-
1
-
-
84922259964
-
Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data
-
Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3, 133–153. doi:10.1017/psrm.2014.7
-
(2015)
Political Science Research and Methods
, vol.3
, pp. 133-153
-
-
Bell, A.1
Jones, K.2
-
2
-
-
84894161242
-
How low can you go?
-
Bell, B. A., Morgan, G. B., Schoeneberger, J. A., Kromrey, J. D., & Ferron, J. M. (2014). How low can you go?. Methodology, 10, 1–11. doi:10.1027/1614-2241/a000062
-
(2014)
Methodology
, vol.10
, pp. 1-11
-
-
Bell, B.A.1
Morgan, G.B.2
Schoeneberger, J.A.3
Kromrey, J.D.4
Ferron, J.M.5
-
3
-
-
33847083094
-
A comparison of Bayesian and likelihood-based methods for fitting multilevel models
-
Browne, W. J., & Draper, D. (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1, 473–514. doi:10.1214/06-BA117
-
(2006)
Bayesian Analysis
, vol.1
, pp. 473-514
-
-
Browne, W.J.1
Draper, D.2
-
4
-
-
0018115641
-
Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
-
Efron, B., & Hinkley, D. V. (1978). Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information. Biometrika, 65, 457–482. doi:10.1093/biomet/65.3.457
-
(1978)
Biometrika
, vol.65
, pp. 457-482
-
-
Efron, B.1
Hinkley, D.V.2
-
6
-
-
66149144783
-
Making treatment effect inferences from multiple-baseline data: The utility of multilevel modeling approaches
-
Ferron, J. M., Bell, B. A., Hess, M. R., Rendina-Gobioff, G., & Hibbard, S. T. (2009). Making treatment effect inferences from multiple-baseline data: The utility of multilevel modeling approaches. Behavior Research Methods, 41, 372–384. doi:10.3758/BRM.41.2.372
-
(2009)
Behavior Research Methods
, vol.41
, pp. 372-384
-
-
Ferron, J.M.1
Bell, B.A.2
Hess, M.R.3
Rendina-Gobioff, G.4
Hibbard, S.T.5
-
7
-
-
70350471314
-
Multilevel mixed linear model analysis using iterative generalized least squares
-
Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika, 73, 43–56. doi:10.1093/biomet/73.1.43
-
(1986)
Biometrika
, vol.73
, pp. 43-56
-
-
Goldstein, H.1
-
8
-
-
0000655117
-
Mean squared error of estimation or prediction under a general linear model
-
Harville, D. A., & Jeske, D. R. (1992). Mean squared error of estimation or prediction under a general linear model. Journal of the American Statistical Association, 87, 724–731. doi:10.1080/01621459.1992.10475274
-
(1992)
Journal of the American Statistical Association
, vol.87
, pp. 724-731
-
-
Harville, D.A.1
Jeske, D.R.2
-
9
-
-
84883641307
-
How few countries will do? Comparative survey analysis from a Bayesian perspective
-
July
-
Hox, J. J., van de Schoot, R., & Matthijsse, S. (2012, July). How few countries will do? Comparative survey analysis from a Bayesian perspective. Survey Research Methods, 6, 87–93.
-
(2012)
Survey Research Methods
, vol.6
, pp. 87-93
-
-
Hox, J.J.1
van de Schoot, R.2
Matthijsse, S.3
-
10
-
-
85041022610
-
Using cluster bootstrapping to analyze nested data with a few clusters
-
Advance online publication
-
Huang, F. L. (2017). Using cluster bootstrapping to analyze nested data with a few clusters. Educational and Psychological Measurement. Advance online publication. doi:10.1177/0013164416678980.
-
(2017)
Educational and Psychological Measurement
-
-
Huang, F.L.1
-
11
-
-
0001089821
-
Unbiasedness of two-stage estimation and prediction procedures for mixed linear models
-
Kackar, R. N., & Harville, D. A. (1981). Unbiasedness of two-stage estimation and prediction procedures for mixed linear models. Communications in Statistics-Theory and Methods, 10, 1249–1261. doi:10.1080/03610928108828108
-
(1981)
Communications in Statistics-Theory and Methods
, vol.10
, pp. 1249-1261
-
-
Kackar, R.N.1
Harville, D.A.2
-
12
-
-
70350184453
-
Approximations for standard errors of estimators of fixed and random effects in mixed linear models
-
Kackar, R. N., & Harville, D. A. (1984). Approximations for standard errors of estimators of fixed and random effects in mixed linear models. Journal of the American Statistical Association, 79, 853–862. doi:10.2307/2288715
-
(1984)
Journal of the American Statistical Association
, vol.79
, pp. 853-862
-
-
Kackar, R.N.1
Harville, D.A.2
-
13
-
-
61849100484
-
An improved approximation to the precision of fixed effects from restricted maximum likelihood
-
Kenward, M. G., & Roger, J. H. (2009). An improved approximation to the precision of fixed effects from restricted maximum likelihood. Computational Statistics and Data Analysis, 53, 2583–2595. doi:10.1016/j.csda.2008.12.013
-
(2009)
Computational Statistics and Data Analysis
, vol.53
, pp. 2583-2595
-
-
Kenward, M.G.1
Roger, J.H.2
-
14
-
-
0030880605
-
Small sample inference for fixed effects from restricted maximum likelihood
-
Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997. doi:10.2307/2533558
-
(1997)
Biometrics
, vol.53
, pp. 983-997
-
-
Kenward, M.G.1
Roger, J.H.2
-
15
-
-
0007019292
-
The analysis of repeated measurements: A comparison of mixed-model Satterthwaite F tests and a nonpooled adjusted degrees of freedom multivariate test
