项目表述效应对人格测验信度和效标效度的影响——以核心自我评价量表(CSES)为例

项目表述效应是指由项目表述方式的差异引起的与测量内容无关的系统变异,项目表述效应模型的统计本质是一种双因子模型。本研究以核心自我评价量表(CSES)为例,探讨项目表述效应对人格测验信效度的影响。采用核心自我评价量表、生活满意度量表和积极情感消极情感量表对340名“蚁族”进行测查。结果表明,CSES在核心自我评价特质以外,还存在一个反向题项目表述效应因子;忽视项目表述效应对CSES的同质性信度和效标关联效度有重要影响：高估CSES的同质性信度,低估核心自我评价与生活满意度、积极情感的正相关,高估核心自我评价与消极情感的负相关。     

The Santa Clara Strength of Religious Faith Questionnaire: Confirmatory Factor Analysis

Within contemporary psychology there is increasing interest in the role of religion on behaviour and psychological functioning. Such interest can be attested to by the growth in the number of pertinent books and articles that have been published and also the development of new self-report measures of religiosity. The Santa Clara Strength of Religious Faith Questionnaire is one such recently developed measure. It is a brief self-report measure comprising 10 items that were designed to measure strength of religious faith regardless of religious denomination or affiliation. Although Plante and Boccaccini (1997a, 1997b) have provided some preliminary evidence for the reliability and validity of the scale, the factor structure of the scale has not yet been examined. The aim of the present paper was to confirm the factor structure of the scale. The hypothesised one-factor model was tested using confirmatory factor analytic methods. Data from a sample of 106 Northern Irish undergraduate university students were examined using confirmatory factor analytic methods. A one-factor model was tested and accepted on the basis of fit statistics, therefore supporting the hypothesised unidimensional structure of the scale. The present results provide further evidence that the Santa Clara Strength of Religious Faith Questionnaire is psychometrically sound and therefore it can be recommended for further use by researchers interested in the construct of strength of religious faith.     

Global Self-Esteem as a Correlate of Work-Related Attitudes: A Question of Dimensionality

We examined the dimensionality of one of the most frequently used measures of global self-esteem, the Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965). Although the RSE is assumed to be unidimensional, Kaplan and Pokorny (1969) provided evidence that the scale taps two independent constructs: self-derogation versus defensive assertion of self-worth, in our study a confirmatory factor analysis was conducted which supported the superiority of the Kaplan and Pokorny two-factor model over a one-factor model but indicated that their model provided a poor fit to the data. Consequently, an exploratory factor analysis was conducted which revealed two dimensions, one mostly defined by negatively worded items (self-derogation) and the second by positively worded items (self-enhancement). Although the correlations obtained were generally quite small, relations with work-related attitudes shown for the negative factor were in the opposite direction and somewhat larger than those shown for the positive factor. Thus, additional support was provided for a two-dimensional conceptualization of the scales.     

A bias-corrected exploratory and confirmatory factor analysis of right-wing authoritarianism: Support for a three-factor structure

The factor structure of right-wing authoritarianism (RWA) remains a contentious issue. Although designed to measure three underlying attitude clusters, aggression, submission and conventionalism, many items are deliberately double- or triple-barrelled, to capture the covariation of the three clusters in a unidimensional scale. Additionally, although the scale is balanced, there is an item wording direction bias in the clusters; aggression items are pro-trait, and conventionalism items are con-trait. Sub-scale structure is therefore potentially confounded with acquiescence bias. Although RWA as a unitary construct has been an effective tool for exploring prejudice, it would be useful in many cases to measure its underlying components directly. Proposed solutions to this problem include creating short-form scales as subsets of the original scale, or modifying items to simplify and un-confound the structure. We present convergent evidence of an underlying factor structure by considering one-, two- and three-factor solutions to the uncorrected scale and then using an indirect method to correct for acquiescence bias. Before and after correction, factor analysis supported a three-factor solution. Confirmatory factor analyses also support a three-factor solution compared to a one-factor solution.     

The Oxford Happiness Questionnaire: Transformation from an Ordinal to an Interval Measure Using Rasch Analysis

The scientific study of happiness requires accurate measurement of the construct that satisfies assumptions of parametric statistics and thus allows both researchers and clinicians to make reliable and valid comparisons with the relevant data sources. The 29-item Oxford Happiness Questionnaire (OHQ) is a widely-used scale for assessment of personal happiness. While its psychometric properties are acknowledged to be acceptable, it presents scores on an ordinal scale and may thus not discriminate precisely between individual happiness levels. The current study aimed to improve precision and item functioning of the OHQ by applying Rasch analysis to a sample of 281 participants. To correct disordered thresholds items were rescored in a uniform fashion. Four items displayed poor relationships with the latent trait of happiness and were removed. Best fit to the unidimensional Rasch model was achieved after locally dependent items were combined into subtests and adjusted for personal differences. Using the ordinal-to-interval conversion tables published here, ordinal OHQ scores can now be transformed to interval level data and thus subjected to parametric statistical analysis without violating fundamental assumptions. The precision of the instrument can be improved significantly by these minor modifications without the need to modify the original response format.     

