confirmatory factor analysis (CFA), as well as their relationships with one second-order factor (i.e., adaptive behavior). To that end, 388 children with and without intellectual disabilities were assessed with the Diagnostic Adaptive Behavior Scale (DABS). Results of CFA indicated that the 3 first-order factors solution provides the best fit to the data. Reliability and validity of the multidimensional model were also analyzed through different methods such as the composite reliability and the average variance extracted. Finally, implications of these findings and possible directions for future research are discussed.     

Structural equation modeling: reviewing the basics and moving forward

This tutorial begins with an overview of structural equation modeling (SEM) that includes the purpose and goals of the statistical analysis as well as terminology unique to this technique. I will focus on confirmatory factor analysis (CFA), a special type of SEM. After a general introduction, CFA is differentiated from exploratory factor analysis (EFA), and the advantages of CFA techniques are discussed. Following a brief overview, the process of modeling will be discussed and illustrated with an example using data from a HIV risk behavior evaluation of homeless adults (Stein & Nyamathi, 2000). Techniques for analysis of nonnormally distributed data as well as strategies for model modification are shown. The empirical example examines the structure of drug and alcohol use problem scales. Although these scales are not specific personality constructs, the concepts illustrated in this article directly correspond to those found when analyzing personality scales and inventories. Computer program syntax and output for the empirical example from a popular SEM program (EQS 6.1; Bentler, 2001) are included.     

Revisiting the Empirical Distinction Between Hedonic and Eudaimonic Aspects of Well-Being Using Exploratory Structural Equation Modeling

The distinction between hedonic (i.e., subjective well-being) and eudaimonic (i.e., psycho-social functioning) components of well-being is questioned by some researchers on the grounds that these two aspects of well-being are highly correlated. However, I argue that previous research has relied on confirmatory factor analysis (CFA), which is likely to overestimate interfactor correlations, because cross-loadings are constrained to be zero in CFA. In contrast, the new method of exploratory structural equation modeling (ESEM) does not constrain cross-ladings to zero, which results in more accurate factor intercorrelations. The present study used ESEM to reinvestigate the relationship between hedonic and eudaimonic aspects of well-being in a sample of 3986 American adults. The results showed that the ESEM model fitted the data better than the CFA model. As expected, interfactor correlations obtained with ESEM were substantially smaller than those obtained with CFA, indicating greater factor distinctiveness. These results suggest that hedonic and eudaimonic factors are correlated yet largely independent from each other. The results also suggest that ESEM is a more appropriate method than CFA in the study of multi-dimensional constructs, such as mental well-being.     

Assessment of Psychopathic Traits in Singaporean Adolescents: Validation of the Antisocial Process Screening Device (APSD)

There is little knowledge available concerning psychopathic traits in Asian adolescents; a lack of a suitable measurement instrument for assessing psychopathy in Asian societies may account for this. This study aimed to validate a widely used scale in the West — the Antisocial Process Screening Device (APSD) — in Singaporean school-based and at-risk adolescents. Using an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA), this study examined the two-factor (i.e., grandiose-manipulative/impulsive traits and callous-unemotional traits) and three-factor (i.e., grandiose-manipulative traits, impulsivity, and callous-unemotional traits) models of the APSD in 1027 school-based and 113 at-risk adolescents. School samples are adolescents from three secondary schools, while at-risk samples are adolescents who manifest different types of delinquent behaviors and are either placed in more structured settings or need closer supervision although they have not violated the law. Gender invariance was further tested in the school-based sample by conducting a multigroup CFA. The convergent validity of the APSD was also investigated in the school-based sample. For the school-based adolescents, the APSD revealed that the three-factor model provided a superior fit over the two-factor model and the factorial invariance across gender. Significant relationships between the three dimensions of the APSD and aggression and delinquency support the convergent validity of the APSD. As for the at-risk adolescents, both the two- and three-factor models were acceptable, but the two-factor model was preferred as it was parsimonious and it aligned with the conceptualized characteristics of psychopathic traits. Findings suggest that the APSD is a reliable and sound instrument for measuring psychopathic traits in Asian school-based and at-risk adolescents.     

A practical guide to multilevel modeling

Collecting data from students within classrooms or schools, and collecting data from students on multiple occasions over time, are two common sampling methods used in educational research that often require multilevel modeling (MLM) data analysis techniques to avoid Type-1 errors. The purpose of this article is to clarify the seven major steps involved in a multilevel analysis: (1) clarifying the research question, (2) choosing the appropriate parameter estimator, (3) assessing the need for MLM, (4) building the level-1 model, (5) building the level-2 model, (6) multilevel effect size reporting, and (7) likelihood ratio model testing. The seven steps are illustrated with both a cross-sectional and a longitudinal MLM example from the National Educational Longitudinal Study (NELS) dataset. The goal of this article is to assist applied researchers in conducting and interpreting multilevel analyses and to offer recommendations to guide the reporting of MLM analysis results.     

Mixture Modeling Methods for the Assessment of Normal and Abnormal Personality,Part I: Cross-Sectional Models

Over the past 75 years, the study of personality and personality disorders has been informed considerably by an impressive array of psychometric instruments. Many of these tests draw on the perspective that personality features can be conceptualized in terms of latent traits that vary dimensionally across the population. A purely trait-oriented approach to personality, however, might overlook heterogeneity that is related to similarities among subgroups of people. This article describes how factor mixture modeling (FMM), which incorporates both categories and dimensions, can be used to represent person-oriented and trait-oriented variability in the latent structure of personality. We provide an overview of different forms of FMM that vary in the degree to which they emphasize trait- versus person-oriented variability. We also provide practical guidelines for applying FMM to personality data, and we illustrate model fitting and interpretation using an empirical analysis of general personality dysfunction.     

