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1.
Shelley A. Blozis Xiaojia Ge Shu Xu Misaki N. Natsuaki Daniel S. Shaw Jenae M. Neiderhiser 《Structural equation modeling》2013,20(2):283-298
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software. 相似文献
2.
Linying Ji Sy-Miin Chow Alice C. Schermerhorn Nicholas C. Jacobson E. Mark Cummings 《Structural equation modeling》2018,25(5):715-736
Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010). 相似文献
3.
Recent changes to federal guidelines for the collection of data on race and ethnicity allow respondents to select multiple race categories. Redefining race subgroups in this manner poses problems for research spanning both sets of definitions. NAEP long-term trends have used the single-race subgroup definitions for over thirty years. Little is known about the effects of redefining race subgroups on these trends. Bridging methods for reconciling the single and multiple race definitions have been developed. These methods treat single-race subgroup membership as unknown or missing. A simulation study was conducted to determine the effectiveness of four bridging methods: multiple imputation logistic regression, multiple imputation probabilistic whole assignment, deterministic whole assignment—smallest group, and deterministic whole assignment—largest group. Only the first of these methods incorporates covariate information about examinees into the bridging procedure. The other three methods only use information contained in the race item response. The simulation took into account the percentage of biracial examinees and the missing data mechanism. Results indicated that the multiple imputation logistic regression was often the best performing method. Given that all K-12 and higher education institutions will be required to use the multiple-race definitions by 2009, implications for No Child Left Behind and other federally mandated reporting are discussed. 相似文献
4.
There is an increasing interest in reporting subscores, both at examinee level and at aggregate levels. However, it is important to ensure reasonable subscore performance in terms of high reliability and validity to minimize incorrect instructional and remediation decisions. This article employs a statistical measure based on classical test theory that is conceptually similar to the test reliability measure and can be used to determine when subscores have any added value over total scores. The usefulness of subscores is examined both at the level of the examinees and at the level of the institutions that the examinees belong to. The suggested approach is applied to two data sets from a basic skills test. The results provide little support in favor of reporting subscores for either examinees or institutions for the tests studied here. 相似文献
5.
Pawlak粗糙集理论是基于完备信息系统提出的．而在现实世界中，绝大多数信息系统是不完备的，为了使经典粗糙集理论能够处理不完备信息系统，很多方法被提出来用于处理信息系统的不完备性．通过对这些方法进行对比，发现各种方法都不可避免地会造成信息丢失，基于此，试探性地提出了方法结合的不完备信息系统的处理方法． 相似文献
6.
Admission decisions frequently rely on multiple assessments. As a consequence, it is important to explore rational approaches to combine the information from different educational tests. For example, U.S. graduate schools usually receive both TOEFL iBT® scores and GRE® General scores of foreign applicants for admission; however, little guidance has been given to combine information from these two assessments, even though the relationships between such sections as GRE Verbal and TOEFL iBT Reading are obvious. In this study, principles are provided to explore the extent to which different assessments complement one another and are distinguishable. Augmentation approaches developed for individual tests are applied to provide an accurate evaluation of combined assessments. Because augmentation methods require estimates of measurement error and internal reliability data are unavailable, required estimates of measurement error are obtained from repeaters, examinees who took the same test more than once. Because repeaters are not representative of all examinees in typical assessments, minimum discriminant information adjustment techniques are applied to the available sample of repeaters to treat the effect of selection bias. To illustrate methodology, combining information from TOEFL iBT scores and GRE General scores is examined. Analysis suggests that information from the GRE General and TOEFL iBT assessments is complementary but not redundant, indicating that the two tests measure related but somewhat different constructs. The proposed methodology can be readily applied to other situations where multiple assessments are needed. 相似文献
7.
Maria Pampaka Graeme Hutcheson Julian Williams 《International Journal of Research & Method in Education》2016,39(1):19-37
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
8.
《Learning and individual differences》2007,17(1):25-34
This paper serves as an illustration of the usefulness of structurally incomplete designs as an approach to reduce the length of educational questionnaires. In structurally incomplete test designs, respondents only fill out a subset of the total item set, while all items are still provided to the whole sample. The scores on the unadministered items are subsequently dealt with by using methods for the estimation of missing data. Two structurally incomplete test designs — one recording two thirds, and the other recording a half of the potentially complete data — were applied to the complete item scores on 8 educational psychology scales. The incomplete item scores were estimated with missing data method Data Augmentation. Complete and estimated test data were compared at the estimates of total scores, reliability, and predictive validity of an external criterion. The reconstructed data yielded estimates that were very close to the values in the complete data. As expected the statistical uncertainty was higher in the design that recorded fewer item scores. It was concluded that the procedure of applying incomplete test designs and subsequently dealing with the missing values is very fruitful for reducing questionnaire length. 相似文献
9.
10.
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
11.
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait (θ) estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to calibrate items using the incomplete data from MST design. Further complication arises when there are multiple correlated subscales per test, and when items from different subscales need to be calibrated according to their respective score reporting metric. The current calibration-per-subscale method produced biased item parameters, and there is no available method for resolving the challenge. Deriving from the missing data principle, we showed when calibrating all items together the Rubin's ignorability assumption is satisfied such that the traditional single-group calibration is sufficient. When calibrating items per subscale, we proposed a simple modification to the current calibration-per-subscale method that helps reinstate the missing-at-random assumption and therefore corrects for the estimation bias that is otherwise existent. Three mainstream calibration methods are discussed in the context of MST, they are the marginal maximum likelihood estimation, the expectation maximization method, and the fixed parameter calibration. An extensive simulation study is conducted and a real data example from NAEP is analyzed to provide convincing empirical evidence. 相似文献
12.
