# ForceChoice Model Theory and Selection Guide

#### Haijiang Qin & Lei Guo

#### 2026-07-22

## Overview

This vignette describes the theoretical foundations of the eight model families in ForceChoice and provides guidance for model selection. Core models support Stan (full Bayesian HMC) and iStEM (iterative Stochastic EM); FCGDINA additionally supports deterministic EM.

The equations below use the parameterizations implemented in the package. For several families, especially MGPCM and MGGUM, this means the notation is not a verbatim copy of the original unidimensional source article. The cited papers identify the model family and response-process assumptions; the implementation details are those exposed by the corresponding `fit.*()` documentation.

## Traditional (Single-Stimulus) Models

### MIRT: Multidimensional Item Response Theory

The MIRT model extends unidimensional IRT to multiple latent dimensions. For binary response \(Y_{ij} \in \{0, 1\}\), the item response function is:

\[ P(Y_{ij}=1 \mid \boldsymbol{\theta}_j) = c_i + (d_i - c_i) \times \frac{1}{1 + \exp[-( \mathbf{a}_i' \boldsymbol{\theta}_j - b_i)]} \]

where \(\mathbf{a}_i = (a_{i1}, \ldots, a_{iD})'\) is the discrimination vector, \(b_i\) is the difficulty (intercept), \(c_i\) is the lower asymptote (pseudo-guessing), and \(d_i\) is the upper asymptote.

Model variants:

Model | Free Parameters | Fixed
--- | --- | ---
M1PL (Rasch) | \(b_i\) | \(a_{id} = q_{id}\), \(c_i=0\), \(d_i=1\)
M2PL | \(a_{id}, b_i\) | \(c_i=0\), \(d_i=1\)
M3PL | \(a_{id}, b_i, c_i\) | \(d_i=1\)
M4PL | \(a_{id}, b_i, c_i, d_i\) | -

When to use: Binary response data where items vary in discrimination and difficulty. Dominance model: higher trait always increases endorsement probability.

Key reference: Reckase, M. D. (2009). Multidimensional Item Response Theory. Springer.

### MGPCM: Multidimensional Generalized Partial Credit Model

For polytomous responses \(Y_{ij} \in \{0, 1, \ldots, K_i-1\}\), ForceChoice implements a multidimensional softmax parameterization:

\[ P(Y_{ij} = k \mid \boldsymbol{\theta}_j) = \frac{\exp\{k\eta_{ij} + d_{ik}\}} {\sum_{r=0}^{K_i-1}\exp\{r\eta_{ij} + d_{ir}\}}, \qquad \eta_{ij}=\sum_{d=1}^{D}a_{id}\theta_{jd}. \]

The \(d_{ik}\) values are category intercepts rather than cumulative step difficulties; \(d_{i0} \equiv 0\) is fixed for identification. This implementation nests the generalized partial credit model of Muraki (1992) in the unidimensional case.

When to use: Polytomous (ordered category) data such as Likert-type responses. A dominance model: higher trait implies higher category.

Key reference: Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.

### MGGUM: Multidimensional Generalized Graded Unfolding Model

The MGGUM is an ideal-point (unfolding) model where endorsement is governed by distance between the person and item locations. In ForceChoice, define

\[ r_{ij} = \left[ \sum_{d=1}^{D} a_{id}^2(\theta_{jd}-\delta_{id})^2 \right]^{1/2}, \qquad S_i = \sum_{d=1}^{D} a_{id}, \]

and let \(\tau_{i0}=0\), \(\psi_{ik}=S_i\sum_{v=0}^{k}\tau_{iv}\), and \(M_i=2K_i-1\). Then

\[ P(Y_{ij}=k \mid \boldsymbol{\theta}_j)= \frac{ \exp\{kr_{ij}-\psi_{ik}\}+ \exp\{(M_i-k)r_{ij}-\psi_{ik}\} }{ \sum_{r=0}^{K_i-1} [\exp\{rr_{ij}-\psi_{ir}\}+ \exp\{(M_i-r)r_{ij}-\psi_{ir}\}] }. \]

The signed Q-matrix (\(q_{id} \in \{-1, 0, 1\}\)) indicates active dimensions and the side of the latent continuum on which the item location lies. The sign controls \(\delta_{id}\), not the sign of the discrimination parameter.

