人格测验的项目反应过程与展开模型研究述评

一些研究者提出了人格测验的项目反应理想点过程假设, 并在此基础上开发了拓广等级展开模型(GGUM)用于现有人格测验的数据分析和新量表的建构, 显示出了较优势模型更优良的一些性质。不过, 现有项目反应过程的研究结果来自于大样本的调查, 缺乏实验证据的支持, GGUM也存在不适用于分析多类别人格测验数据的局限性。未来需要对GGUM进行拓展, 剔除其主观反应类别阈限对称的限定。此外, 还要重视配对格式人格测验数据分析模型的研究和开发。     

展开模型对EPQ反应数据的拟合调查

调查了拓广等级展开模型（GGUM）对EPQ反应数据的拟合情况。结果发现：（1）E量表最符合GGUM单维性的假设,即得到2个线性主成分,项目在这2个主成分上的负荷略微呈现一个半圆形的模式;（2）约束GGUM的阈限参数在项目间相等而得到的模型A在对EPQ的数据进行分析时是最简洁而有效的：（3）模型A和2PLM相比,前者在E和P上的拟合显著优于后者,在N上,两个模型提供了相似的拟合度。结果表明展开模型比优势模型更适合用于分析EPQ的数据。     

IRT展开模型及对非累积反应机制的检测

被试回答人格测验题目时并不是特质水平越高其得分率越高,这称为非累积反应机制。广义等级展开模型GGUM就是针对这一机制提出来的。使用EPQ和五因素人格问卷发现GGUM比累积IRT模型有更好的模型拟合度和测量精度。研究结果表明GGUM有其合理性,且有助于反应心理过程机制的深入探讨。     

采用理想点方法检验生活取向测验及其修订版的单维性

该文从项目反应过程出发,认为可能是由于原有的维度分析方法将生活取向测验（LOT）及其修订版（LOT-R）这两个测验的项目反应理想点过程错误地限定为优势过程,导致两个测验违背单维性的结论;进而采用主成分分析的未旋转成分负荷散点图和对应分析的维度分数散点图,并结合项目相关矩阵法验证LOT和LOT-R的维度。结果显示LOT-R的单维性得到了验证,LOT由于包含两个测量应对的项目而导致违背单维性。最后作者提出应该与理想点过程相一致的心理计量学模型对LOT-R的数据进行分析。     

重参数化多分属性DINA模型的多级评分拓广——基于等级反应模型

多分属性认知诊断模型（CDMs）比传统的二分属性CDMs提供更详细的诊断反馈信息,但现有大部分多分属性CDMs并不具备直接分析多级（或混合）评分数据的功能。本文基于等级反应模型对重参数化多分属性DINA模型进行多级评分拓广,开发一个可处理多级评分数据的等级反应多分属性DINA模型。首先通过实证数据分析呈现新模型的现实可应用性;然后通过模拟研究探究新模型的参数估计返真性。结果表明,新模型满足同时处理多分属性和多级评分数据的现实需求;且具备良好的心理计量学性能,但对测验质量有一定要求（e.g., 题目质量较高且测验Qp矩阵具有完备性等）。     

展开模型和优势模型在职业兴趣测验中的比较研究

该研究调查了展开模型(GGUM)和优势模型(GRM)对职业兴趣测验反应数据的拟合情况,并对展开模型和优势模型两种测验编制方法在职业兴趣测验中进行了比较。结果发现:(1)展开模型的模型拟合情况和测量精度优于累积模型,两种模型对被试能力参数估计的差异主要体现在极端被试上,对兴趣水平极端高的被试,展开模型的估计值更精确;(2)采用展开模型编制的测验在信度上远远高于Likert方法编制的测验,中间区域题目的增加提高了测验的信度,但两种方法在测验的效标关联效度上没有差异。结果表明,在职业兴趣的测量上,展开模型更精确;在职业兴趣测验的编制上,GGUM和Likert法没有差异,反而Likert法具有简便、易懂的优势。     

人格测量：从累积式模型到展开式模型

当前大多数人格测量都采用的是累积式反应模型方法,该模型假设被试在测验上的得分随其能力或特质提高而增加,但是随着人格测量技术的不断发展,这一模型的实施效果遭到了质疑,研究者们开始关注展开式模型,该模型认为被试的反应取决于被试能力和项目阈值的匹配程度,当被试能力与项目阈值完全匹配时,被试做出肯定回答的概率达到最高点,称之为“理想点”,展开式模型的目的就是找到被试的理想点,从而寻找其真正的态度强度或人格特质水平。GGUM作为一种比较成熟的展开式模型,已经开始应用于人格测量的各个领域,但仍需要进行大规模的试测,在评估和预测效度方面积累经验,建立业界认可的心理测量学标准,不断探讨和开发相应的心理测量理论和简便易行的统计程序。     

