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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.

AbstractThis 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.

The similarities between the logistic regression model and the Rasch model (used in psychometric item response theory) are used to derive several methods based on logits that produce parameter estimates for the Rasch model. A result from LeCam and Dzhaparidze is used by which an initial consistent estimate is transformed by one scoring method iteration into an estimate that has the same asymptotic efficiency as the (in this case conditional) maximum likelihood estimate of the item parameters. Indirect evidence about the bias of this CML estimator is produced by studying the (more easily derived) bias of the estimator based on the unweighted logits. Finally, some simple weighted least squares logit‐based estimates are presented, and their performance is assessed. On the whole, the computationally simpler logit‐based estimates give a fairly good approximation to the CML estimates.