Advances in Psychological Science ›› 2020, Vol. 28 ›› Issue (4): 673-680.doi: 10.3724/SP.J.1042.2020.00673
• Research Method • Previous Articles
Received:
2019-08-09
Online:
2020-04-15
Published:
2020-02-24
Contact:
ZHANG Minqiang
E-mail:2640726401@qq.com
CLC Number:
FANG Junyan, ZHANG Minqiang. What is the minimum number of effect sizes required in meta-regression? An estimation based on statistical power and estimation precision[J]. Advances in Psychological Science, 2020, 28(4): 673-680.
检验方法 | τ2 = 0.08 | ||||||
---|---|---|---|---|---|---|---|
β为0 | β(均)较小 | β(均)较大 | β一个较大, 一个较小 | ||||
β = 0 | β = (0, 0) | β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5,0.5) | β = (0.2,0.5) | |
Knha-test | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
z-test | 23 | 25 | 23 | 25 | 23 | 25 | 25 |
检验方法 | τ2 = 0.08 | ||||||
---|---|---|---|---|---|---|---|
β为0 | β(均)较小 | β(均)较大 | β一个较大, 一个较小 | ||||
β = 0 | β = (0, 0) | β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5,0.5) | β = (0.2,0.5) | |
Knha-test | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
z-test | 23 | 25 | 23 | 25 | 23 | 25 | 25 |
检验方法 | τ2=0.32 | ||||||
---|---|---|---|---|---|---|---|
β为0 | β(均)较小 | β(均)较大 | β一个较大, 一个较小 | ||||
β = 0 | β = (0,0) | β = 0.2 | β = (0.2,0.2) | β = 0.5 | β = (0.5,0.5) | β = (0.2,0.5) | |
Knha-test | 38 | 38 | 38 | 38 | 38 | 38 | 38 |
z-test | 43 | 43 | 43 | 43 | 43 | 43 | 43 |
检验方法 | τ2=0.32 | ||||||
---|---|---|---|---|---|---|---|
β为0 | β(均)较小 | β(均)较大 | β一个较大, 一个较小 | ||||
β = 0 | β = (0,0) | β = 0.2 | β = (0.2,0.2) | β = 0.5 | β = (0.5,0.5) | β = (0.2,0.5) | |
Knha-test | 38 | 38 | 38 | 38 | 38 | 38 | 38 |
z-test | 43 | 43 | 43 | 43 | 43 | 43 | 43 |
检验 方法 | τ2 = 0.08 | τ2 = 0.32 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
β(均)较小 | β(均)较大 | β一个较大 一个较小 | β(均)较小 | β(均)较大 | β一个较大 一个较小 | |||||
β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5, 0.5) | β = (0.2, 0.5) | β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5, 0.5) | β = (0.2, 0.5) | |
Knha-test | 30 | 30 | √ | √ | 20 | 70 | 70 | 20 | 20 | 50 |
z-test | 38 | 38 | √ | √ | 30 | 80 | 80 | 20 | 20 | 52 |
检验 方法 | τ2 = 0.08 | τ2 = 0.32 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
β(均)较小 | β(均)较大 | β一个较大 一个较小 | β(均)较小 | β(均)较大 | β一个较大 一个较小 | |||||
β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5, 0.5) | β = (0.2, 0.5) | β = 0.2 | β = (0.2, 0.2) | β = 0.5 | β = (0.5, 0.5) | β = (0.2, 0.5) | |
Knha-test | 30 | 30 | √ | √ | 20 | 70 | 70 | 20 | 20 | 50 |
z-test | 38 | 38 | √ | √ | 30 | 80 | 80 | 20 | 20 | 52 |
回归系数取值 | 剩余异质性较小 | 剩余异质性较大 | ||
---|---|---|---|---|
包含一个调节变量 | 包含两个调节变量 | 包含一个调节变量 | 包含两个调节变量 | |
β(均)为0 | 20 | 20 | 38 | 38 |
β(均)较小 | 30 | 30 | 70 | 70 |
β(均)较大 | 20 | 20 | 38 | 38 |
β1较小β2较大 | —— | 20 | —— | 50 |
回归系数取值 | 剩余异质性较小 | 剩余异质性较大 | ||
---|---|---|---|---|
包含一个调节变量 | 包含两个调节变量 | 包含一个调节变量 | 包含两个调节变量 | |
β(均)为0 | 20 | 20 | 38 | 38 |
β(均)较小 | 30 | 30 | 70 | 70 |
β(均)较大 | 20 | 20 | 38 | 38 |
β1较小β2较大 | —— | 20 | —— | 50 |
回归系数取值 | 剩余异质性较小 | 剩余异质性较大 | ||
