Pairwise likelihood estimation and limited-information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling

Jamil, Haziq, Moustaki, Irini and Skinner, Chris J. (2024) Pairwise likelihood estimation and limited-information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling. British Journal of Mathematical and Statistical Psychology. ISSN 0007-1102 (In Press)

Text (GOF_for_complex_data) - Accepted Version Pending embargo until 1 January 2100. Download (1MB) | Request a copy

Abstract

his paper discusses estimation and limited-information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited-information test statistics: the Pearson chi-squared test and the Wald test. To enhance computational efficiency, the paper introduces modifications to both test statistics. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data.

Item Type:	Article
Additional Information:	© 2024
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	17 Sep 2024 08:51
Last Modified:	17 Sep 2024 08:57
URI:	http://eprints.lse.ac.uk/id/eprint/125419

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