项目反应理论等级反应模型项目信息量

信息函数作为项目反应理论中的一个重要概念,在进行项目和测验分析的工作中,以及在指导测验编制的工作中,有着非常重要的应用价值。信息函数的应用在计算机化自适应测验中更是重中之重,也受到最大关注。然而,关于多级记分项目信息函数特性的研究还比较少。本研究模拟了被试特质水平参数数据和项目参数数据,其中被试特质水平参数生成了121个被试特质水平参数点,项目参数生成了4批不同区分度参数数据,每批数据有126个不同难度等级参数组合模式的项目,每个项目有5个难度等级。通过数据分析后发现,等级反应模型项目提供最大信息量所对应的被试特质水平,是与该项目几个相互临近的难度等级组相适应,既不是只与其中一个难度等级对应,也不一定是与所有难度等级对应。本研究称这种规律为“临近难度等级占优”。这个发现无疑对测验质量分析和测验编制工作,包括计算机化自适应测验编制,具有重要的指导意义     

项目间多维测验作答时间数据分析：潜在特质速度间效应建模

随着计算机测验使用的普及化,被试在心理与教育测验上的作答反应时的获取也越发便利。为了充分利用项目反应时信息,单维与多维的反应时模型相继被提出。然后,在项目间多维反应时数据中,潜在特质速度之间可能存在共同关系（比如,层阶关系）,此时现有的反应时模型并不能适用。基于此,本研究提出了高阶对数正态反应时模型与双因子对数正态反应时模型。在模拟研究中,高阶对数正态反应时模型与双因子对数正态反应时模型的各参数都能被准确估计。在瑞文标准推理测验的三组测验项目的反应时数据中,双因子对数正态反应时模型表现出更为优秀的拟合效果,同时基于多个统计量说明了局部与全局潜在特质速度同时存在的必要性。因此,在项目间多维测验反应时数据分析中,非常有必要考虑多维潜在特质速度之间的共同效应。     

计算机自适应测验在特质焦虑量表中的运用

将基于项目反应理论的计算机自适应测验运用于特质焦虑量表,考察这一测验形式在人格测量中所具有的特性.收集特质焦虑量表真实纸笔作答数据,选用合适的心理测量模型,模拟计算机自适应测验.结果表明：相对纸笔测验而言,计算机自适应测验的测试效率更高、对被试的分辨力更强、结果更直观.计算机自适应测验在人格测量中的实践值得进一步探索.     

GGUMLINK: A computer program to link parameter estimates of the generalized graded unfolding model from item response theory

The generalized graded unfolding model (GGUM) is an item response theory (IRT) model that implements symmetric, nonmonotonic, single-peaked item characteristic curves. The GGUM is appropriate for measuring individual differences for a variety of psychological constructs, especially attitudes. Like other IRT models, the location and scale (i.e., the metric) of parameter estimates from the GGUM are data dependent. Therefore, parameter estimates from alternative calibrations will generally not be comparable, even when responses to the same items are analyzed. GGUMLINK is a computer program developed to reexpress parameter estimates from two separate GGUM calibrations in a common metric. In this way, the results from separate calibrations of model parameters can be compared. GGUMLINK can secure a common metric by using one of five methods that have recently been generalized to the GGUM. The GGUMLINK executable program is available free and may be downloaded from http://www.education.umd.edu/EDMS/tutorials/index.html.     

Generalized graded unfolding model with structural equation for subject parameters

The generalized graded unfolding model (GGUM) is capable of analyzing polytomous scored, unfolding data such as agree‐disagree responses to attitude statements. In the present study, we proposed a GGUM with structural equation for subject parameters, which enabled us to evaluate the relation between subject parameters and covariates and/or latent variables simultaneously, in order to avoid the influence of attenuation. Additionally, an algorithm for parameter estimation is newly implemented via the Markov Chain Monte Carlo (MCMC) method, based on Bayesian statistics. In the simulation, we compared the accuracy of estimates of regression coefficients between the proposed model and a conventional method using a GGUM (where regression coefficients are estimated using estimates of θ). As a result, the proposed model performed much better than the conventional method in terms of bias and root mean squared errors of estimates of regression coefficients. The study concluded by verifying the efficacy of the proposed model, using an actual data example of attitude measurement.     

