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1.
Technical difficulties occasionally lead to missing item scores and hence to incomplete data on computerized tests. It is not straightforward to report scores to the examinees whose data are incomplete due to technical difficulties. Such reporting essentially involves imputation of missing scores. In this paper, a simulation study based on data from three educational tests is used to compare the performances of six approaches for imputation of missing scores. One of the approaches, based on data mining, is the first application of its kind to the problem of imputation of missing data. The approach based on data mining and a multiple imputation approach based on chained equations led to the most accurate imputation of missing scores, and hence to most accurate score reporting. A simple approach based on linear regression performed the next best overall. Several recommendations are made regarding the reporting of scores to examinees with incomplete data.  相似文献   

2.
Herman M 《Child development》2004,75(3):730-748
This paper categorizes multiracial youth (N=1,496) ages 14 to 19 and compares them with each other and with monoracial youth on identity development measures. The multiracial categories used here are derived from youths' reports of their own and their parents' race(s). Comparisons are made within groups of multiracial respondents who make different choices among single-race categories. Results show differences between subgroups in strength and importance of ethnic identity, self-esteem, and perceptions of ethnic discrimination. Multinomial logistic regression shows further that physiognomy, ethnic identity, and race of coresident parent(s) are significantly associated with reported race. Also related to racial identification among part-Hispanic youth are the racial distribution and socioeconomic status of their neighborhoods and the racial distributions of their schools.  相似文献   

3.
In this article, grade point average (GPA) is considered a missing data technique for unavailable grades in school grade records. In Study 1, theoretical and empirical differences between GPA and seven alternative missing grade techniques were considered. These seven techniques are subject mean substitution, corrected subject mean, subject correlation substitution, regression imputation, expectation maximization algorithm imputation and two multiple imputation methods-stochastic regression imputation and data augmentation., The missing grade techniques differ greatly. Data augmentation and stochastic regression imputation appear to be superior as missing grade techniques. In Study 2, the completed grade records (observed and imputed values) were used in two prediction analyses of academic achievement. One analysis was based on unweighed grades, the other on weighed grades. In both analyses, alternative missing grade methods produced better and more consistent predictions. It is concluded that some alternative missing grade methods are superior to GPA.  相似文献   

4.
This paper reviews methods for handling missing data in a research study. Many researchers use ad hoc methods such as complete case analysis, available case analysis (pairwise deletion), or single-value imputation. Though these methods are easily implemented, they require assumptions about the data that rarely hold in practice. Model-based methods such as maximum likelihood using the EM algorithm and multiple imputation hold more promise for dealing with difficulties caused by missing data. While model-based methods require specialized computer programs and assumptions about the nature of the missing data, these methods are appropriate for a wider range of situations than the more commonly used ad hoc methods. The paper provides an illustration of the methods using data from an intervention study designed to increase students’ ability to control their asthma symptoms.  相似文献   

5.
Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from nonresponse are full information maximum likelihood (FIML) or multiple imputation (MI). The effects on bias of growth parameter estimates from using these methods are compared via a simulation study. The results indicate that caution needs to be taken when utilizing panel weights when there is missing data, and to consider methods like FIML and MI, which are not as susceptible to the omission of important auxiliary variables.  相似文献   

6.
Although structural equation modeling software packages use maximum likelihood estimation by default, there are situations where one might prefer to use multiple imputation to handle missing data rather than maximum likelihood estimation (e.g., when incorporating auxiliary variables). The selection of variables is one of the nuances associated with implementing multiple imputation, because the imputer must take special care to preserve any associations or special features of the data that will be modeled in the subsequent analysis. For example, this article deals with multiple group models that are commonly used to examine moderation effects in psychology and the behavioral sciences. Special care must be exercised when using multiple imputation with multiple group models, as failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random. This study investigates two imputation strategies that have been proposed in the literature, product term imputation and separate group imputation. A series of simulation studies shows that separate group imputation adequately preserves the multiple group data structure and produces accurate parameter estimates.  相似文献   

7.
Methods of uniform differential item functioning (DIF) detection have been extensively studied in the complete data case. However, less work has been done examining the performance of these methods when missing item responses are present. Research that has been done in this regard appears to indicate that treating missing item responses as incorrect can lead to inflated Type I error rates (false detection of DIF). The current study builds on this prior research by investigating the utility of multiple imputation methods for missing item responses, in conjunction with standard DIF detection techniques. Results of the study support the use of multiple imputation for dealing with missing item responses. The article concludes with a discussion of these results for multiple imputation in conjunction with other research findings supporting its use in the context of item parameter estimation with missing data.  相似文献   

8.
When using multiple imputation in the analysis of incomplete data, a prominent guideline suggests that more than 10 imputed data values are seldom needed. This article calls into question the optimism of this guideline and illustrates that important quantities (e.g., p values, confidence interval half-widths, and estimated fractions of missing information) suffer from substantial imprecision with a small number of imputations. Substantively, a researcher can draw categorically different conclusions about null hypothesis rejection, estimation precision, and missing information in distinct multiple imputation runs for the same data and analysis with few imputations. This article explores the factors associated with this imprecision, demonstrates that precision improves by increasing the number of imputations, and provides practical guidelines for choosing a reasonable number of imputations to reduce imprecision for each of these quantities.  相似文献   

9.
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their implications for educational research. We illustrate the issues with an educational, longitudinal survey in which missing data was significant, but for which we were able to collect much of these missing data through subsequent data collection. We thus compare methods, that is, step-wise regression (basically ignoring the missing data) and MI models, with the model from the actual enhanced sample. The value of MI is discussed and the risks involved in ignoring missing data are considered. Implications for research practice are discussed.  相似文献   

