A Comparison Between Linear IRT Observed‐Score Equating and Levine Observed‐Score Equating Under the Generalized Kernel Equating Framework

In this article, linear item response theory (IRT) observed‐score equating is compared under a generalized kernel equating framework with Levine observed‐score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when using data from IRT models, linear IRT observed‐score equating is virtually identical to Levine observed‐score equating. This leads to the conclusion that poststratification equating based on true anchor scores can be viewed as the curvilinear Levine observed‐score equating.     

Choosing Among Tucker or Chained Linear Equating in Two Testing Situations: Rater Comparability Scoring and Randomly Equivalent Groups With an Anchor

Tucker and chained linear equatings were evaluated in two testing scenarios. In Scenario 1, referred to as rater comparability scoring and equating, the anchor‐to‐total correlation is often very high for the new form but moderate for the reference form. This may adversely affect the results of Tucker equating, especially if the new and reference form samples differ in ability. In Scenario 2, the new and reference form samples are randomly equivalent but the correlation between the anchor and total scores is low. When the correlation between the anchor and total scores is low, Tucker equating assumes that the new and reference form samples are similar in ability (which, with randomly equivalents groups, is the correct assumption). Thus Tucker equating should produce accurate results. Results indicated that in Scenario 1, the Tucker results were less accurate than the chained linear equating results. However, in Scenario 2, the Tucker results were more accurate than the chained linear equating results. Some implications are discussed.     

Comparison of the One‐ and Bi‐Direction Chained Equipercentile Equating

This study investigated differences between two approaches to chained equipercentile (CE) equating (one‐ and bi‐direction CE equating) in nearly equal groups and relatively unequal groups. In one‐direction CE equating, the new form is linked to the anchor in one sample of examinees and the anchor is linked to the reference form in the other sample. In bi‐direction CE equating, the anchor is linked to the new form in one sample of examinees and to the reference form in the other sample. The two approaches were evaluated in comparison to a criterion equating function (i.e., equivalent groups equating) using indexes such as root expected squared difference, bias, standard error of equating, root mean squared error, and number of gaps and bumps. The overall results across the equating situations suggested that the two CE equating approaches produced very similar results, whereas the bi‐direction results were slightly less erratic, smoother (i.e., fewer gaps and bumps), usually closer to the criterion function, and also less variable.     

A Comparison of IRT Equating and Beta 4 Equating

Four equating methods (3PL true score equating, 3PL observed score equating, beta 4 true score equating, and beta 4 observed score equating) were compared using four equating criteria: first-order equity (FOE), second-order equity (SOE), conditional-mean-squared-error (CMSE) difference, and the equipercentile equating property. True score equating more closely achieved estimated FOE than observed score equating when the true score distribution was estimated using the psychometric model that was used in the equating. Observed score equating more closely achieved estimated SOE, estimated CMSE difference, and the equipercentile equating property than true score equating. Among the four equating methods, 3PL observed score equating most closely achieved estimated SOE and had the smallest estimated CMSE difference, and beta 4 observed score equating was the method that most closely met the equipercentile equating property.     

Equating Error and Statistical Bias in Small Sample Linear Equating

A resampling study was conducted to compare the statistical bias and standard errors of nonequivalent-groups linear test equating in small samples of examinees. Sample sizes of 15, 25, 50, and 100 were examined. One thousand samples of each size were drawn with replacement from each of 5 archival data files from teacher subject area tests. For each test, data files from 2 parallel forms were used. Results suggest trivial levels of equating bias even with small samples, but substantial increases in standard errors as sample size decreases. Results were interpreted in terms of applications to testing situations in which small numbers of examinees are available.     

A General Linear Method for Equating With Small Samples

Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed‐score equating, one which provides flexible control over how form difficulty is assumed versus estimated to change across the score scale. A general linear method is presented as an extension of traditional linear methods. The general method is then compared to other linear and nonlinear methods in terms of accuracy in estimating a criterion equating function. Results from two parametric bootstrapping studies based on real data demonstrate the usefulness of the general linear method.     

A Comparison of the Kernel Equating Method with Traditional Equating Methods Using SAT® Data

This study applied kernel equating (KE) in two scenarios: equating to a very similar population and equating to a very different population, referred to as a distant population, using SAT^® data. The KE results were compared to the results obtained from analogous traditional equating methods in both scenarios. The results indicate that KE results are comparable to the results of other methods. Further, the results show that when the two populations taking the two tests are similar on the anchor score distributions, different equating methods yield the same or very similar results, even though they have different assumptions.     

