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In recent decades, factorial survey experiments (FSEs) have become increasingly widespread and successful for analyzing attitudes and behavioral intentions. FSEs measure the ratings of multidimensional treatments embedded in textual scenarios, which are called vignettes. Analyses of FSEs often assume that these ratings are interval scaled. Past research indicates that this assumption is problematic. Therefore, the following article develops a design for interval scaling tests in FSEs and a method for analyzing FSEs which is sensitive to the scaling level of ratings. An exemplary application of scaling sensitive factorial survey analysis in comparison to standard methods yields effect sizes, which are about a sixth larger with regard to the treatments used in the FSE and about a third larger with respect to influences of between respondent differences. The new method also enables the evaluation of interval in contrast to noninterval scaled rating behavior.

Summary Social workers in criminal justice provide reports to courts, including assessments of the likelihood of re-offending, which are used to assist in judicial decisions. This study used a factorial survey with 93 social workers employed as probation officers to measure factors influencing their judgement of the risk of re-offending. Findings Analysis using regression and analysis of variance showed that judgements about the likelihood of re-offending were influenced by dynamic factors (such as substance misuse, support networks, level of responsibility taken for offending behaviour and cooperation with probation supervision) as well as more widely tested static risk factors (such as previous convictions and age). Application This study highlights a range of dynamic factors that might inform review of criminal justice social work assessment tools which typically incorporate the better-tested static factors. The findings will contribute to current thinking in social work education which is starting to address issues of risk and decision making more explicitly in the curriculum at both qualifying and post-qualifying stages. The more nuanced assessment of factors considered by experienced criminal justice social workers will complement the evidence from more strongly evidenced static risk factors to inform teaching about professional judgements. As we seek to incorporate statistical knowledge into the human processes of social work assessment, Brunskwik's Lens Model and other psycho-social rationality models – which bridge between analytic and descriptive models of human judgement – may be useful conceptualisations of the professional judgement process in social work.

This book presents the results of an empirical study of distributive justice attitudes in the post-Soviet, transforming society of Ukraine. The focus of this study is on the mechanisms of the formation of justice attitudes, which are explained within the methodological framework of analytical sociology. Two perspectives of research were applied in this study - a contextual and a comparative approach - in order to test the hypotheses stemming from a combination of the major statements of huma

This article develops a unified framework, based on Rossi's factorial survey method, for studying positive beliefs and normative judgments. The framework enables estimation of individuals' positive-belief and normative-judgment equations, leading to analysis of the components of beliefs and judgments, assessment of interpersonal variability in the components, and estimation of two further equations, representing, respectively, the determinants and consequences of the components. We describe procedures for data collection, assemble a set of tools for estimating the positive-belief and normative-judgment equations and carrying out the corresponding homogeneity tests, and propose ways of estimating the determinants and consequences equations. To illustrate the framework, we investigate both a positive-belief equation (describing adolescents' views concerning determination of marital happiness) and a normative-judgment equation (describing judgments of the justice of earnings). This article thus provides a guide to contemporary factorial survey analysis, and points the way to its further development.

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.

ObjectiveThis study analyzes which characteristics of pension recipients are taken into account when evaluating the fairness of pensions. Furthermore, it identifies some respondents' characteristics and preferences that could be related to the justice evaluation of different pension amounts.MethodsA factorial survey was designed to simultaneously analyze the association of respondents' and recipients' characteristics with the pensions' justice evaluation.ResultsFindings indicate that although there is a consensual demand for larger pensions, it is still believed that pensions should be allocated primarily based on individual achievement.ConclusionsAlthough in general, larger pensions are on average considered as more just, the justice criteria rely heavily on individual achievement over redistributive considerations, showing willingness to accept very low pensions for those considered not deserving them.

Objective This study used factorial survey data from five countries to assess the factors that shape young adults' attitudes toward the ideal number of children for described couples. Background Continuously low fertility rates in many Asian and European countries generate an interest in understanding the fertility attitudes of young adults-and the implications for family policies. Method The causal impact of socioeconomic and cultural factors on the ideal number of children for couples described in the vignettes was tested using a factorial survey experiment (vignette analysis). Data were collected from Germany, Japan, Russia, Ukraine, and the United Arab Emirates (UAE) to represent five different contexts each with different economies, political regimes, cultural and religious backgrounds, and population structures. Seven vignette-level and four respondent-level factors were assumed to affect the conditional ideal number of children. Results The strongest predictors of the higher ideal number of children for couples described in the vignettes were income, availability of childcare, and husband's full employment. The highest average ideal number of children for described couples was observed in the UAE (2.8 children), followed by Germany (1.6 children), Ukraine and Russia (1.3 children), and Japan (1.2 children). Conclusion The existing gap between public attitudes and fertility behaviors could be addressed by child-friendly policies which allow a better reconciliation of work and family life.

The factorial survey is a quasi-experimental method for collecting data on human judgements. It builds on the use of vignettes (i.e. fictive descriptions), in which a number of variables or dimensions are simultaneously varied in order to be able to assess their relative importance for the respondents judgements. This case provides an account of how we used the factorial survey in a mixed methods research project, whose aim was to model social workers, police officers and prosecutors individual judgements about the seriousness of intimate partner violence. Each respondent in the study (N = 39) judged 100 randomly constructed vignettes portraying situations involving intimate partner violence. The univariate and multivariate statistical analyses based on these data revealed which dimensions influenced a particular respondents judgements about the seriousness of the abuse, how much each of these dimensions affected the judgements and in what ways they shaped the judgements. Data generated by means of interviews and focus groups were used to answer questions about why the respondents had weighted the vignette information in the ways indicated by the statistical analyses. This mixed methods approach to collecting data may be used both for studying professional judgements and for developing professional judgements.

Factorial survey experiments are widely used in the social sciences to study decision-making and attitudes through controlled, experimentally manipulated scenarios—typically presented as text. However, textual vignettes may limit the realism of vignettes and participant en-gagement, motivating the search for alternative approaches. This article explores how gener-ative artificial intelligence (AI) can be used to create photorealistic, customisable images for visual vignettes. It demonstrates techniques for producing and selectively editing images, highlighting their potential addressing the demands of experimental social science research while also addressing key challenges, including ethical considerations, biases inherent in AI tools, and technical limitations. The article showcases potential applications of AI-generated images in social science research and presents survey results on how these images are per-ceived by participants. By critically evaluating both opportunities and challenges, this article provides researchers with practical guidance on integrating AI-generated visuals into factori-al survey experiments, enhancing methodological approaches in social science.

The potentials and pitfalls of factorial survey experiments (FSE) are discussed for empirical tests of theoretical explanations in the sociology of education. The possibilities and limits of FSE are outlined in relation to the internal validity, construct validity, and external validity of the obtained results and illustrated using an example experiment on the decision of university students to study abroad. It is demonstrated that FSE are an enriching complement to laboratory and field experiments, and observational studies.

Factorial Survey Analysis (FSA) is an analytical tool that presents respondents with fictional situations ("vignettes") to be rated or judged. In this paper we study the use of FSA in labour market sociology, with a particular focus on employer-based surveys, and what they can teach us about hiring preferences. FSA is useful in this context as it targets employers directly and comes close to a causal design. This review article seeks to pinpoint the contributions FSA has made to the field, identify its limits and propose topics in which it may be useful.