The science of attention checks
The science of attention checks Imperfectly attentive respondents are often more representative of the real world than attentive ones. Excluding attention check failures carries severe...
The science of attention checks Imperfectly attentive respondents are often more representative of the real world than attentive ones. Excluding attention check failures carries severe...
Passing respondents back to Qualtrics after a SurveyEngine DCE Many researchers use Qualtrics for their wider survey but need SurveyEngine for the choice experiment component....
Using Ngene-Lite for DCE designs You can now create experiment designs directly in SurveyEngine using Ngene™ Lite. This is preferred choice of professional researchers who...
Password protecting a SurveyEngine survey Adding a password to the beginning of a survey to restrict access. A step-by-step interactive tutorial on password protecting a...
Pilot study design for a discrete choice experiment A pilot study is not optional in DCE research. It is the only way to identify problems...
Cognitive interviewing for DCE survey testing Cognitive interviews reveal how respondents actually interpret your survey - which is often very different from how you intended...
Sample size for a discrete choice experiment Sample size is one of the most common questions in DCE research - and one of the most...
Choosing a panel provider for your DCE Panel quality is the single biggest determinant of DCE data quality that is outside the researcher's direct control....
Reporting DCE results - standards and best practice The PREFS checklist and ISPOR standards set the minimum bar for DCE reporting - but good reporting...
Project planning for a DCE study A DCE study has eight distinct phases each with its own deliverables, dependencies, and risk points. Planning them correctly...
WTP space vs preference space estimation Estimating WTP from the ratio of coefficients produces unreliable confidence intervals. WTP space models estimate WTP directly and give...
Interpreting MNL model results from a DCE MNL output tables contain coefficients, t-ratios, and fit statistics - knowing what each means is the difference between...
Mixed logit - accounting for preference heterogeneity The MNL model assumes everyone values attributes the same way. Mixed logit relaxes this assumption - and typically...
DCE vs other preference elicitation methods DCEs, BWS, ranking tasks, and rating scales all measure preferences but produce different outputs. The choice of method depends...
Choosing attributes and levels for your DCE The attributes and levels in a DCE determine the quality of the preference estimates. Poor attribute selection cannot...
Using Ngene for advanced DCE design NGENE is required for advanced DCE designs - Bayesian priors, complex constraints, generic-specific parameters, and designs SurveyEngine's built-in generator...
What makes a good experimental design? D-efficiency measures how precisely a design estimates preference parameters. Most researchers use it without understanding what drives it. What...
Blocking and versions in DCE experimental designs A full D-efficient design may require 20 or more choice sets - too many for any single respondent....
Detecting straightliners and speeders in Survey data Straightliners and Speeder are a common forms of inattentive responding research. Detecting them early prevents contaminated data from...
Incidence rate monitoring in DCE fieldwork Incidence rate should be stable throughout fieldwork. A rising IR is the clearest early warning sign of organised panel...