A Comparison of IRT Proficiency Estimation Methods Under Adaptive Multistage Testing

This inquiry is an investigation of item response theory (IRT) proficiency estimators’ accuracy under multistage testing (MST). We chose a two‐stage MST design that includes four modules (one at Stage 1, three at Stage 2) and three difficulty paths (low, middle, high). We assembled various two‐stage MST panels (i.e., forms) by manipulating two assembly conditions in each module, such as difficulty level and module length. For each panel, we investigated the accuracy of examinees’ proficiency levels derived from seven IRT proficiency estimators. The choice of Bayesian (prior) versus non‐Bayesian (no prior) estimators was of more practical significance than the choice of number‐correct versus item‐pattern scoring estimators. The Bayesian estimators were slightly more efficient than the non‐Bayesian estimators, resulting in smaller overall error. Possible score changes caused by the use of different proficiency estimators would be nonnegligible, particularly for low‐ and high‐performing examinees.     

A Comparison of Two Procedures for Computing IRT Equating Coefficients

In order to equate tests under Item Response Theory (IRT), one must obtain the slope and intercept coefficients of the appropriate linear transformation. This article compares two methods for computing such equating coefficients–Loyd and Hoover (1980) and Stocking and Lord (1983). The former is based upon summary statistics of the test calibrations; the latter is based upon matching test characteristic curves by minimizing a quadratic loss function. Three types of equating situations: horizontal, vertical, and that inherent in IRT parameter recovery studies–were investigated. The results showed that the two computing procedures generally yielded similar equating coefficients in all three situations. In addition, two sets of SAT data were equated via the two procedures, and little difference in the obtained results was observed. Overall, the results suggest that the Loyd and Hoover procedure usually yields acceptable equating coefficients. The Stocking and Lord procedure improves upon the Loyd and Hoover values and appears to be less sensitive to atypical test characteristics. When the user has reason to suspect that the test calibrations may be associated with data sets that are typically troublesome to calibrate, the Stocking and Lord procedure is to be preferred.     

Adjoined Piecewise Linear Approximations (APLAs) for Equating: Accuracy Evaluations of a Postsmoothing Equating Method

The purpose of this study was to evaluate the use of adjoined and piecewise linear approximations (APLAs) of raw equipercentile equating functions as a postsmoothing equating method. APLAs are less familiar than other postsmoothing equating methods (i.e., cubic splines), but their use has been described in historical equating practices of large‐scale testing programs. This study used simulations to evaluate APLA equating results and compare these results with those from cubic spline postsmoothing and from several presmoothing equating methods. The overall results suggested that APLAs based on four line segments have accuracy advantages similar to or better than cubic splines and can sometimes produce more accurate smoothed equating functions than those produced using presmoothing methods.     

不同病情魔芋植株根际土壤营养成分研究

为探明不同病情魔芋(Amorphophallus konjac)植株根际土壤的营养成分差异及其与病情的相关性。以丹凤凯农魔芋种植基地为调查对象,对不同发病情况样地的根际土壤进行采样及营养成分比较分析。结果显示,六个样地的土壤样本在有机质、速效氮、速效磷、速效钾、全氮含量以及pH等方面均存在显著性差异。在调查的土壤样本中,正常生长植株根际土壤的有机质、速效钾、全氮含量均显著高于发病植株,而速效氮、速效磷呈现不一致的结果。有机质和全氮含量以阳河村最高,分别达2.15%和0.1%;速效钾含量以南丈沟村最高,达131.5 mg·kg~(-1)。魔芋根际土壤中有机质、速效钾、全氮的含量可能是影响魔芋软腐病发生的关键营养因子。建议在魔芋栽培管理中,适当控制磷肥,而增加有机质和钾肥增强魔芋植株的抗病能力。     

Exact Versus Asymptotic Mantel-Haenszel DIF Statistics: A Comparison of Performance Under Small-Sample Conditions

This study evaluated exact testing (Agresti, 1992) as a method for conducting Mantel-Haenszel DIF analyses (Holland & Thayer, 1988) with relatively small samples. Sample-size restrictions limit the standard asymptotic Mantel-Haenszel for many practical applications; however, new developments in computing technology have made exact testing procedures feasible. The highly discrete distributions that are likely to occur in small-sample DIF analyses could yield very different results for asymptotic versus exact methods. It is therefore important to determine under controlled conditions the extent to which the exact approach is effective in correctly identifying DIF. A series of computer simulations were conducted in which 3 levels of induced bias (IRT b -parameter differences between groups of .25, .50, and .75) and 4 sample sizes (reference group = 500, focal group = 25, 50, 100, and 200) were investigated. Power comparisons at .01 and .05 alpha levels were carried out between the exact testing procedure and the conventional Mantel-Haenszel     

不同外界条件下的足球战术选择

足球比赛是在室外进行的。场地、气候、环境等客观因素对比赛的影响是肯定的。据调查，不少球队对此认识不足，准备不够。文章就不同的外界条件对足球比赛的影响，阐述了如何合理、有效地选择足球战术。     

用统计分析方法对区域经济状况进行分类比较研究

利用统计资料 ,从消费支出的角度 ,运用多元统计分析中的聚类分析及判别分析的方法 ,建立数学模型 ,对我国不同地区经济发展水平进行分类比较研究 .     

