Mokken scale analysis for polychotomous items: theory,a computer program and an empirical application

This paper contains three subjects. First, an extension of Mokken's nonparametric item response models from dichotomous items to items with two or more ordered answer categories is proposed. Second, a computer program to analyze multicategory item scores is presented. This program is called MSP. The analyses by means of MSP are based on the multicategory extension of Mokken's theory. Finally, an application of MSP to empirical multicategory test data is presented in order to illuminate its possibilities.     

STRATEGIC ASSET ALLOCATION FOR LONG‐TERM INVESTORS: PARAMETER UNCERTAINTY AND PRIOR INFORMATION

We study the effect of parameter uncertainty on the long‐run risk for three asset classes: stocks, bills and bonds. Using a Bayesian vector autoregression with an uninformative prior we find that parameter uncertainty raises the annualized long‐run volatilities of all three asset classes proportionally with the same factor relative to volatilities that are conditional on maximum likelihood parameter estimates. As a result, the horizon effect in optimal asset allocations is much weaker compared to models in which only equity returns are subject to parameter uncertainty. Results are sensitive to alternative informative priors, but generally the term structure of risk for stocks and bonds is relatively flat for investment horizons up to 15 years. Copyright © 2013 John Wiley & Sons, Ltd.     

Recent methodological studies on survey questioning

In this article an overview is presented of some 50 methodological studies on survey questioning, published from 1986 until 1987.     

Formal statistics and informal data analysis, or why laziness should be discouraged

The paper gives a personal view on how empirical research and subsequent data analysis and reporting should ideally proceed. Although the introduction concedes that practical limitations and imperfect knowledge usually impose restrictions on our possibilities to approximate this ideal situation, it is argued that investigators and statistical consultants should make a strong effort to come close to it. At the stages of design, analysis, and reporting, this requires careful consideration of the selection of the units of analysis, the choice of variables to be included, their measurement process, and the extent to which relevant assumptions are fulfilled. In so far as exploratory data analysis techniques offer the suggestion that they can produce results without such careful consideration, their use should be discouraged unless they are followed by a confirmatory analysis in which the claims of generalizability are better substantiated. Some protest is raised against the idea that the data themselves have a clear structure lying ready to be unveiled by the investigator by means of some numerical or spatial representation, which would permit to draw general conclusions.     

STATISTICS IN THE SOCIAL AND BEHAVIORAL SCIENCES

This survey paper gives an impression of the main ways in which statistics is used in disciplines such as sociology and psychology. After an introductory section 1 the negative image of social science research is discussed in section 2. Section 3 is devoted to the enormous influence of modern computing facilities. Measurement of human behaviour has its specific problems (section 4). The use of linear and log–linear models is the topic of section 5. Latent variables are a basic concept for social and behavioral scientists, both in some linear models (section 6) and in item response theory (section 7). In the next section multidimensional and optimal scaling techniques are mentioned, and a selection of other topics is the content of section 9. Some general remarks on the generalizability claim of statistical methods constitute the final section. Because of space limitations and priority considerations, the author has decided to write a paper about topics and not about individual research contributions. For this reason there is no list of references (it would take several pages) and no painful split of all Dutch authors into those mentioned and those omitted. In general the Dutch research community has made quite a few major contributions to the area discussed in this paper.     

About handy, handmade and handsome models

Statistical inference deals with natural variation between units and with uncertainty due to sampling, measurement error and random assignment to treatments. Almost always it does so within one model, assumed fixed and valid. Here, however, variation of conclusions due to variation in model choice will be discussed. Heuristics to find appropriate models are seldom taught, and model fit tests and model fit diagnostics are far from optimal. The use of more than one model for the same data is illustrated by examples from Item Response Theory.     

Asking and answering yes/no-questions in survey interviews: a conversational approach

The authors discuss the process of asking and answering yes/no-questions in personal survey interviews from a conversational perspective. They examine the process with regard to yes/no-questions as given in the questionnaire and with regard to the yes/no-questions that interviewers may pose in the subsequent stages of a sequence, when they are probing on the respondent's initial answer or are trying to solve other problems. Hypotheses are derived from the co-operation principle and the politeness principle of conversation, and then empirically evaluated for survey interview settings. In the relatively informal stages of the answering process (subsequences) the conversations appear to go quite well according to the conversation rules, but in the beginning formal stage to a much lesser degree. In particular it has been observed that interviewers strongly prefer to ask one-sided positive yes/no-questions in the subsequences, and, secondly, that respondents usually give agreeing answers to these yes/no-questions. It is argued that this “normal” conversation strategy may seriously affect the validity of the information obtained, and, more generally, that the practical demands from the conversation rules on the interviewer's behaviour set limits on the researcher's abstract demands on that behaviour.

     

An item response model with single peaked item characteristic curves: The PARELLA model

In this paper an item response model (the PARELLA model) designed specifically for the measurement of attitudes and preferences will be introduced. In contrast with the item response models currently used (e.g. the Rasch model and, the two and three parameter logistic model) the item characteristic curve is single peaked instead of monotonically increasing. The model and its properties will be introduced. After this the consequences of these properties for the item writing stage, the evaluation of the concordance of data and model, and the applicability of the model will be discussed. The paper is finished with an example concerning the measurement of change in the attitude in the car-environment issue.