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Advances in Psychological Science    2019, Vol. 27 Issue (1) : 181-189     DOI: 10.3724/SP.J.1042.2019.00181
Research Method |
Alignment: A new method for multiple-group analysis
WEN Congcong1(),WU Weiping1,LIN Guangjie2
1 Overseas Education College/International College, Xiamen University, Xiamen 361102, China
2 School of Journalism and Communication, Xiamen University, Xiamen 361005, China
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Abstract  

Comparing the latent factor means across groups need to test the comparability of the instrument. Researchers usually test the scalar invariance to achieve this goal but often get unsatisfactory results. The alignment is proposed under this background. It allows the comparison of the latent factor means by testing the approximate invariance of the configural model parameters and has become a significant complement to multiple-group CFA. This article gives a detailed description of the multiple-group CFA and alignment, summarizes the research procedures and points to which researchers need to pay attention when applying alignment, uses an undergraduates’ work value data to illustrate how to use alignment to do a research with Mplus. In the end, this article summarizes the advantages and limitations of alignment and reviews its research developments and empirical applications.

Keywords multiple-group analysis      multiple-group CFA      measurement invariance      alignment      Monte Carlo simulation study     
ZTFLH:  B841  
Issue Date: 23 November 2018
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Congcong WEN
Weiping WU
Guangjie LIN
Cite this article:   
Congcong WEN,Weiping WU,Guangjie LIN. Alignment: A new method for multiple-group analysis[J]. Advances in Psychological Science, 2019, 27(1): 181-189.
URL:  
http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2019.00181     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2019/V27/I1/181
维度设计 题号 题项内容
社会责任 Q1 有助于可持续发展和环境保护
Q5 帮助他人
Q9 做对社会发展有重要意义的事情
个人兴趣 Q2 自我发展
Q3 做我感兴趣的事情
Q4 获得一份有保障的工作
Q6 发挥我的天赋和能力
经济动机 Q7 获得高薪的机会
Q8 尽快开始赚钱
  
题项 F1 F2 F3
1. 有助于可持续发展和环境保护 0.767
2. 自我发展 0.742
3. 做我感兴趣的事情 0.833
4. 获得一份有保障的工作 0.625
5. 帮助他人 0.828
6. 发挥我的天赋和能力 0.689
7. 获得高薪的机会 0.671
8. 尽快开始赚钱 0.705
9. 做对社会发展有重要意义的事情 0.717
  
模型 参数数目 卡方值 自由度 p
构置恒定 120 1028.009 96 <0.001
因素载荷恒定 102 1058.646 114 <0.001
截距恒定 84 1120.288 132 <0.001
载荷恒定对构置恒定 - 30.638 18 0.0317
截距恒定对构置恒定 - 92.279 36 <0.001
截距恒定对载荷恒定 - 61.614 18 <0.001
  
参数类型 近似恒定组别 参数类型 近似恒定组别
截距 因素载荷
Y1 1,2,3,4 Y1 1,2,3,4
Y2 1,2,3,4 Y2 1,2,3,4
Y3 1,2,3,4 Y3 1,2,3,4
Y4 1,2,3,4 Y4 1,2,3,4
Y5 1,2,3,4 Y5 1,2,3,4
Y6 1,2,3,4 Y6 1,2,3,4
Y7 1,2,3,4 Y7 1,2,3,4
Y8 1,2,3,4 Y8 1,2,3,4
Y9 1,2,3,4 Y9 1,2,3,4
  
排名 群组号 因素均值 因素均值显著
小于该组
1 4 0.162 2,3,1
2 2 0.038
3 3 0.028
4 1 0.000
  
排名 群组号 因素均值 因素均值显著
小于该组
1 2 0.163 1,4
2 3 0.127 1,4
3 1 0.000
4 4 -0.014
  
均值类别 固定优
化算法
真实值
估计值 自由优
化算法
真实值
估计值
群组1
个人兴趣因素 0.000 0.000 0.314 0.221
社会责任因素 0.000 0.000 -0.081 -0.072
经济因素 0.000 0.000 1.848 0.520
群组2
个人兴趣因素 0.038 0.040 0.351 0.261
社会责任因素 0.075 0.079 -0.004 0.008
经济因素 0.163 0.164 2.009 0.682
群组3
个人兴趣因素 0.028 0.033 0.339 0.252
社会责任因素 0.065 0.070 -0.018 -0.002
经济因素 0.127 0.129 1.975 0.648
群组4
个人兴趣因素 0.162 0.163 0.472 0.381
社会责任因素 -0.005 -0.006 -0.086 -0.077
经济因素 -0.014 -0.021 1.810 0.484
  
因素 固定优化算法
均方误差
自由优化算法
均方误差
均值 标准差 均值 标准差
个人兴趣因素
因素均值 0.036 0.016 0.224 0.179
因素方差 0.046 0.020 0.046 0.020
社会责任因素
因素均值 0.038 0.017 0.184 0.141
因素方差 0.050 0.022 0.050 0.022
经济因素
因素均值 0.041 0.018 1.527 1.139
因素方差 0.060 0.025 0.060 0.025
  
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