Reducing bias in psychometric assessment of culturally and linguistically diverse students from refugee backgrounds in Australian schools: A process approach

The present article examines a process developed within Brisbane Catholic Education to reduce bias in assessment of students from refugee backgrounds when there is a concern about a possible intellectual disability. Several factors impacting upon assessment are described including previous trauma, language, lack of previous schooling and acculturation difficulties. A proactive process (Support Process for the Assistance and Assessment of Students from Refugee Backgrounds) is described involving broad‐based, longitudinal collection of data by the school psychologist as part of a support team. Factors to consider in conducting standardised psychometric assessments are discussed. Strengths and limitations of this process as well as the need for future research into its effectiveness are summarised.     

14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants

The use of electroencephalography (EEG) to study infant brain development is a growing trend. In addition to classical longitudinal designs that study the development of neural, cognitive and behavioural functions, new areas of EEG application are emerging, such as novel social neuroscience paradigms using dual infant-adult EEG recordings. However, most of the experimental designs, analysis methods, as well as EEG hardware were originally developed for single-person adult research. When applied to study infant development, adult-based solutions often pose unique problems that may go unrecognised. Here, we identify 14 challenges that infant EEG researchers may encounter when designing new experiments, collecting data, and conducting data analysis. Challenges related to the experimental design are: (1) small sample size and data attrition, and (2) varying arousal in younger infants. Challenges related to data acquisition are: (3) determining the optimal location for reference and ground electrodes, (4) control of impedance when testing with the high-density sponge electrode nets, (5) poor fit of standard EEG caps to the varying infant head shapes, and (6) ensuring a high degree of temporal synchronisation between amplifiers and recording devices during dual-EEG acquisition. Challenges related to the analysis of longitudinal and social neuroscience datasets are: (7) developmental changes in head anatomy, (8) prevalence and diversity of infant myogenic artefacts, (9) a lack of stereotypical topography of eye movements needed for the ICA-based data cleaning, (10) and relatively high inter-individual variability of EEG responses in younger cohorts. Additional challenges for the analysis of dual EEG data are: (11) developmental shifts in canonical EEG rhythms and difficulties in differentiating true inter-personal synchrony from spurious synchrony due to (12) common intrinsic properties of the signal and (13) shared external perturbation. Finally, (14) there is a lack of test-retest reliability studies of infant EEG. We describe each of these challenges and suggest possible solutions. While we focus specifically on the social neuroscience and longitudinal research, many of the issues we raise are relevant for all fields of infant EEG research.     

The effect of response style bias on the measurement of transformational,transactional, and laissez-faire leadership

Transformational, transactional, and laissez-faire leadership are considered to be three distinct leadership styles. In this research we argue that response style behaviour in the form of acquiescence and extreme response style can distort the measurement of these dimensions of leadership. Using a sample of 864 employees selected from 135 work teams, this research demonstrates that (1) response styles affect measurement; (2) divergent validity of the three dimensions increases when response styles are taken into account; (3) gender is spuriously related to leadership upon response styles; and (4) team ratings substantially change when controlling for response styles bias. As a secondary topic of this research, we elaborate on a relatively new approach in diagnosing response styles, i.e., a confirmatory latent-class factor analysis. We explain the advantages of this approach and illustrate the steps a researcher has to take in conducting this type of analysis.     

Offender interviewing in custodial settings: Understanding validity,value and veracity issues

The purpose of this scoping literature review was to identify trends regarding validity, value and veracity issues when conducting (qualitative) interviews with offenders in custodial settings. We selected for review works from electronic databases in SAGE, Springer, and Taylor and Francis by using e-journals, Google Scholar and the behavioural science database, PsycINFO. The inclusion criteria for our studies were based on whether sources addressed offender interviewing as a wellspring of information, but excluded those that focused solely on the acquisition of clinical information (at the expense of focusing on validity, value and veracity issues). We utilised the content analysis approach to identify themes/ patterns in the research findings. Key findings suggest that offenders with impression management motivations deceive and manipulate in their research interview presentations. When gathering data from inmates in custodial care settings, best practice includes cross-checking offender interview data against other sources of evidence and taking into account the context in which they are serving their sentences.     

Morphological decomposition and the reverse base frequency effect

If recognition of a polymorphemic word always takes place via its decomposition into stem and affix, then the higher the frequency of its stem (i.e., base frequency) the easier the lexical decision response should be when frequency of the word itself (i.e., surface frequency) is controlled. Past experiments have demonstrated such a base frequency effect, but not under all circumstances. Thus, a dual pathway notion has become dominant as an account of morphological processing whereby both decomposition and whole-word access is possible. Two experiments are reported here that demonstrate how an obligatory decomposition account can handle the absence of base frequency effects. In particular, it is shown that the later stage of recombining the stem and affix is harder for high base frequency words than for lower base frequency words when matched on surface frequency, and that this can counterbalance the advantage of easier access to the higher frequency stem. When the combination stage is crucial for discriminating the word items from the nonword items, a reverse base frequency effect emerges, revealing the disadvantage at this stage for high base frequency words. Such an effect is hard for the dual-pathway account to explain, but follows naturally from the idea of obligatory decomposition.