-
Keselman, H. J., Algina, J., Kowalchuk, R. K., & Wolfinger, R. D. (1999). The analysis of repeated measurements: A comparison of mixed-model Satterthwaite F tests and a nonpooled adjusted degrees of freedom multivariate test. Communications in Statistics-Theory and Methods, 28, 2967–2999. doi:10.1080/03610929908832460
-
(1999)
Communications in Statistics-Theory and Methods
, vol.28
, pp. 2967-2999
-
-
Keselman, H.J.1
Algina, J.2
Kowalchuk, R.K.3
Wolfinger, R.D.4
-
16
-
-
69949190053
-
Sufficient sample sizes for multilevel modeling
-
Maas, C. J., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology, 1, 86–92. doi:10.1027/1614-2241.1.3.86
-
(2005)
Methodology
, vol.1
, pp. 86-92
-
-
Maas, C.J.1
Hox, J.J.2
-
17
-
-
84974808077
-
On using Bayesian methods to address small sample problems
-
McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23, 750–773. doi:10.1080/10705511.2016.1186549
-
(2016)
Structural Equation Modeling
, vol.23
, pp. 750-773
-
-
McNeish, D.1
-
18
-
-
84973596359
-
Modeling clustered data with very few clusters
-
McNeish, D., & Stapleton, L. M. (2016a). Modeling clustered data with very few clusters. Multivariate Behavioral Research, 51, 495–518. doi:10.1080/00273171.2016.1167008
-
(2016)
Multivariate Behavioral Research
, vol.51
, pp. 495-518
-
-
McNeish, D.1
Stapleton, L.M.2
-
19
-
-
84908298355
-
The effect of small sample size on two-level model estimates: A review and illustration
-
McNeish, D., & Stapleton, L. M. (2016b). The effect of small sample size on two-level model estimates: A review and illustration. Educational Psychology Review, 28, 295–314. doi:10.1007/s10648-014-9287-x
-
(2016)
Educational Psychology Review
, vol.28
, pp. 295-314
-
-
McNeish, D.1
Stapleton, L.M.2
-
20
-
-
84873047637
-
Bayesian structural equation modeling: a more flexible representation of substantive theory
-
Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychological Methods, 17, 313–335. doi:10.1037/a0026802
-
(2012)
Psychological Methods
, vol.17
, pp. 313-335
-
-
Muthén, B.1
Asparouhov, T.2
-
21
-
-
84900529999
-
The estimation of the mean squared error of small-area estimators
-
Prasad, N. G. N., & Rao, J. N. (1990). The estimation of the mean squared error of small-area estimators. Journal of the American Statistical Association, 85, 163–171. doi:10.1080/01621459.1990.10475320
-
(1990)
Journal of the American Statistical Association
, vol.85
, pp. 163-171
-
-
Prasad, N.G.N.1
Rao, J.N.2
-
23
-
-
84872636119
-
An approximate distribution of estimates of variance components
-
Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin, 2, 110–114. doi:10.2307/3002019
-
(1946)
Biometrics Bulletin
, vol.2
, pp. 110-114
-
-
Satterthwaite, F.E.1
-
24
-
-
2342641287
-
Adequacy of approximations to distributions of test statistics in complex mixed linear models
-
Schaalje, G. B., McBride, J. B., & Fellingham, G. W. (2002). Adequacy of approximations to distributions of test statistics in complex mixed linear models. Journal of Agricultural, Biological, and Environmental Statistics, 7, 512–524. doi:10.1198/108571102726
-
(2002)
Journal of Agricultural, Biological, and Environmental Statistics
, vol.7
, pp. 512-524
-
-
Schaalje, G.B.1
McBride, J.B.2
Fellingham, G.W.3
-
25
-
-
0000935535
-
Standard errors and sample sizes for two-level research
-
Snijders, T. A., & Bosker, R. J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–259. doi:10.2307/1165134
-
(1993)
Journal of Educational Statistics
, vol.18
, pp. 237-259
-
-
Snijders, T.A.1
Bosker, R.J.2
-
26
-
-
84938968883
-
Standardized effect size measures for mediation analysis in cluster-randomized trials
-
Stapleton, L. M., Pituch, K. A., & Dion, E. (2015). Standardized effect size measures for mediation analysis in cluster-randomized trials. The Journal of Experimental Education, 83, 547–582. doi:10.1080/00220973.2014.919569
-
(2015)
The Journal of Experimental Education
, vol.83
, pp. 547-582
-
-
Stapleton, L.M.1
Pituch, K.A.2
Dion, E.3
-
27
-
-
84977106631
-
Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors
-
van de Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., & van Loey, N. E. (2015). Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology, 6. doi:10.3402/ejpt.v6.25216
-
(2015)
European Journal of Psychotraumatology
, vol.6
-
-
van de Schoot, R.1
Broere, J.J.2
Perryck, K.H.3
Zondervan-Zwijnenburg, M.4
van Loey, N.E.5
-
28
-
-
84899945186
-
A gentle introduction to Bayesian analysis: Applications to developmental research
-
van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Aken, M. A. (2014). A gentle introduction to Bayesian analysis: Applications to developmental research. Child Development, 85, 842–860. doi:10.1111/cdev.12169
-
(2014)
Child Development
, vol.85
, pp. 842-860
-
-
van de Schoot, R.1
Kaplan, D.2
Denissen, J.3
Asendorpf, J.B.4
Neyer, F.J.5
Aken, M.A.6
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