Local independence

Local independence refers to the property where the indicators or response variables within a cluster or group are not influenced by each other and are considered independent. This assumption allows for the analysis of the relationship between the observed covariates and the latent process without the confounding effects of inter-indicator dependencies. The assessment of local independence is typically done through a conditional association procedure.From: Statistical and Machine-Learning Data Mining [2017], Longitudinal analysis of public transport usage by older people using a latent Markov model [2023], The Finnish Version of the Affinity for Technology Interaction (ATI) Scale: Psychometric Properties and an Examination of Gender Differences [2023]

Market Segmentation Based on Time-Series Data Using Latent Class Analysis

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Published in Bruce Ratner, Statistical and Machine-Learning Data Mining, 2017

Bruce Ratner

A serious problem with LCA is the assumption of conditional dependence, also known as local independence. Local independence means the indicators within a cluster class are independent of each other. The local independence is sometimes not a tenable assumption. The standard LCA model must be modified to account for this (http://john-uebersax.com/stat/faq.htm).

The Finnish Version of the Affinity for Technology Interaction (ATI) Scale: Psychometric Properties and an Examination of Gender Differences

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Published in International Journal of Human–Computer Interaction, 2023

Ville Heilala, Riitta Kelly, Mirka Saarela, Päivikki Jääskelä, Tommi Kärkkäinen

We utilized non-parametric item response theory (Sijtsma, 2005), namely the monotone homogeneity model (Mokken, 1971), by applying Mokken scale analysis (Sijtsma & van der Ark, 2017) to the scale data. First, we examined the scalability of the scale items using the coefficient H (Appendix Table B1). After reverse coding the reverse-worded items, all scalability coefficients were positive. For individual items, the values were (0.014 < SE < 0.027) exceeding the traditional cutoff value c = 0.3. For item pairs, the values were . The effect of including outliers was small. Local independence was assessed using conditional association procedure (Straat et al., 2016). Without outliers, all items were found to be locally independent. With outliers, flagged the item pair ATI7–ATI1 as locally dependent.

Local independence

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Market Segmentation Based on Time-Series Data Using Latent Class Analysis

The Finnish Version of the Affinity for Technology Interaction (ATI) Scale: Psychometric Properties and an Examination of Gender Differences