The Personal Interview

Part One: The Historical and Intellectual Foundations of School Effectiveness Research 1.An Introduction to School Effectiveness Research 2.Current Topics and Approaches in School Effectiveness Research: The Contemporary Field Part Two: The Knowledge Base of School Effectiveness Research 3.The Methodology and Scientific Properties of School Effectiveness Research 4.The Processes of School Effectiveness 5.Context Issues within School Effectiveness Research Part Three: The Cutting Edge Issues of School Effectiveness Research 6.Some Methodological Issues in School Effectiveness Research 7.School Effectiveness and School Improvement 8.School Effectiveness: The International Dimension 9.School Effectiveness and Education Indicators 10.Theory Development in School Effectiveness Part Four: The Future of School Effectiveness Research 11. School Effectiveness Research and the Social and Behavioural Sciences 12.The Future Agenda for School Effectiveness Research

The International Handbook of School Effectiveness Research

Fundamental statistics in psychology and education.

A multidimensional Rasch-type item response model, the multidimensional random coefficients multinomial logit model, is presented as an extension to the Adams & Wilson (1996) random coefficients multinomial logit model. The model is developed in a form that permits generalization to the multidimensional case of a wide class of Rasch models, including the simple logistic model, Masters' partial credit model, Wilson's ordered partition model, and Fischer's linear logistic model. Moreover, the model includes several existing multidimensional models as special cases, including Whitely's multicomponent latent trait model, Andersen's multidimensional Rasch model for repeated testing, and Embretson's multidimensional Rasch model for learning and change. Marginal maximum likelihood estimators for the model are derived and the estimation is examined using a simulation study. Implications and applications of the model are discussed and an example is given.

The Multidimensional Random Coefficients Multinomial Logit Model

A model-based modification (SIBTEST) of the standardization index based upon a multidimensional IRT bias modeling approach is presented that detects and estimates DIF or item bias simultaneously for several items. A distinction between DIF and bias is proposed. SIBTEST detects bias/DIF without the usual Type 1 error inflation due to group target ability differences. In simulations, SIBTEST performs comparably to Mantel-Haenszel for the one item case. SIBTEST investigates bias/DIF for several items at the test score level (multiple item DIF called differential test functioning: DTF), thereby allowing the study of test bias/DIF, in particular bias/DIF amplification or cancellation and the cognitive bases for bias/DIF.

A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF

The purpose of this investigation was to study the nature of the item and ability estimates obtained when the modified three-parameter logistic model is used with two-dimensional data. To examine the effects of two-dimensional data on unidimensional parameter es timates, the relative potency of the two dimensions was systematically varied by changing the correlations between the two ability dimensions. Data sets based on correlations of .0, .3, .6, .9, and .95 were gener ated for each of four combinations of sample size and test length. Also, for each of these four combinations, five unidimensional data sets were simulated for com parison purposes. Relative to the nature of the unidi mensional estimates, it was found that the â value seemed best considered as the average of the true a values. The b value seemed best thought of as an overestimate of the true b1 values. The θ value seemed best considered as the average of the true ability pa rameters. Although there was a consistent trend for these relation...

/pdf/an-examination-of-the-characteristics-of-unidimensional-irt-5167xm67uc.pdf

An examination of the characteristics of unidimensional IRT parameter estimates derived from two-dimensional data

This study compared the effects of using a unidi mensional IRT model with two-dimensional data gener ated by noncompensatory and compensatory multidi mensional IRT models. Within each model, simulated datasets differed according to the degree of correlation between two vectors of θ parameters, ranging from 0 to .95. Results showed that the number-correct distri butions for each group of datasets were generally comparable, although factor analyses of tetrachoric correlations suggested that differences existed in the structure of the data from the two models. For the uni dimensional parameter estimates, it was found that the â values from the noncompensatory model appeared to be averages of the a1 and a2 values, while the â values from the compensatory model were best considered as an estimate of the sum of the a1 and a2 values. Con versely, the b values for the noncompensatory data were consistently greater than the b1 values, while the b values from the compensatory model were best con sidered as the aver...

Robert A. Forsyth

Papers

An examination of the characteristics of unidimensional IRT parameter estimates derived from two-dimensional data

The Comparative Effects of Compensatory and Noncompensatory Two-Dimensional Data on Unidimensional IRT Estimates

A Response to "Setting Reasonable and Useful Performance Standards" in the National Academy of Science's Grading the Nations Report Card

The relationship between teacher and student anxiety levels.

An Investigation of Empirical Sampling Distributions of Correlation Coefficients Corrected for Attenuation