TY - JOUR UR - http://lib.ugent.be/catalog/pug01:8506764 ID - pug01:8506764 LA - eng TI - Hypothesis testing using factor score regression : a comparison of four methods PY - 2016 JO - (2016) EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT SN - 0013-1644 SN - 1552-3888 PB - SAGE Publications 2016 AU - Devlieger, Ines PP01 000080090674 AU - Mayer, Axel UGent 802001746339 AU - Rosseel, Yves PP01 PP54 801000974909 0000-0002-4129-4477 AU - Marcoulides, George A editor AB - In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate. ER -Download RIS file
00000nam^a2200301^i^4500 | |||
001 | 8506764 | ||
005 | 20181113145517.0 | ||
008 | 170202s2016------------------------eng-- | ||
022 | a 0013-1644 | ||
022 | a 1552-3888 | ||
024 | a 000383393500003 2 wos | ||
024 | a 1854/LU-8506764 2 handle | ||
024 | a 10.1177/0013164415607618 2 doi | ||
040 | a UGent | ||
245 | a Hypothesis testing using factor score regression : a comparison of four methods | ||
260 | b SAGE Publications c 2016 | ||
520 | a In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate. | ||
598 | a A1 | ||
700 | a Devlieger, Ines u PP01 0 000080090674 0 802001686826 9 1012316C-F0EE-11E1-A9DE-61C894A0A6B4 | ||
700 | a Mayer, Axel u UGent 0 802001746339 0 976740471392 9 D6733710-8B94-11E3-91E9-018A10BDE39D | ||
700 | a Rosseel, Yves u PP01 u PP54 0 801000974909 0 0000-0002-4129-4477 9 F4D626CE-F0ED-11E1-A9DE-61C894A0A6B4 | ||
700 | a Marcoulides, George A e editor | ||
650 | a Social Sciences | ||
650 | a Mathematics and Statistics | ||
653 | a factor score regression | ||
653 | a bias | ||
653 | a standard error | ||
653 | a standardized parameterization | ||
653 | a unstan- dardized parameterization | ||
773 | t EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT g EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT . 2016. SAGE Publications. 76 (5) p.741-770 q 76:5<741 | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/8506764/file/8506780 z [open] y Devlieger2015 (2).pdf | ||
920 | a article | ||
Z30 | x PP 1 PP01 | ||
922 | a UGENT-PP |
All data below are available with an Open Data Commons Open Database License. You are free to copy, distribute and use the database; to produce works from the database; to modify, transform and build upon the database. As long as you attribute the data sets to the source, publish your adapted database with ODbL license, and keep the dataset open (don't use technical measures such as DRM to restrict access to the database).
The datasets are also available as weekly exports.