When to use: Attitude/ preference measurement where both ends of the trait continuum can lead to disagreement (e.g., “I like moderate temperatures”: both extreme cold-lovers and heat-lovers disagree).

Key references: - Roberts, J. S., Donoghue, J. R., & Laughlin, J. E. (2000). A general item response theory model for unfolding unidimensional polytomous responses. Applied Psychological Measurement, 24(1), 3–32. - Tu, N., Zhang, B., Angrave, L., Sun, T., & Neuman, M. (2023). Estimating the Multidimensional Generalized Graded Unfolding Model with covariates using a Bayesian approach. Journal of Intelligence, 11, 163.

## Forced-Choice (Comparative) Models

Forced-choice (FC) formats ask respondents to compare items within blocks rather than rating items in isolation. This can reduce response-style biases such as acquiescence, social desirability, and extreme responding, but it introduces comparative/ipsative information with model-specific identification requirements.

### FCMIRT: Forced-Choice MIRT

Item-level endorsement follows the MIRT model (1PL–4PL). At the block level, ForceChoice uses a sequential Luce–Plackett ranking kernel with utilities equal to the logit of the item-level endorsement probability:

\[ u_i(\boldsymbol{\theta}_j) = \log\left[\frac{P_i(\boldsymbol{\theta}_j)} {1-P_i(\boldsymbol{\theta}_j)}\right]. \]

\[ P(i_{(1)} \succ \cdots \succ i_{(K_b)} \mid \boldsymbol{\theta}_j) = \prod_{m=1}^{K_b-1} \frac{\exp\{u_{i_{(m)}}(\boldsymbol{\theta}_j)\}} {\sum_{r=m}^{K_b}\exp\{u_{i_{(r)}}(\boldsymbol{\theta}_j)\}}. \]

Identification: Block difficulties sum to zero: \(\sum_{i \in b} b_i = 0\).

FC types: - RANK: Full ranking of all items within each block - MOLE: Only most and least preferred items identified - PICK: Only most preferred item identified

When to use: Forced-choice questionnaires with dominance items (personality, vocational preferences).

Key reference: Zheng, C., Liu, J., Li, Y., et al. (2024). A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm. Behavior Research Methods, 56, 6363–6388.

Ranking-model references: Luce (1959) and Plackett (1975).

### FCGGUM: Forced-Choice GGUM

Combines the GGUM ideal-point model at the item level with the same sequential Luce–Plackett ranking kernel used by FCMIRT. The block-level ranking probability uses the logit of the GGUM endorsement probability as the statement utility.

Identification: Same block-level sum-to-zero constraint as FCMIRT. Signed Q-matrix indicates both dimension membership and statement direction.

When to use: Forced-choice questionnaires with ideal-point/unfolding items (e.g., personality items where both extremes disagree).

Key reference: Lee, P., Joo, S.-H., Stark, S., & Chernyshenko, O. S. (2018). GGUM-RANK statement and person parameter estimation with multidimensional forced choice triplets. Applied Psychological Measurement, 43(3), 226–240.

### TIRT: Thurstonian IRT for Forced-Choice

The TIRT model works directly with pairwise comparisons via a probit link. For each pair \((i, k)\) in block \(b\), the latent comparative judgment is:

\[ y_{j,ik}^* = -\gamma_{ik} + \lambda_i \mathbf{q}_i'\boldsymbol{\theta}_j - \lambda_k \mathbf{q}_k'\boldsymbol{\theta}_j + \varepsilon_{j,i} - \varepsilon_{j,k} \]

with \(\varepsilon_{j,i} \sim N(0, \psi_i^2)\). The probability of preferring \(i\) over \(k\) is:

\[ P(Y_{j,ik}=1 \mid \boldsymbol{\theta}_j) = \Phi\left(\frac{-\gamma_{ik} + \lambda_i\mathbf{q}_i'\boldsymbol{\theta}_j - \lambda_k\mathbf{q}_k'\boldsymbol{\theta}_j} {\sqrt{\psi_i^2 + \psi_k^2}}\right) \]

