In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.

In: Sociological Methods & Research

Abstract:
The factorial survey is an experimental design consisting of varying situations (vignettes) that have to be judged by respondents. For more complex research questions, it quickly becomes impossible for an individual respondent to judge all vignettes. To overcome this problem, random designs are recommended most of the time, whereas quota designs are not discussed at all. First comparisons of random designs with fractional factorial and D-efficient designs are based on fictitious data, first comparisons with fractional factorial and confounded factorial designs are restricted to theoretical considerations. The aim of this contribution is to compare different designs regarding their reliability and their internal validity. The benchmark for the empirical comparison is established by the estimators from a parsimonious full factorial design, each answered by a sample of 132 students (real instead of fictitious data). Multilevel analyses confirm that, if they exist, balanced confounded factorial designs are ideal. A confounded D-efficient design, as proposed for the first time in this article, is also superior to simple random designs.