---|---|---|---|---|
包含一个调节变量 | 包含两个调节变量 | 包含一个调节变量 | 包含两个调节变量 | |
β(均)为0 | 23 | 25 | 43 | 43 |
β(均)较小 | 38 | 38 | 80 | 80 |
β(均)较大 | 23 | 25 | 43 | 43 |
β1较小β2较大 | —— | 30 | —— | 52 |
回归系数取值 | 剩余异质性较小 | 剩余异质性较大 | ||
---|---|---|---|---|
包含一个调节变量 | 包含两个调节变量 | 包含一个调节变量 | 包含两个调节变量 | |
β(均)为0 | 23 | 25 | 43 | 43 |
β(均)较小 | 38 | 38 | 80 | 80 |
β(均)较大 | 23 | 25 | 43 | 43 |
β1较小β2较大 | —— | 30 | —— | 52 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||
---|---|---|---|---|---|---|
β = 0 | β = 0.2 | β = 0.5 | β = 0 | β = 0.2 | β = 0.5 | |
20 | -0.0004 | -0.0001 | 0.0003 | -0.0012 | 0.0008 | 0.0056 |
40 | 0.0009 | 0.0004 | -0.0001 | -0.0031 | -0.0011 | -0.0009 |
60 | 0.0006 | 0.0000 | -0.0009 | 0.0010 | -0.0014 | -0.0009 |
80 | 0.0004 | -0.0003 | -0.0003 | 0.0000 | 0.0003 | -0.0005 |
100 | -0.0007 | 0.0000 | 0.0002 | -0.0006 | 0.0004 | 0.0000 |
120 | 0.0000 | 0.0000 | -0.0004 | 0.0003 | -0.0001 | 0.0002 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||
---|---|---|---|---|---|---|
β = 0 | β = 0.2 | β = 0.5 | β = 0 | β = 0.2 | β = 0.5 | |
20 | -0.0004 | -0.0001 | 0.0003 | -0.0012 | 0.0008 | 0.0056 |
40 | 0.0009 | 0.0004 | -0.0001 | -0.0031 | -0.0011 | -0.0009 |
60 | 0.0006 | 0.0000 | -0.0009 | 0.0010 | -0.0014 | -0.0009 |
80 | 0.0004 | -0.0003 | -0.0003 | 0.0000 | 0.0003 | -0.0005 |
100 | -0.0007 | 0.0000 | 0.0002 | -0.0006 | 0.0004 | 0.0000 |
120 | 0.0000 | 0.0000 | -0.0004 | 0.0003 | -0.0001 | 0.0002 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||
---|---|---|---|---|---|---|
β = 0 | β = 0.2 | β = 0.5 | β = 0 | β = 0.2 | β = 0.5 | |
20 | 0.0000 | 0.0000 | 0.0003 | 0.0026 | 0.0005 | -0.0009 |
40 | 0.0007 | 0.0004 | -0.0009 | 0.0000 | -0.0004 | 0.0003 |
60 | -0.0003 | 0.0007 | -0.0001 | -0.0005 | 0.0000 | 0.0003 |
80 | 0.0000 | 0.0001 | 0.0008 | 0.0001 | 0.0013 | 0.0017 |
100 | -0.0002 | 0.0001 | -0.0001 | 0.0005 | -0.0009 | -0.0014 |
120 | 0.0001 | 0.0003 | -0.0006 | 0.0007 | 0.0000 | 0.0002 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||
---|---|---|---|---|---|---|
β = 0 | β = 0.2 | β = 0.5 | β = 0 | β = 0.2 | β = 0.5 | |
20 | 0.0000 | 0.0000 | 0.0003 | 0.0026 | 0.0005 | -0.0009 |
40 | 0.0007 | 0.0004 | -0.0009 | 0.0000 | -0.0004 | 0.0003 |
60 | -0.0003 | 0.0007 | -0.0001 | -0.0005 | 0.0000 | 0.0003 |
80 | 0.0000 | 0.0001 | 0.0008 | 0.0001 | 0.0013 | 0.0017 |
100 | -0.0002 | 0.0001 | -0.0001 | 0.0005 | -0.0009 | -0.0014 |
120 | 0.0001 | 0.0003 | -0.0006 | 0.0007 | 0.0000 | 0.0002 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||||
---|---|---|---|---|---|---|---|---|
β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | |
20 | -0.0008 | 0.0006 | -0.0007 | 0.0003 | 0.0001 | -0.0007 | 0.0003 | -0.0005 |
40 | 0.0003 | 0.0006 | -0.0001 | 0.0008 | -0.0008 | 0.0001 | -0.0017 | -0.0005 |
60 | 0.0000 | -0.0006 | -0.0003 | 0.0000 | 0.0004 | 0.0001 | 0.0003 | -0.0002 |
80 | 0.0001 | 0.0002 | -0.0004 | 0.0001 | 0.0005 | 0.0005 | 0.0006 | 0.0001 |
100 | -0.0002 | 0.0002 | -0.0003 | 0.0001 | 0.0004 | 0.0006 | -0.0003 | 0.0002 |
