Item Count Technique | ScienceGate

In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the violation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respectively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence intervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly.

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A Meta-analysis of the Relative Effectiveness of the Item Count Technique Compared to Direct Questioning

Sociological Methods & Research ◽

10.1177/0049124119882468 ◽

2019 ◽

pp. 004912411988246

Author(s):

Jiayuan Li ◽

Wim Van den Noortgate

Keyword(s):

Meta Analysis ◽

Relative Effectiveness ◽

Cultural Orientation ◽

Effect Sizes ◽

Discussion Section ◽

Survey Experiments ◽

Item Count Technique

This article presents an updated meta-analysis of survey experiments comparing the performance of the item count technique (ICT) and the direct questioning method. After synthesizing 246 effect sizes from 54 studies, we find that the probability that a sensitive item will be selected is .089 higher when using ICT compared to direct questioning. In recognition of the heterogeneity across studies, we seek to explain this variation by means of moderator analyses. We find that the relative effectiveness of ICT is moderated by cultural orientation in the context in which ICT is conducted (collectivism vs. individualism), the valence of topics involved in the applications (socially desirable vs. socially undesirable), and the number of nonkey items. In the Discussion section, we elaborate on the methodological implications of the main findings.

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Measuring Social Desirability Effects on Self-Reported Turnout Using the Item Count Technique

International Journal of Public Opinion Research ◽

10.1093/ijpor/eds019 ◽

2012 ◽

Vol 25 (2) ◽

pp. 153-172 ◽

Cited By ~ 15

Author(s):

M. Comsa ◽

C. Postelnicu

Keyword(s):

Social Desirability ◽

Item Count Technique

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The generalization of the non-randomized parallel model and item count technique in surveys with sensitive questions

10.5353/th_b5576775 ◽

2015 ◽

Author(s):

Yin Liu

Keyword(s):

Parallel Model ◽

Sensitive Questions ◽

Item Count Technique

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Optimal Allocation of the Sample in the Poisson Item Count Technique

Acta Universitatis Lodziensis Folia oeconomica ◽

10.18778/0208-6018.335.03 ◽

2018 ◽

Vol 3 (335) ◽

pp. 35-47

Author(s):

Michał Bernardelli ◽

Barbara Kowalczyk

Keyword(s):

Em Algorithm ◽

Optimal Allocation ◽

Practical Importance ◽

Analytical Formula ◽

Ml Estimation ◽

Treatment Groups ◽

The Em Algorithm ◽

Expectation Maximisation ◽

Ml Estimator ◽

Item Count Technique

Indirect methods of questioning are of utmost importance when dealing with sensitive questions. This paper refers to the new indirect method introduced by Tian et al. (2014) and examines the optimal allocation of the sample to control and treatment groups. If determining the optimal allocation is based on the variance formula for the method of moments (difference in means) estimator of the sensitive proportion, the solution is quite straightforward and was given in Tian et al. (2014). However, maximum likelihood (ML) estimation is known from much better properties, therefore determining the optimal allocation based on ML estimators has more practical importance. This problem is nontrivial because in the Poisson item count technique the study sensitive variable is a latent one and is not directly observable. Thus ML estimation is carried out by using the expectation‑maximisation (EM) algorithm and therefore an explicit analytical formula for the variance of the ML estimator of the sensitive proportion is not obtained. To determine the optimal allocation of the sample based on ML estimation, comprehensive Monte Carlo simulations and the EM algorithm have been employed.

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