Comparison of two factorial analyses

Holzinger, Karl J.; Harman, Harry H.

doi:10.1007/bf02287919

Cited by 59 publications

(27 citation statements)

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“…Subsequent to Spearman's (1904) identification of a general intelligence, g, he developed his two-factor theory (Spearman 1927) and Thurstone (1938) developed his model of primary mental abilities. While Thurstone's (1938) model initially included a factor representing spatial ability which he termed Space, Spearman's model did not and only came to describe the nature of specific factors after further work by Holzinger and his colleagues (Holzinger and Harman 1938;Holzinger and Swineford 1939). The evolving understanding of the factor structure of human intelligence is illustrated chronologically in subsequent models such as those by Burt (1949), Vernon (1950), and Guilford (1966Guilford ( , 1967 and ultimately the use of Thurstone's factor analytic methods with various batteries of psychometric tests resulted in the identification of over 120 primary mental abilities (Carroll 1993).…”

Section: The Origin and Evolution Of Spatial Ability As A Factor Of Hmentioning

confidence: 99%

A Heuristic Framework of Spatial Ability: a Review and Synthesis of Spatial Factor Literature to Support its Translation into STEM Education

2018

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An abundance of empirical evidence exists identifying a significant correlation between spatial ability and educational performance particularly in science, technology, engineering and mathematics (STEM). Despite this evidence, a causal explanation has yet to be identified. Pertinent research illustrates that spatial ability can be developed and that doing so has positive educational effects. However, contention exists within the relevant literature concerning the explicit definition for spatial ability. There is therefore a need to define spatial ability relative to empirical evidence which in this circumstance relates to its factor structure. Substantial empirical evidence supports the existence of unique spatial factors not represented in modern frameworks. Further understanding such factors can support the development of educational interventions to increase their efficacy and related effects in STEM education. It may also lead to the identification of why spatial ability has such a significant impact on STEM educational achievement as examining more factors in practice can help in deducing which are most important. In light of this, a synthesis of the spatial factors offered within existing frameworks with those suggested within contemporary studies is presented to guide further investigation and the translation of spatial ability research to further enhance learning in STEM education.

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Section: The Origin and Evolution Of Spatial Ability As A Factor Of Hmentioning

confidence: 99%

A Heuristic Framework of Spatial Ability: a Review and Synthesis of Spatial Factor Literature to Support its Translation into STEM Education

2018

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“…Herein, we describe these indices and offer a new module which easily facilitates their computa on. co directo (Canivez, 2016;Holzinger & Harman, 1938, Holzinger & Swineford, 1937Reise, 2012;Reise, Morizot, & Hays, 2007) el más adecuado para evaluar simultáneamente la influencia del FG y FEs sobre la variabilidad de cada ítem. De este modo, podría valorarse si es per nente la consideración de un FG de forma exclusiva, o de diferentes FEs, al evaluar determinado instrumento.…”

Section: Introductionunclassified

Índices estadísticos de modelos bifactor

Domínguez-Lara

Rodriguez²

2017

Interacciones

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BY CrossMarkclick for updates iD iD RESUMENMuchos instrumentos se crean con el propósito principal de evaluar sujetos con relación a un solo rasgo. Sin embargo, los rasgos psicológicos son frecuentemente complejos y con enen manifestaciones de dominio específico. Como tal, muchos instrumentos brindan información que son consistentes tanto con las estructuras unidimensionales como las mul dimensionales. Por desgracia, muchas veces, los inves gadores aplicados hacen determinaciones sobre la estructura final basado únicamente en los índices de ajuste obtenidos a par r de modelos de ecuaciones estructurales. Dado que los índices de ajuste generalmente favorecen al modelo bifactor sobre los modelos de medición compe dores, es impera vo que los inves gadores hacen uso de la información disponible que los modelos bifactor enen para ofrecer con el fin de calcular los índices informavos incluyendo coeficientes de confiabilidad omega, confiabilidad del constructo, varianza común explicada, y el porcentaje de correlaciones no contaminadas. Dichos índices proporcionan información acerca de la fuerza tanto de los factores generales como de los factores específicos con el fin de sacar conclusiones acerca de la dimensionalidad y la puntuación global de las escalas (y subescalas). En este documento, se describen estos índices y ofrecen un nuevo módulo que facilita su cálculo. PALABRAS CLAVE Análisis factorial confirmatorio;Bifactor;Omega; Confiabilidad del constructo;Varianza común explicada;Porcentaje de correlaciones no contaminadas. ISSN 2411-5940 (impreso) / e-ISSN 2413 Obra bajo licencia de

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“…L.L. Thurstone's application of the centroid method of factor analysis (1938) found no g and no s but several primary mental abýýJties which he asserted were unique and not dependent on g. Spearman (1939) reanalyzed Thurstone's data and located g, as did Holzinger and Harman (1938). Thurstone then spent many years trying to develop pure measures of distinct abilities, but these efforts were in vain.…”

Section: Introductionmentioning

confidence: 99%