Nonparametric IRT: Testing the bi-isotonicity of isotonic probabilistic models (ISOP)

Nonparametric tests for testing the validity of polytomous ISOP-models (unidimensional ordinal probabilistic polytomous IRT-models) are presented. Since the ISOP-model is a very general nonparametric unidimensional rating scale model the test statistics apply to a great multitude of latent trait models. A test for the comonotonicity of item sets of two or more items is suggested. Procedures for testing the comonotonicity of two item sets and for item selection are developed. The tests are based on Goodman-Kruskal's gamma index of ordinal association and are generalizations thereof. It is an essential advantage of polytomous ISOP-models within probabilistic IRT-models that the tests of validity of the model can be performed before and without the model being fitted to the data. The new test statistics have the further advantage that no prior order of items or subjects needs to be known.     

Assessing the dimensionality of the Singhapakdi, Vitell, and Kraft measure of moral intensity

In a sample of 105 students, the dimensionality of a moral intensity scale developed by Singhapakdi, Vitell, and Kraft in 1996 was assessed. Principal components analysis yielded a one-factor solution across 10 ethics scenarios, indicating that moral intensity may be treated as a unidimensional construct in some situations.     

The structure of the Beck Hopelessness Scale: A confirmatory factor analysis in UK students

The aim of this paper was to confirm the factor structure of the 20-item Beck Hopelessness Scale in a non-clinical population. Previous research has highlighted a lack of clarity in its construct validity with regards to this population.Based on previous factor analytic findings from both clinical and non-clinical studies, 13 separate confirmatory factor models were specified and estimated using LISREL 8.72 to test the one, two and three-factor models.Psychology and medical students at Queen’s University, Belfast (n = 581) completed both the BHS and the Beck Depression Inventory (BDI).All models showed reasonable fit, but only one, a four-item single-factor model demonstrated a non-significant chi-squared statistic. These four items can be used to derive a Short-Form BHS (SBHS) in which increasing scores (0-4) corresponded with increasing scores in the BDI. The four items were also drawn from all three of Beck’s proposed triad, and included both positively and negatively scored items.This study in a UK undergraduate non-clinical population suggests that the BHS best measures a one-factor model of hopelessness. It appears that a shorter four-item scale can also measure this one-factor model.     

Examination of the factor structure and concurrent validity of the Langer Mindfulness/Mindlessness Scale

Langer's theory of mindfulness proposes that a mindful person seeks out and produces novelty, is attentive to context, and is flexible in thought and behavior. In three independent studies, the factor structure of the Langer Mindfulness/Mindlessness Scale was examined. Confirmatory factor analysis failed to replicate the four-factor model and a subsequent exploratory factor analysis revealed the presence of a two-factor (mindfulness and mindlessness) solution. Study 2 demonstrated that the two factors assessed discrete constructs and were not merely products of acquiescence. Support was also found for a nine-item, one-factor model comprised solely of mindfulness items. On comparing models, Study 3 suggested the superiority of the one-factor mindfulness model. Finally, a preliminary investigation of the concurrent validity of the revised nine-item Langer Mindfulness/Mindlessness Scale is presented. The current article offers researchers a revised version of a mindfulness measure derived from a cognitive perspective.     

识字能力的单维性检验研究

本研究以义务教育阶段学生识字量测验为工具,综合运用探索性结构方程建模(ESEM)以及非参数项目反应理论中的摩根量表(Mokken量表)和DETECT分析方法,探讨了识字能力的维度。探索性结构方程建模结果显示,识字的单维性模型优于多维模型,多维的结果更多的体现出一个难度维度的特征,即字频的作用。Mokken量表分析结果显示,1~2年级和3~9年级测验更倾向于单维量表的特征。DETECT分析结果显示,两个测验的D值趋近于零,表明识字能力是单维能力。结合三种分析方法,识字能力具有单维性。     

结构方程建模中的题目打包策略

结构方程建模中题目打包法的优缺点包括：指标数据质量变好、模型拟合程度提高; 估计偏差不大, 可校正; 估计稳定, 但降低了敏感性与可证伪性。打包法的前提条件是单维、同质, 适合结构模型分析, 不适合测量模型分析。对于单维测验, 给出了一个打包流程。对于通常的多个子量表(多维结构)测验, 推荐在子量表内打包, 每个子量表打包成1个指标或者3个指标, 用于结构方程建模。     

Separating ability from need: clarifying the dimensional structure of the Need for Closure Scale

The validity of the Need for Closure Scale (NFCS) has recently been debated in the research literature. In the present study, it is argued that the traditional Decisiveness scale primarily taps ability content instead of the hypothesized motivational need; therefore, new items that explicitly probe the need for quick and unambiguous answers were developed. It was shown that these need items form a reliable scale (Study 1); were predictive of the seizing process over and beyond ability, whereas the old Decisiveness scale was not (Study 2); and were sensitive to experimentally manipulated levels of task motivation (Study 3). Finally, a reassembled NFCS with the new items replacing the traditional Decisiveness items showed superior fit as a unidimensional model. In the Discussion, it is argued that the specific position of Decisiveness is due to its particular operationalization, not to its theoretical status.     