10.
Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in the context of a MANOVA, with the typical default for dealing with missing data: listwise deletion. When data are missing at random, the new methods maintained the nominal Type I error rate and had power comparable to the complete data condition. When 40% of the data were missing completely at random, the Type I error rates for the new methods were inflated, but not for lower percents.  相似文献   

11.
Testing the goodness of fit of item response theory (IRT) models is relevant to validating IRT models, and new procedures have been proposed. These alternatives compare observed and expected response frequencies conditional on observed total scores, and use posterior probabilities for responses across θ levels rather than cross-classifying examinees using point estimates of θ and score responses. This research compared these alternatives with regard to their methods, properties (Type 1 error rates and empirical power), available research, and practical issues (computational demands, treatment of missing data, effects of sample size and sparse data, and available computer programs). Different advantages and disadvantages related to these characteristics are discussed. A simulation study provided additional information about empirical power and Type 1 error rates.  相似文献   

12.
由于有限群子群的乘积不一定是子群,如何判断子群的乘积为子群是一个重要的问题.本文主要证明有限群的所有共轭子群的乘积是子群,并且给出了共轭子群的几个性质.  相似文献   

13.
The current research demonstrates the effectiveness of using structural equation modeling (SEM) for the investigation of subgroup differences with sparse data designs where not every student takes every item. Simulations were conducted that reflected missing data structures like those encountered in large survey assessment programs (e.g., National Assessment of Educational Progress). A maximum likelihood method of estimation was implemented that allowed all data to be used without performing any imputation. A multiple indicators multiple causes (MIMIC) model was used to examine group differences. There was no detriment to the estimation of the MIMIC model parameters under sparse data design conditions when compared to the design without missing data. The overall size of samples had more influence on the variability of estimates than did the data design.  相似文献   

14.
A previous study of the initial, preoperational version of the Graduate Record Examinations (GRE) analytical ability measure (Powers & Swinton, 1984) revealed practically and statistically significant effects of test familiarization on analytical test scores. (Two susceptible item types were subsequently removed from the test.) Data from this study were reanalyzed for evidence of differential effects for subgroups of examinees classified by age, ethnicity, degree aspiration, English language dominance, and performance on other sections of the GRE General Test. The results suggested little, if any, difference among subgroups of examinees with respect to their response to the particular kind of test preparation considered in the study. Within the limits of the data, no particular subgroup appeared to benefit significantly more or significantly less than any other subgroup.  相似文献   

15.
考虑响应变量随机缺失下线性模型响应变量均值的估计问题,分别获得了基于完全观测样本数据、线性回归插补后的"完全样本"和逆概率加权插补后的"完全样本"得到的响应变量均值估计,并证明了其渐近正态性.  相似文献   

16.
Missing data are a common problem in a variety of measurement settings, including responses to items on both cognitive and affective assessments. Researchers have shown that such missing data may create problems in the estimation of item difficulty parameters in the Item Response Theory (IRT) context, particularly if they are ignored. At the same time, a number of data imputation methods have been developed outside of the IRT framework and been shown to be effective tools for dealing with missing data. The current study takes several of these methods that have been found to be useful in other contexts and investigates their performance with IRT data that contain missing values. Through a simulation study, it is shown that these methods exhibit varying degrees of effectiveness in terms of imputing data that in turn produce accurate sample estimates of item difficulty and discrimination parameters.  相似文献   

17.
Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML tends to perform poorly with small-sample growth models. This report demonstrates that the fault lies not with how FIML accommodates missingness but rather with maximum likelihood estimation itself. We discuss how the less popular restricted likelihood form of FIML, along with small-sample-appropriate methods, yields trustworthy estimates for growth models with small samples and missing data. That is, previously reported small sample issues with FIML are attributable to finite sample bias of maximum likelihood estimation not direct likelihood. Estimation issues pertinent to joint multiple imputation and predictive mean matching are also included and discussed.  相似文献   

18.
几种不同缺失值填充方法的比较   总被引：1，自引：0，他引：1  
在数据挖掘和机器学习领域,缺失数据经常出现,本文从理论和实验两方面分析了常用的几种处理缺失数据的方法的优、缺点。  相似文献   

19.
ABSTRACT

Differential item functioning (DIF) analyses have been used as the primary method in large-scale assessments to examine fairness for subgroups. Currently, DIF analyses are conducted utilizing manifest methods using observed characteristics (gender and race/ethnicity) for grouping examinees. Homogeneity of item responses is assumed denoting that all examinees respond to test items using a similar approach. This assumption may not hold with all groups. In this study, we demonstrate the first application of the latent class (LC) approach to investigate DIF and its sources with heterogeneous (linguistic minority groups). We found at least three LCs within each linguistic group, suggesting the need to empirically evaluate this assumption in DIF analysis. We obtained larger proportions of DIF items with larger effect sizes when LCs within language groups versus the overall (majority/minority) language groups were examined. The illustrated approach could be used to improve the ways in which DIF analyses are typically conducted to enhance DIF detection accuracy and score-based inferences when analyzing DIF with heterogeneous populations.  相似文献   

20.
For a certification, licensure, or placement exam, allowing examinees to take multiple attempts at the test could effectively change the pass rate. Change in the pass rate can occur without any change in the underlying latent trait, and can be an artifact of multiple attempts and imperfect reliability of the test. By deriving formulae to compute the pass rate under two definitions, this article provides tools for testing practitioners to compute and evaluate the change in the expected pass rate when a certain (maximum) number of attempts are allowed without any change in the latent trait. This article also includes a simulation study that considers change in ability and differential motivation of examinees to retake the test. Results indicate that the general trend shown by the analytical results is maintained—that is, the marginal expected pass rate increases with more attempts when the testing volume is defined as the total number of test takers, and decreases with more attempts when the testing volume is defined as the total number of test attempts.  相似文献   

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