120 | 0.0000 | 0.0001 | 0.0001 | -0.0001 | -0.0003 | 0.0000 | 0.0002 | 0.0004 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||||
---|---|---|---|---|---|---|---|---|
β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | |
20 | -0.0008 | 0.0006 | -0.0007 | 0.0003 | 0.0001 | -0.0007 | 0.0003 | -0.0005 |
40 | 0.0003 | 0.0006 | -0.0001 | 0.0008 | -0.0008 | 0.0001 | -0.0017 | -0.0005 |
60 | 0.0000 | -0.0006 | -0.0003 | 0.0000 | 0.0004 | 0.0001 | 0.0003 | -0.0002 |
80 | 0.0001 | 0.0002 | -0.0004 | 0.0001 | 0.0005 | 0.0005 | 0.0006 | 0.0001 |
100 | -0.0002 | 0.0002 | -0.0003 | 0.0001 | 0.0004 | 0.0006 | -0.0003 | 0.0002 |
120 | 0.0000 | 0.0001 | 0.0001 | -0.0001 | -0.0003 | 0.0000 | 0.0002 | 0.0004 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||||
---|---|---|---|---|---|---|---|---|
β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | |
20 | -0.0003 | -0.0002 | 0.0009 | 0.0006 | 0.0005 | -0.0002 | 0.0000 | 0.0000 |
40 | -0.0008 | -0.0001 | 0.0000 | -0.0002 | 0.0002 | -0.0002 | 0.0002 | 0.0010 |
60 | 0.0001 | -0.0002 | -0.0004 | -0.0001 | 0.0010 | 0.0002 | 0.0005 | 0.0001 |
80 | 0.0000 | -0.0003 | -0.0001 | 0.0002 | 0.0005 | -0.0002 | -0.0001 | 0.0001 |
100 | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.0006 | -0.0003 | 0.0000 | 0.0002 |
120 | 0.0001 | 0.0002 | 0.0005 | -0.0001 | 0.0005 | 0.0005 | 0.0000 | 0.0004 |
k | τ2 = 0.08 | τ2 = 0.32 | ||||||
---|---|---|---|---|---|---|---|---|
β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | β = (0, 0) | β = (0.2, 0.2) | β = (0.5, 0.5) | β = (0.2, 0.5) | |
20 | -0.0003 | -0.0002 | 0.0009 | 0.0006 | 0.0005 | -0.0002 | 0.0000 | 0.0000 |
40 | -0.0008 | -0.0001 | 0.0000 | -0.0002 | 0.0002 | -0.0002 | 0.0002 | 0.0010 |
60 | 0.0001 | -0.0002 | -0.0004 | -0.0001 | 0.0010 | 0.0002 | 0.0005 | 0.0001 |
80 | 0.0000 | -0.0003 | -0.0001 | 0.0002 | 0.0005 | -0.0002 | -0.0001 | 0.0001 |
100 | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.0006 | -0.0003 | 0.0000 | 0.0002 |
120 | 0.0001 | 0.0002 | 0.0005 | -0.0001 | 0.0005 | 0.0005 | 0.0000 | 0.0004 |
1 | 董圣杰, 曾宪涛, 郭毅 . ( 2012). R软件Metafor程序包在Meta分析中的应用. 中国循证医学杂志, 12( 9), 1141-1147. |
2 | 方杰, 张敏强 . ( 2012). 中介效应的点估计和区间估计:乘积分布法、非参数Bootstrap和MCMC法. 心理学报, 44( 10), 1408-1420. |
3 | 刘俊, 秦传燕 . ( 2018). 企业社会责任与员工绩效的关系:一项元分析. 心理科学进展, 26( 7), 1152-1164. |
4 | 王超, 袁蒙蒙, 姜媛, 方平 . ( 2019). 宜人性对企业家成就的影响:来自元分析的证据. 心理与行为研究, 17( 1), 126-133. |
5 | 张天嵩, 刘江波, 钟文昭 . ( 2009). Stata在探索异质性来源—Meta回归分析中的应用. 循证医学, 9( 1), 48-50. |
6 | 张云权, 马露, 冯仁杰, 朱耀辉, 李存禄 . ( 2015). 模型回归系数的合并分析在R软件metafor包中的实现. 中国循证医学杂志, 15( 3), 367-372. |
7 | Borenstein M., Hedges L. V., Higgins J. P. T., & Rothstein H. R . ( 2009). Introduction to meta-analysis. UK: John Wiley & Sons, Ltd., Publication. |
8 | Borenstein M., Hedges L. V., Higgins J. P.T., & Rothstein H. R . ( 2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1( 2), 97-111. |