Identification constraints: - Case A (\(I_b=2, D>2\)): All \(\psi_i^2 = 0.5\) - Case B (\(D=2, I_b=2\)): First block \(\lambda_i = 0.80\), all \(\psi_i^2 = 0.5\) - General case: Last item per block has \(\psi_i^2 = 1.0\)

When to use: When you want a factor-analytic decomposition of forced-choice data with factor loadings and uniquenesses, rather than the item-level endorsement approach of FCMIRT.

Key reference: Brown, A., & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71(3), 460–502.

### FCDCM: Forced-Choice Diagnostic Classification Model

A higher-order cognitive diagnostic model for paired-comparison FC data. Each block contains exactly two statements. The discrete attribute mastery profile \(\boldsymbol{\alpha}_j\) is governed by a continuous higher-order trait \(\theta_j\):

\[ P(\alpha_{jd} = 1 \mid \theta_j) = \frac{1}{1 + \exp[-(\delta_{1d}\theta_j - \delta_{1d}\delta_{0d})]} \]

Condensation rules: - DINA (conjunctive): \(\zeta_{ij} = \prod_{d: q_{id}=1} \alpha_{jd}\) (all required attributes must be mastered) - DINO (disjunctive): \(\zeta_{ij} = 1 - \prod_{d: q_{id}=1} (1-\alpha_{jd})\) (at least one required attribute mastered)

Block response probabilities are parameterized by \(\eta_{0b}\) (base rate) and \(\eta_{ABb}\) (joint-mastery bonus), with exact marginalization over all \(2^D\) attribute profiles.

When to use: Diagnostic classification with forced-choice format - classifying respondents into discrete attribute mastery profiles while avoiding response biases via the FC format.

Key references: - Huang, H.-Y. (2022). Diagnostic classification model for forced-choice items and noncognitive tests. Educational and Psychological Measurement, 83(1), 146–180. - Zhu, Y.-A., Xu, J., Wang, D., Li, X., Cai, Y., & Tu, D. (2024). A ranking forced choice diagnostic classification model for psychological assessment. British Journal of Mathematical and Statistical Psychology, 78, 617–646.

### FCGDINA: Forced-Choice GDINA

FCGDINA combines a statement-level cognitive diagnostic model with a forced-choice block response model. The statement model may be GDINA, DINA, DINO, or ACDM. For a person with attribute profile \(\boldsymbol{\alpha}_j \in \{0,1\}^D\), a statement-level endorsement probability is computed from the selected CDM design matrix. The block probabilities are then obtained by the same Luce–Plackett transformation used by FCMIRT and FCGGUM.

The likelihood marginalizes over all \(2^D\) attribute profiles:

\[ P(\mathbf{Y}_j) = \sum_{c=1}^{2^D}\pi_c \prod_{b=1}^{B} P(Y_{jb}\mid \boldsymbol{\alpha}_c, \boldsymbol{\delta}, \text{block}_b). \]

Important identification note: Forced-choice data identify within-block utility contrasts, not absolute single-statement endorsement levels. In the Stan implementation, the nonidentified null space of the delta effects is removed and the reported coefficients use the package’s identifying convention. EM and iStEM provide probability-scale CDM estimates under the same block response likelihood.

When to use: Diagnostic classification with ranking, most-least, or best-only forced-choice blocks, especially when the statement-level CDM needs GDINA/ACDM flexibility rather than the paired-comparison FCDCM structure.

Key references: de la Torre (2011) for GDINA; Luce (1959) and Plackett (1975) for the ranking kernel.