Middle Response Scale Options are Inappropriate for Ideal Point Scales

Purpose

The purpose of this study is to provide a theoretical rationale for the inappropriateness of middle response options on the response scales offered on ideal point scales, and to provide empirical support for this argument to assist ideal point scale development.

Design/Methodology/Approach

The same ideal point scale was administered in three quasi-experimental groups varying only in the response scale offered: the three groups received either a four-, five-, or six-option response scale. An ideal point Item Response Theory model, the Generalized Graded Unfolding Model (GGUM), was fit to the response data, and model-data fit was compared across conditions.

Findings

Responses from the four- and six-option conditions were fit well by the GGUM, but responses from the five-option condition were not fit well.

Implications

Despite the scale being constructed to follow the tenets of ideal point responding, the GGUM was unable to provide a reasonable probabilistic account of responding when the response scale contained a middle option. The authors find support for the argument that an odd-numbered response scale does not match the principles of ideal point responding, and can actually result in misspecifying the underlying response process.

Originality/Value

Although a growing body of research has suggested that attitude and personality measurement is best conceptualized under the assumptions of ideal point responding, little practical advice has been given to researchers or practitioners regarding scale creation. This was the first study to theoretically and empirically assess the response scale on ideal point scales, and offer guidance for constructing ideal point scales.     

Poisson signal-detection theory: link between threshold models and the Gaussian assumption.

The Gaussian model of signal detection cannot fit asymmetric data as long as the variances of the distributions are kept equal. It is therefore common practice to assume unequal variances in order to fit these data. But this assumption leads to the well-known crossover problem. The present paper provides new arguments for the abandonment of the Gaussian model with unequal variances. In its stead, this paper reevaluates multiple-parallel-threshold models. In particular, the Poisson model turns out to be very useful: it can handle data with any degree of asymmetry, giving a reasonable interpretation of the two parameters of the receiver-operating characteristic. The three-state-threshold model (Krantz, 1969) is given a new interpretation in light of the Poisson model. The slope of Poisson double-probability plots turns out to be much closer to unity than is predicted by the Gaussian approximation.     

Poisson signal-detection theory: Link between threshold models and the Gaussian assumption

The Gaussian model of signal detection cannot fit asymmetric data as long as the variances of the distributions are kept equal. It is therefore common practice to assume unequal variances in order to fit these data. But this assumption leads to the well-known crossover problem. The present paper provides new arguments for the abandonment of the Gaussian model with unequal variances. In its stead, this paper reevaluates multiple-parallel-threshold models. In particular, the Poisson model turns out to be very useful: it can handle data with any degree of asymmetry, giving a reasonable interpretation of the two parameters of the receiver-operating characteristic. The three-state-threshold model (Krantz, 1969) is given a new interpretation in light of the Poisson model. The slope of Poisson double-probability plots turns out to be much closer to unity than is predicted by the Gaussian approximation.     

Confirmatory factor analysis of the three-factor structure of the schizotypal personality questionnaire and Chapman schizotypy scales

We examined the factor structure of the Schizotypal Personality Questionnaire (SPQ; Raine, 1991), using confirmatory factor analysis in 3 experiments, with an aim to better understand the construct of schizotypy. In Experiment 1 we tested the fit of 2-, 3-, and 4-factor models on SPQ data from a normal sample. The paranoid 4-factor model fit the data best but not adequately. Based on the strong basis for the Raine 3-factor model we attempted to improve the fit of the 3-factor model by making 3 modifications to the Raine model. These modifications produced a well-fitting model. In Experiment 2 the good fit of this modified 2-factor model to SPQ scores was replicated in an independent normal sample. In Experiment 3, the modified 3-factor model was successfully extended to include the 3 Chapman schizotypy scales. Together these 3 experiments indicate that the 3-factor model of the SPQ, albeit with some slight modifications, is a good model for schizotypy structure that is not restricted to 1 measure of schizotypal personality traits.     

The factor structure of the Beck Depression Inventory-II: an evaluation

The Beck Depression Inventory-II (BDI-II) is a frequently used scale for measuring depressive severity. BDI-II data (404 clinical; 695 nonclinical adults) were analyzed by means of confirmatory factor analysis to test whether the factor structure model with a somatic-affective and cognitive component of depression, formulated by Beck and colleagues, has a good fit. We also evaluated 10 alternative models. The fit of Beck's model was not good for all criteria. Three of the alternative models had a better fit in both samples, but none of these met all criteria for good fit. Of the alternatives with a better fit, we selected the only model with unidimensional subscales, which assesses a somatic, affective, and cognitive dimension. For this model, which we recommend, as well as for Beck' original model, a good fitting structure containing 15 and 16 items was developed with an item-deletion algorithm.