Unidimensionality of life satisfaction and its relation to social desirability: a confirmatory study of a short form of the Life Satisfaction Scale

This study investigated construct validity of a short version of the Life Satisfaction in the Elderly Scale developed from an exploratory factor analysis in 1990 of the original version of Salamon and Conte's Life Satisfaction scale. First, a confirmatory factor analysis (maximum likelihood method) was conducted on 149 adult and elderly Italians to assess whether the latent variable of the Life Satisfaction Short Form scale was adequately represented by one factor. Analysis showed a good fit for the proposed unidimensional model, the items achieved good internal consistency on the scale, and no age differences arose in the score for the Life Satisfaction factor. Second, the correlations between the items measuring Life Satisfaction and the Eysenck 1985 Lie scale indicated that the items on the Life Satisfaction Short Form are largely independent of social desirability for younger and older adults.     

Hope and General Self-efficacy: Two Measures of the Same Construct?

The aim of this study was to test the extent to which hope measure is equivalent to general self-efficacy measure. Questionnaire data on these two constructs and other external variables were collected from 199 Chinese college students. The factor analytic results suggested that hope and self-efficacy items measured the same construct. The unidimensional model combining hope items and GSE items fit the data as well as the bidimensional model, indicating that their corresponding items measured the same underlying construct. Further analyses showed that hope and GSE did not correlate with external variables differently in a systematic manner. Most of these correlational differences were non-significant and negligible. These findings suggested that the literatures studying GSE and hope could be considered to be integrated and that researchers need to recognize and acknowledge the conceptual and operational similarities among these constructs in the literature.     

Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores

The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.     

An extension of the rating scale model with an application to the measurement of change

The polytomous unidimensional Rasch model with equidistant scoring, also known as the rating scale model, is extended in such a way that the item parameters are linearly decomposed into certain basic parameters. The extended model is denoted as the linear rating scale model (LRSM). A conditional maximum likelihood estimation procedure and a likelihood-ratio test of hypotheses within the framework of the LRSM are presented. Since the LRSM is a generalization of both the dichotomous Rasch model and the rating scale model, the present algorithm is suited for conditional maximum likelihood estimation in these submodels as well. The practicality of the conditional method is demonstrated by means of a dichotomous Rasch example with 100 items, of a rating scale example with 30 items and 5 categories, and in the light of an empirical application to the measurement of treatment effects in a clinical study.Work supported in part by the Fonds zur Förderung der Wissenschaftlichen Forschung under Grant No. P6414.     

An Inequality for Correlations in Unidimensional Monotone Latent Variable Models for Binary Variables

It is shown that a unidimensional monotone latent variable model for binary items implies a restriction on the relative sizes of item correlations: The negative logarithm of the correlations satisfies the triangle inequality. This inequality is not implied by the condition that the correlations are nonnegative, the criterion that coefficient H exceeds 0.30, or manifest monotonicity. The inequality implies both a lower bound and an upper bound for each correlation between two items, based on the correlations of those two items with every possible third item. It is discussed how this can be used in Mokken’s (A theory and procedure of scale-analysis, Mouton, The Hague, 1971) scale analysis.     

Multidimensional modeling with unidimensional approximations

This paper advances nonparametric multidimensional item response theory by reporting experimental results on the use of nonmetric multidimensional scaling (MDS) to synthesize a multidimensional model from several approximating one-dimensional models. A two-dimensional simulation data set contains items in which the two-component traits combine linearly (dominance model items) and items in which the two-component traits combine quadratically (ideal point items). Several unidimensional approximations of the two-dimensional model were obtained by running unidimensional estimation software on the simulated data set. The graphs reconstructed from MDS of the unidimensional approximations at selected points clearly separate dominance items from ideal point items, and also various types of dominance or ideal point models. MDS also succeeded in determining the dimensionality of the simulation model items from the observable item responses.     

协单参数单维度Rasch模型的优势与意义

相比多参数多维度IRT模型通过增加参数的方式来提升模型拟合度和解释度，Rasch模型流派强调“理论驱动研究”和“数据符合模型”，推崇单参数单维度的测量模型能最大限度地减少额外因素对真实测量目的的影响和干扰，从而保证测量的客观性和准确性。Rasch模型关注测量目标与测量工具的对应关系，它的“简单”特性有助于研究者更准确地评估和解释被测目标与测量工具间的适配性，且在将非线性数据转化为等距数据时具有天然的优势。