गुप्त वर्ग और मिश्रण
8 विधियाँ इस परिवार में।
विशेष रूप से चयनित
अव्यक्त वर्ग विश्लेषण (Latent Class Analysis - LCA)Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It isअव्यक्त प्रोफ़ाइल विश्लेषण (LPA)Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns अव्यक्त संक्रमण विश्लेषणLatent Transition Analysis (LTA) is a method for studying transitions between latent classes over time, developed by Collins and Lanza (2010). LTA combines latent class analysis (gअव्यक्त वर्ग विश्लेषण (LCA)Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of caमिश्रण मॉडलिंगMixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws frसुदृढ़ अव्यक्त वर्ग विश्लेषणRobust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimatio
अध्ययन पथ
इस विषय की सर्वाधिक उद्धृत आधारभूत पद्धतियाँ, उनके विकास के क्रम में — यदि आप यहाँ नए हैं तो आरम्भ करने का स्थान।