## Model Selection Guide

Criterion | Recommended Model
--- | ---
Binary responses, dominance | `fit.MIRT()`
Polytomous (Likert), dominance | `fit.MGPCM()`
Polytomous, ideal-point | `fit.MGGUM()`
FC ranking, dominance items | `fit.FCMIRT()`
FC ranking, ideal-point items | `fit.FCGGUM()`
FC ranking, factor-analytic approach | `fit.TIRT()`
FC paired comparison, diagnostic classification | `fit.FCDCM()`
FC ranking/MOLE/PICK, diagnostic classification | `fit.FCGDINA()`

## Estimation Method Selection

Criterion | Recommended
--- | ---
Final inference, moderate N | Stan
Large datasets, exploratory analysis | iStEM
Need posterior SDs and R-hat | Stan
Need fast point estimates + SEs | iStEM
Complex model, multi-chain diagnostics | Stan
Very large N (> 5000) or many items | iStEM

## Goodness-of-Fit Framework

All models use the limited-information M2 framework (Maydeu-Olivares & Joe, 2005, 2006):

- M2 statistic: Tests the null of exact model fit using univariate and bivariate margins.
- RMSEA: Approximate fit index with 90% confidence interval.
- CFI / TLI: Comparative fit against an independence baseline.
- SRMSR: Standardized root mean square residual.
- Yen’s Q3: Local dependence diagnostics from residual correlations.

For FC models, within-block pairwise residuals are excluded (by design, items in the same block are locally dependent through the ranking mechanism).

## References

Brown, A., & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71(3), 460–502. https://doi.org/10.1177/0013164410375112

de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199. https://doi.org/10.1007/s11336-011-9207-7

Huang, H.-Y. (2022). Diagnostic classification model for forced-choice items and noncognitive tests. Educational and Psychological Measurement, 83(1), 146–180. https://doi.org/10.1177/00131644211069906

Lee, P., Joo, S.-H., Stark, S., & Chernyshenko, O. S. (2018). GGUM-RANK statement and person parameter estimation with multidimensional forced choice triplets. Applied Psychological Measurement, 43(3), 226–240. https://doi.org/10.1177/0146621618768294

Luce, R. D. (1959). Individual choice behavior: A theoretical analysis. Wiley.

Maydeu-Olivares, A., & Joe, H. (2005). Limited- and full-information estimation and goodness-of-fit testing in 2^n contingency tables: A unified framework. Journal of the American Statistical Association, 100(471), 1009–1020. https://doi.org/10.1198/016214504000002069

Maydeu-Olivares, A., & Joe, H. (2006). Limited information goodness-of-fit testing in multidimensional contingency tables. Psychometrika, 71(4), 713–732. https://doi.org/10.1007/s11336-005-1295-9

Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176. https://doi.org/10.1177/014662169201600206

Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C (Applied Statistics), 24(2), 193–202. https://doi.org/10.2307/2346567

Reckase, M. D. (2009). Multidimensional Item Response Theory. Springer. https://doi.org/10.1007/978-0-387-89976-3

Roberts, J. S., Donoghue, J. R., & Laughlin, J. E. (2000). A general item response theory model for unfolding unidimensional polytomous responses. Applied Psychological Measurement, 24(1), 3–32. https://doi.org/10.1177/01466216000241001

Tu, N., Zhang, B., Angrave, L., Sun, T., & Neuman, M. (2023). Estimating the multidimensional generalized graded unfolding model with covariates using a Bayesian approach. Journal of Intelligence, 11(8), 163. https://doi.org/10.3390/jintelligence11080163

Zheng, C., Liu, J., Li, Y., Xu, P., Zhang, B., Wei, R., Zhang, W., Liu, B., & Huang, J. (2024). A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm. Behavior Research Methods, 56(6), 6363–6388. https://doi.org/10.3758/s13428-023-02315-x

Zhu, Y.-A., Xu, J., Wang, D., Li, X., Cai, Y., & Tu, D. (2024). A ranking forced choice diagnostic classification model for psychological assessment using forced choice questionnaires. British Journal of Mathematical and Statistical Psychology, 78(2), 617–646. https://doi.org/10.1111/bmsp.12376