\(^1\) University of Trento, Rovereto (IT), \(^2\) Psicostat, Padova (IT) \(^3\) University of Padova, Padova (IT)
Many items \(\rightarrow\) good measurement precision, great reliability and so on
Not always!
People might get tired & frustrated
\[Q \subset B\]
Item Response Theory models for the win
Being focused on the item information and on the ability of each item to measure different levels of the latent trait, IRT models provide an ideal framework for developing STF (and not torturing people)
Automated test assembly and maxmin algorithms
AIM
Size matters: How well can we estimate the latent trait with less and less items?
\[P(x_{pi}= 1| \theta_p, b_i, a_i, c_i, d_i) = c_i + (d_i -c_i) \dfrac{\exp[a_i(\theta_p - b_i)]}{1 + \exp[a_i(\theta_p - b_i)]}\]
\[ \text{IIF}_{i}(\theta) = \dfrac{a_i^2[P(\theta)-c_i]^2[d_i - P(\theta)]^2}{(d_{i}-c_i)^2 P(\theta)Q(\theta)}\]
\[TIF = \sum_{i = 1}^{||B||} IIF_i\] (\(B\): Set of items in a test (\(||X||\) cardinality of set \(X\)))
\(d\) depends on the \(r\) rank of the item presentation during the administration, \(d_r\):
\[\text{TIF}^*\]
\[k = 0\]
\[k = 0\]
\[k = 0\]
\[k = 0\]
\[k = 0\]
\[k = 0\]
\[k = 1\]
\[k = 1\]
\[k = 1\]
\[k = 1\]
Frank
At \(k = 0\): \(\text{TIF}^0(\theta) = 0 \, \forall \theta\), \(Q^0 = \emptyset\).
For \(k \geq 0\),
\(i^* = \arg \min_{i \in A^k} (|\text{TIF}^* - \text{pTIF}_i^k|)\)
Termination criterion: \(|\text{TIF}^* - \text{pTIF}_{i^*}^k| \geq |\text{TIF}_B - \text{TIF}^{k}|\):
FALSE: \(Q^{k+1} = Q^{k} \cup \{i^*\}\), \(\text{TIF}^{k+1} = p\text{TIF}_{i^*}\), iterates 1-4
TRUE: Stop, \(Q_{\text{Frank}} = Q^k\)
Léon
At \(k = 0\): \(\text{TIF}^0(\theta) = 0 \, \forall \theta\), \(Q^0 = \emptyset\).
For \(k \geq 0\),
\(i^* = \arg \min_{i \in A^k} (|\text{TIF}^* - \text{pTIF}_i^k|)\)
Termination criterion: \(|\text{TIF}^* - \text{pTIF}_{i^*}^k| \geq |\text{TIF}_B - \text{TIF}^{k}|\):
FALSE: \(Q^{k+1} = Q^{k} \cup \{i^*\}\), \(\text{TIF}^{k+1} = p\text{TIF}_{i^*}\), iterates 1-4
TRUE: Stop, \(Q_{\text{Léon}} = Q^k\)
1000 respondents with \(\theta \sim \mathcal{U}(-3,3)\)
Item bank \(B\) of 70 items:
\(b \sim \mathcal{U}(-3, 3)\)
\(a \sim \mathcal{U}(.90, 2.0)\)
\(c_i = 0\), \(\forall i \in B\)
\(d_r = \exp(-0.01 r)\), with \(r = \{0, \ldots, ||B|| -1\}\)
\(\text{TIF}^* = \sum_{i = 1}^{||B||} \frac{\text{IIF}_i}{||B||}\), with \(d_i = 1\), \(\forall i \in B\) 🥇
Considering \(TIF^*\), \(B\), and \(d_r\), Léon and Frank are applied to find the best \(Q \subset B\) in 100 replications
Plot twist! There is a minimum number of items: 10%, 25%, 50% of \(||B||\)
Tip
Acknowledging for the response fatigue during the item selection itself helps to find the item selection able to minimize the distance from the target
Warning
The order of the items selected by Léon cannot be randomized
ottavia.epifania@untin.it
AIP 2025, Torino