等级记分模型下几种等值方法的比较研究

等值对考试具有重要意义,而我国的大部分考试却没有实现等值,在少数经过等值的考试中,大多只限于对二级记分题目的等值,鲜有对多级记分题目的等值研究。该研究针对包含多级记分题目的国内某大型语言类考试,探讨了等级反应模型下的同时校准法、固定共同题参数法以及链接独立校准法中的平均数标准差方法、平均数平均数方法、Haebara法和Stocking-Lord法六种等值方法的效果,从而优选最适合该考试的等值方法。     

A Comparison of Angoff's Design I and Design II for Vertical Equating Using Traditional and IRT Methodology

Practical considerations in conducting an equating study often require a trade-off between testing time and sample size. A counterbalanced design (Angoff's Design II) is often selected because, as each examinee is administered both test forms and therefore the errors are correlated, sample sizes can be dramatically reduced over those required by a spiraling design (Angoff's Design I), where each examinee is administered only one test form. However, the counterbalanced design may be subject to fatigue, practice, or context effects. This article investigated these two data collection designs (for a given sample size) with equipercentile and IRT equating methodology in the vertical equating of two mathematics achievement tests. Both designs and both methodologies were judged to adequately meet an equivalent expected score criterion; Design II was found to exhibit more stability over different samples.     

Comparison of Item Preequating and Random Groups Equating Using IRT and Equipercentile Methods

An item-preequating design and a random groups design were used to equate forms of the American College Testing (ACT) Assessment Mathematics Test. Equipercentile and 3-parameter logistic model item-response theory (IRT) procedures were used for both designs. Both pretest methods produced inadequate equating results, and the IRT item preequating method resulted in more equating error than had no equating been conducted. Although neither of the item preequating methods performed well, the results from the equipercentile preequating method were more consistent with those from the random groups method than were the results from the IRT item pretest method. Item context and position effects were likely responsible, at least in part, for the inadequate results for item preequating. Such effects need to be either controlled or modeled, and the design further researched before the item preequating design can be recommended for operational use.     

Futility1 of Log‐Linear Smoothing when Equating with Unrepresentative Small Samples

The impact of log‐linear presmoothing on the accuracy of small sample chained equipercentile equating was evaluated under two conditions . In the first condition the small samples differed randomly in ability from the target population. In the second condition the small samples were systematically different from the target population. Results showed that equating with small samples (e.g., N < 25 or 50) using either raw or smoothed score distributions led to considerable large random equating error (although smoothing reduced random equating error). Moreover, when the small samples were not representative of the target population, the amount of equating bias also was quite large. It is concluded that although presmoothing can reduce random equating error, it is not likely to reduce equating bias caused by using an unrepresentative sample. Other alternatives to the small sample equating problem (e.g., the SiGNET design) which focus more on improving data collection are discussed.     

不同光环境下地果叶形态及叶绿素荧光参数的变化

比较了稀疏林地和林缘旷地两种不同光环境下,地果的单叶面积、比叶重(LMA)、叶绿素含量以及叶绿素荧光参数,探讨地果形态和光合特征对光照的适应性.结果显示:稀疏林下地果单叶面积显著高于林缘旷地,比叶重显著小于林缘旷地;叶绿素a,叶绿素b,总叶绿素含量显著高于林缘旷地;初始荧光值Fo显著高于林缘旷地,最大荧光Fm,最大光能转化效率Fm,实际光化学效率Yield,光化学淬灭qP和非光化学淬灭qN均大于林缘旷地,但不显著.说明地果对弱光环境有一定程度的适应能力,但稀疏林地的弱光已导致PSII部分失活或破坏,限制了地果对光强更低的生境的适应,可能是地果在郁闭度较高的林地中鲜有分布的重要原因.     

A New Statistic for Selecting the Smoothing Parameter for Polynomial Loglinear Equating Under the Random Groups Design

Smoothing is designed to yield smoother equating results that can reduce random equating error without introducing very much systematic error. The main objective of this study is to propose a new statistic and to compare its performance to the performance of the Akaike information criterion and likelihood ratio chi-square difference statistics in selecting the smoothing parameter for polynomial loglinear equating under the random groups design. These model selection statistics were compared for four sample sizes (500, 1,000, 2,000, and 3,000) and eight simulated equating conditions, including both conditions where equating is not needed and conditions where equating is needed. The results suggest that all model selection statistics tend to improve the equating accuracy by reducing the total equating error. The new statistic tended to have less overall error than the other two methods.