A procedure for evaluating candidate auxiliary variable correlations with response variables in incomplete data sets is outlined. The method provides point and interval estimates of the outcome-residual correlations with potentially useful auxiliaries, and of the bivariate correlations of outcome(s) with the latter variables. Auxiliary variables found in this way can enhance considerably the plausibility of the popular missing at random (MAR) assumption if included in ensuing maximum likelihood analyses, or can alternatively be incorporated in imputation models for subsequent multiple imputation analyses. The approach can be particularly helpful in empirical settings where violations of the MAR assumption are suspected, as is the case in many longitudinal studies, and is illustrated with data from cognitive aging research. 相似文献
13.
The examinee‐selected‐item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set of items (e.g., choose one item to respond from a pair of items), always yields incomplete data (i.e., only the selected items are answered and the others have missing data) that are likely nonignorable. Therefore, using standard item response theory models, which assume ignorable missing data, can yield biased parameter estimates so that examinees taking different sets of items to answer cannot be compared. To solve this fundamental problem, in this study the researchers utilized the specific objectivity of Rasch models by adopting the conditional maximum likelihood estimation (CMLE) and pairwise estimation (PE) methods to analyze ESI data, and conducted a series of simulations to demonstrate the advantages of the CMLE and PE methods over traditional estimation methods in recovering item parameters in ESI data. An empirical data set obtained from an experiment on the ESI design was analyzed to illustrate the implications and applications of the proposed approach to ESI data. 相似文献
14.
Niels Smits Gideon J. Mellenbergh Harrie C. M. Vorst 《Journal of Educational Measurement》2002,39(3):187-206
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
15.
Daniel Y. Lee Jeffrey R. Harring Laura M. Stapleton 《Journal of Experimental Education》2013,81(4):596-615
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
16.
In this study, we created a computer-delivered problem-solving task based on the cognitive research literature and investigated its validity for graduate admissions assessment. The task asked examinees to sort mathematical word problem stems according to prototypes. Data analyses focused on the meaning of sorting scores and examinee perceptions of the task. Results showed that those who sorted well tended to have higher GRE General Test scores and college grades than did examinees who sorted less proficiently. Examinees generally preferred this task to multiple-choice items like those found on the General Test's Quantitative section and felt the task was a fairer measure of their ability to succeed in graduate school. Adaptations of the task might be used in admissions tests, as well as for instructional assessments to help lower- scoring examinees localize and remediate problem-solving difficulties. 相似文献
17.
Therese D. Pigott 《Educational Research and Evaluation》2013,19(4):353-383
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
18.
Mark R. Raymond Kimberly A. Swygert Nilufer Kahraman 《Journal of Educational Measurement》2012,49(4):339-361
Although a few studies report sizable score gains for examinees who repeat performance‐based assessments, research has not yet addressed the reliability and validity of inferences based on ratings of repeat examinees on such tests. This study analyzed scores for 8,457 single‐take examinees and 4,030 repeat examinees who completed a 6‐hour clinical skills assessment required for physician licensure. Each examinee was rated in four skill domains: data gathering, communication‐interpersonal skills, spoken English proficiency, and documentation proficiency. Conditional standard errors of measurement computed for single‐take and multiple‐take examinees indicated that ratings were of comparable precision for the two groups within each of the four skill domains; however, conditional errors were larger for low‐scoring examinees regardless of retest status. In addition, on their first attempt multiple‐take examinees exhibited less score consistency across the skill domains but on their second attempt their scores became more consistent. Further, the median correlation between scores on the four clinical skill domains and three external measures was .15 for multiple‐take examinees on their first attempt but increased to .27 for their second attempt, a value, which was comparable to the median correlation of .26 for single‐take examinees. The findings support the validity of inferences based on scores from the second attempt. 相似文献
19.
A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a familiar function while being only marginally less efficient than the full information ML (FIML) approach. Additional advantages of the TS approach include that it allows for easy incorporation of auxiliary variables and that it is more stable in smaller samples. The main disadvantage is that the standard errors and test statistics provided by the complete data routine will not be correct. Empirical approaches to finding the right corrections for the TS approach have failed to provide unequivocal solutions. In this article, correct standard errors and test statistics for the TS approach with missing completely at random and missing at random normally distributed data are developed and studied. The new TS approach performs well in all conditions, is only marginally less efficient than the FIML approach (and is sometimes more efficient), and has good coverage. Additionally, the residual-based TS statistic outperforms the FIML test statistic in smaller samples. The TS method is thus a viable alternative to FIML, especially in small samples, and its further study is encouraged. 相似文献
20.
WENDY M. YEN 《Journal of Educational Measurement》1984,21(2):93-111
A procedure is presented for obtaining maximum likelihood trait estimates from number-correct (NC) scores for the three-parameter logistic model. The procedure produces an NC score to trait estimate conversion table, which can be used when the hand scoring of tests is desired or when item response pattern (IP) scoring is unacceptable for other (e.g., political) reasons. Simulated data are produced for four 20-item and four 40-item tests of varying difficulties. These data indicate that the NC scoring procedure produces trait estimates that are tau-equivalent to the IP trait estimates (i.e., they are expected to have the same mean for all groups of examinees), but the NC trait estimates have higher standard errors of measurement than IP trait estimates. Data for six real achievement tests verify that the NC trait estimates are quite similar to the IP trait estimates but have higher empirical standard errors than IP trait estimates, particularly for low-scoring examinees. Analyses in the estimated true score metric confirm the conclusions made in the trait metric. 相似文献