Factor analysis using column cross-validation

1989 ◽  
Vol 13 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Marius D'Amboise ◽  
Benoit Lagarde
Keyword(s):  
Factor Analysis ◽  
Cross Validation

scholarly journals Consistency and overfitting of multi-omics methods on experimental data

2019 ◽  
Vol 21 (4) ◽  
pp. 1277-1284 ◽  
Author(s):  
Sean D McCabe ◽  
Dan-Yu Lin ◽  
Michael I Love
Keyword(s):  
Experimental Data ◽  
Factor Analysis ◽  
Canonical Correlation ◽  
Cross Validation ◽  
Small Sample Size ◽  
R Package ◽  
Small Sample ◽  
Ready Availability ◽  
Variation Explained ◽  
The Relationship

Abstract Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

A Confirmatory Factor Analysis of the WISC-III in a Clinical Sample With Cross-validation in the Standardization Sample

2001 ◽  
Vol 7 (2) ◽  
pp. 104-116 ◽  
Author(s):  
D. Bradley Burton ◽  
Arash Sepehri ◽  
Fred Hecht ◽  
Anekke VandenBroek ◽  
Joseph J. Ryan ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cross Validation ◽  
Clinical Sample ◽  
Standardization Sample ◽  
Confirmatory Factor

scholarly journals Escala de Comunicación autopercibida en la relación de pareja (CARP)

2019 ◽  
Vol 35 (2) ◽  
pp. 314-322
Author(s):  
Maria-Teresa Iglesias-García ◽  
Antonio Urbano-Contreras ◽  
Raquel-Amaya Martínez-González
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Cross Validation ◽  
Couple Relationship ◽  
Good Reliability ◽  
Conflict Situations ◽  
Confirmatory Factor ◽  
Two Factors

Este estudio busca construir y validar la Escala de Comunicación autopercibida en la relación de pareja (CARP) con el fin de ofrecer un instrumento sencillo y útil. Participaron 620 personas que mantenían una relación de pareja. Para estudiar la estructura factorial de la escala se dividió aleatoriamente la muestra en dos submuestras, realizándose una validación cruzada mediante análisis factorial exploratorio (AFE) y análisis factorial confirmatorio (AFC). Asimismo, para comprobar que el modelo se mantenía estable al tener en cuenta la variable sexo, se repitió el análisis factorial confirmatorio con las submuestras de mujeres y de hombres y se aplicó un AFC Multigrupo para comprobar la invarianza factorial en función de esta variable. Se ha obtenido una escala de 8 ítems constituida por dos factores que explican el 46.6% de la varianza y que presenta una buena fiabilidad (α = .75), comprobándose la invarianza estricta en función del sexo. Esta escala puede ser útil en el campo de la detección, prevención e intervención en situaciones de conflicto entre la pareja. This study aims to design and validate the Scale of Self-perceived Communication in the Couple Relationship (SCCR) in order to provide a straightforward and useful instrument. 620 persons who were in a couple relationship took part in this study. The sample was divided randomly into two subsamples to study the factor structure of the scale, carrying out a cross-validation by using an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA). Also, and to verify that the model remained stable taking account of the variable gender, the confirmatory factor analysis was repeated with the women and men subsamples, and a multigroup CFA was carried out to check the factor invariance according to this variable An 8-items scale was obtained, made up with two factors explaining 46.6% of the variance who also reported a good reliability (α = .75), testing the strict invariance according to the gender. This scale might be useful in the field of detection, prevention and intervention of conflict situations in the couple relationship.

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