9 | Cafri G., Kromrey J. D., & Brannick M. T . ( 2010). A meta-analysis: Empirical review of statistical power, type I error rates, effect sizes, and model selection of meta-analyses published in psychology. Multivariate Behavioral Research, 45( 2), 239-270. |
10 | Card N. A. (2012). Applied meta-analysis for social science research. New York: The Guilford Press. |
11 | Cheung, M. W. L., & Vijayakumar, R . ( 2016). A guide to conducting a meta-analysis. Neuropsychology Review, 26( 2), 121-128. |
12 | Çoğaltay N., & Karadağ E. . ( 2015). Introduction to meta-analysis. In E. Karadağ (Eds.), Leadership and Organizational Outcomes (2nd ed, pp. 19-28). Switzerland: Springer International Publishing. |
13 | Cohn, L. D., & Becker, B. J . ( 2003). How meta-analysis increases statistical power. Psychological Methods, 8( 3), 243-253. |
14 | Field, A. P . ( 2001). Meta-analysis of correlation coefficients: A Monte Carlo comparison of fixed- and random-effects methods. Psychological Methods, 6( 2), 161-180. |
15 | Glass, G. V . ( 1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5( 10), 3-8. |
16 | Huizenga H. M., Visser I., & Dolan C. V . ( 2010). Testing overall and moderator effects in random effects meta-regression. British Journal of Mathematical and Statistical Psychology, 64, 1-19. |
17 | Knapp, G., & Hartung, G . ( 2003). Improved tests for a random effects meta-regression with a single covariate. Statistics in Medicine, 22, 2693-2710. |
18 | López-López J. A., Noortgate W. V. D., Tanner-Smith E. E., Wilson S. J., & Lipsey M. W . ( 2017). Assessing meta- regression methods for examining moderator relationships with dependent effect sizes: A Monte Carlo simulation. Research Synthesis Methods, 8( 4), 435-450. |
19 | Murphy, K. R . ( 2017). What inferences can and cannot be made on the basis of meta-analysis? Human Resource Management Review, 27( 1), 193-200. |
20 | Schmidt, F. L . ( 2017). Statistical and measurement pitfalls in the use of meta-regression in meta-analysis. Career Development International, 22( 5), 469-476. |
21 | Steel, P. D., & Kammeyer-Mueller, J. D . ( 2002). Comparing meta-analytic moderator estimation techniques under realistic conditions. Journal of Applied Psychology, 87( 1), 96-111. |
22 | Suchotzki K., Verschuere B., Bockstaele B. V., Ben-Shakhar G., & Crombez G . ( 2017). Lying takes time: A meta-analysis on reaction time measures of deception. Psychological Bulletin, 143( 4), 428-453. |
23 | Valentine J. C., Pigott T. D., & Rothstein H. R . ( 2010). How many studies do you need? A primer on statistical power for meta-analysis. Journal of Educational & Behavioral Statistics, 35( 2), 215-247. |
24 | Viechtbauer, W . ( 2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36, 1-48. |
25 | Viechtbauer W., López-López J. A., Sánchez-Meca J., & Marín-Martínez F . ( 2015). A comparison of procedures to test for moderators in mixed-effects meta-regression models. Psychological Methods, 20( 3), 360-374. |
[1] | LI Yadan, DU Ying, XIE Cong, LIU Chunyu, YANG Yilong, LI Yangping, QIU Jiang. A meta-analysis of the relationship between semantic distance and creative thinking [J]. Advances in Psychological Science, 2023, 31(4): 519-534. |
