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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Yazd University</PublisherName>
				<JournalTitle>Journal of Statistical Modelling: Theory and Applications</JournalTitle>
				<Issn>2676-7392</Issn>
				<Volume>7</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>01</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Simulation-based path analysis for qualitative mediators‎: ‎A continuous proxy approach</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>37</FirstPage>
			<LastPage>53</LastPage>
			<ELocationID EIdType="pii">4273</ELocationID>
			
<ELocationID EIdType="doi">10.22034/jsmta.2026.24305.1211</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Alireza</FirstName>
					<LastName>Pakgohar</LastName>
<Affiliation>Departent of Statistics‎, ‎Payame Noor University‎, ‎Tehran‎, ‎Iran</Affiliation>
<Identifier Source="ORCID">0000-0003-2512-1581</Identifier>

</Author>
<Author>
					<FirstName>Mohadeseh</FirstName>
					<LastName>Khalili</LastName>
<Affiliation>Department of Engineering, University of Ardakan, Ardakan, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2026</Year>
					<Month>02</Month>
					<Day>08</Day>
				</PubDate>
			</History>
		<Abstract>Conventional path analysis relies on ordinary least squares assumptions, requiring continuous variables. When mediators are qualitative, researchers typically resort to logistic regression, creating a scale incompatibility problem in which coefficients cannot be directly multiplied to estimate indirect effects. This study proposes a Categorical-to-Continuous Signal Transformation technique to address this limitation. Unlike standard dummy coding, we introduce a simulation-based algorithm that generates a continuous proxy variable for the qualitative mediator. By extracting the probabilistic centroid of each category using the group mean and standard error of the dependent variable, we simulate a distribution that retains the structural signal while satisfying ordinary least squares normality assumptions. The method was validated using both primary and synthetic datasets. Results demonstrate that the simulated proxy variables successfully recover the directional pathways and maintain statistical significance consistent with classical analysis of variance. The approach effectively captures the latent influence of categories without the bias of individual-level noise. The proposed simulation method offers a robust alternative for mediation analysis involving qualitative variables. By bridging the gap between categorical data and linear modeling, it enables the direct calculation of indirect effects within a unified ordinary least squares framework, eliminating the need for complex non-linear approximations.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Categorical variable</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Indirect effect</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Mediation analysis</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Normal distribution simulation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Path analysis</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jsm.yazd.ac.ir/article_4273_66ce5822f0c1d881db978154311596ca.pdf</ArchiveCopySource>
</Article>
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