Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship Between Two Variables and an Outcome
Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A...
Ausführliche Beschreibung
Autor*in: |
Dong, Nianbo [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Übergeordnetes Werk: |
Enthalten in: American journal of evaluation - Thousand Oaks, Calif. [u.a.] : Sage, 1998, 36(2015), 1, Seite 42-66 |
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Übergeordnetes Werk: |
volume:36 ; year:2015 ; number:1 ; pages:42-66 |
Links: |
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DOI / URN: |
10.1177/1098214014553261 |
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OLC195702531X |
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10.1177/1098214014553261 doi PQ20160617 (DE-627)OLC195702531X (DE-599)GBVOLC195702531X (PRQ)c1603-7f4bfffcd7c9a3bb3d882ed4b66ec8d398fa679182f58ad73244d76dd572dab40 (KEY)0137518020150000036000100042usingpropensityscoremethodstoapproximatefactoriale DE-627 ger DE-627 rakwb eng 330 ZDB 70.03 bkl Dong, Nianbo verfasserin aut Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship Between Two Variables and an Outcome 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin’s causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children’s reading achievement vary by child care quality. factorial experimental designs propensity score causal inference Enthalten in American journal of evaluation Thousand Oaks, Calif. [u.a.] : Sage, 1998 36(2015), 1, Seite 42-66 (DE-627)246046562 (DE-600)1433908-0 (DE-576)285087576 1098-2140 nnns volume:36 year:2015 number:1 pages:42-66 http://dx.doi.org/10.1177/1098214014553261 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-SOW GBV_ILN_11 GBV_ILN_2005 70.03 AVZ AR 36 2015 1 42-66 |
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Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin’s causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children’s reading achievement vary by child care quality. |
abstractGer |
Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin’s causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children’s reading achievement vary by child care quality. |
abstract_unstemmed |
Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin’s causal model to approximate factorial experimental designs for studies with partial randomization and nonrandomization. I apply a Monte Carlo simulation to evaluate several propensity score applications. The findings suggest the following two applications for reducing bias and mean square error of parameter estimates when analyzing the relationship of two variables and an outcome: (a) inverse of propensity score weighting based on one multinomial propensity score model and (b) subclassification based on two binary propensity score models. As a demonstration, I examine whether the effects of the Head Start program, compared to other center-based care, for improving children’s reading achievement vary by child care quality. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC195702531X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220215130351.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1177/1098214014553261</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC195702531X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC195702531X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1603-7f4bfffcd7c9a3bb3d882ed4b66ec8d398fa679182f58ad73244d76dd572dab40</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0137518020150000036000100042usingpropensityscoremethodstoapproximatefactoriale</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">ZDB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">70.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Dong, Nianbo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship Between Two Variables and an Outcome</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Researchers have become increasingly interested in programs’ main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. 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