[2] | ZENG Runxi, LI You. The Relationship between self-efficacy and online health information seeking: A meta-analysis [J]. Advances in Psychological Science, 2023, 31(4): 535-551. |
[3] | WU Jiahui, FU Hailun, ZHANG Yuhuan. A meta-analysis of the relationship between perceived social support and student academic achievement: The mediating role of student engagement [J]. Advances in Psychological Science, 2023, 31(4): 552-569. |
[4] | GUO Ying, TIAN Xin, HU Dong, BAI Shulin, ZHOU Shuxi. The effects of shame on prosocial behavior: A systematic review and three-level meta-analysis [J]. Advances in Psychological Science, 2023, 31(3): 371-385. |
[5] | CHEN Bizhong, SUN Xiaojun. Cross-temporal changes of college students' time management disposition in the mainland of China during 1999~2020 [J]. Advances in Psychological Science, 2022, 30(9): 1968-1980. |
[6] | DU Yufei, OUYANG Huiyue, YU Lin. The relationship between grandparenting and depression in Eastern and Western cultures: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(9): 1981-1992. |
[7] | WEN Zhonglin, XIE Jinyan, FANG Jie, WANG Yifan. Methodological research on hypothesis test and related issues in China’s mainland from 2001 to 2020 [J]. Advances in Psychological Science, 2022, 30(8): 1667-1681. |
[8] | WEN Zhonglin, FANG Jie, XIE Jinyan, OUYANG Jinying. Methodological research on mediation effects in China’s mainland [J]. Advances in Psychological Science, 2022, 30(8): 1692-1702. |
[9] | ZHAO Ning, LIU Xin, LI Shu, ZHENG Rui. Nudging effect of default options: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(6): 1230-1241. |
[10] | HUANG Xiaoxiao, ZHANG Yali, YU Guoliang. Prevalence of mental health problems among primary school students in Chinese mainland from 2010 to 2010:A meta-analysis [J]. Advances in Psychological Science, 2022, 30(5): 953-964. |
[11] | ZHANG Yali, JIN Juanjuan, YU Guoliang. Prevalence of mental health problems among junior high school students in Chinese mainland from 2010 to 2020: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(5): 965-977. |
[12] | YU Xiaoqi, ZHANG Yali, YU Guoliang. Prevalence of mental health problems among senior high school students in mainland of China from 2010 to 2020: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(5): 978-990. |
[13] | CHEN Yumeng, ZHANG Yali, YU Guoliang. Prevalence of mental health problems among college students in mainland China from 2010 to 2020: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(5): 991-1004. |
[14] | FANG Jie, WEN Zhonglin. Moderation analysis and its effect size based on a two-level regression model [J]. Advances in Psychological Science, 2022, 30(5): 1183-1190. |
[15] | WANG Jiayan, LAN Yuanmei, LI Chaoping. Challenge-hindrance stressors and innovation: A meta-analysis [J]. Advances in Psychological Science, 2022, 30(4): 761-780. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||