Improving Institutional Evaluation Methods: Comparing Three Evaluations Using PSM, Exact and Coarsened Exact Matching
Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwes...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Blankenberger, Bob [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021. corrected publication 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Research in higher education - Springer Netherlands, 1973, 62(2021), 8 vom: 10. Apr., Seite 1248-1275 |
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| Übergeordnetes Werk: |
volume:62 ; year:2021 ; number:8 ; day:10 ; month:04 ; pages:1248-1275 |
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| DOI / URN: |
10.1007/s11162-021-09632-0 |
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OLC2077355638 |
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| 520 | |a Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwestern regional public university, for this study we compared analysis approaches using basic descriptive analyses, regression, standard propensity score matching (PSM), and a mixture of PSM with continuous variables, coarsened exact matching, and exact matching on categorical variables. We used three examples of program evaluations: a freshman seminar, an upper division general education program intended to improve cultural awareness and respect for diverse groups, and multiple living learning communities. We describe how these evaluations were conducted, compare the different results for each type of method employed, and discuss the strengths and weaknesses of each in the context of program evaluation. | ||
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Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwestern regional public university, for this study we compared analysis approaches using basic descriptive analyses, regression, standard propensity score matching (PSM), and a mixture of PSM with continuous variables, coarsened exact matching, and exact matching on categorical variables. We used three examples of program evaluations: a freshman seminar, an upper division general education program intended to improve cultural awareness and respect for diverse groups, and multiple living learning communities. We describe how these evaluations were conducted, compare the different results for each type of method employed, and discuss the strengths and weaknesses of each in the context of program evaluation. © The Author(s) 2021. corrected publication 2021 |
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Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwestern regional public university, for this study we compared analysis approaches using basic descriptive analyses, regression, standard propensity score matching (PSM), and a mixture of PSM with continuous variables, coarsened exact matching, and exact matching on categorical variables. We used three examples of program evaluations: a freshman seminar, an upper division general education program intended to improve cultural awareness and respect for diverse groups, and multiple living learning communities. We describe how these evaluations were conducted, compare the different results for each type of method employed, and discuss the strengths and weaknesses of each in the context of program evaluation. © The Author(s) 2021. corrected publication 2021 |
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Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. Employing the setting of higher education program evaluation at a midwestern regional public university, for this study we compared analysis approaches using basic descriptive analyses, regression, standard propensity score matching (PSM), and a mixture of PSM with continuous variables, coarsened exact matching, and exact matching on categorical variables. We used three examples of program evaluations: a freshman seminar, an upper division general education program intended to improve cultural awareness and respect for diverse groups, and multiple living learning communities. We describe how these evaluations were conducted, compare the different results for each type of method employed, and discuss the strengths and weaknesses of each in the context of program evaluation. © The Author(s) 2021. corrected publication 2021 |
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10.1007/s11162-021-09632-0 |
| up_date |
2024-07-03T15:07:14.984Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2077355638</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505144154.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11162-021-09632-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2077355638</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11162-021-09632-0-p</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">370</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,2</subfield><subfield code="a">5,3</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Blankenberger, Bob</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5025-4880</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving Institutional Evaluation Methods: Comparing Three Evaluations Using PSM, Exact and Coarsened Exact Matching</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2021. corrected publication 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Policymakers and institutional leaders in higher education too often make decisions based on descriptive data analyses or even anecdote when better analysis options could produce more nuanced and more valuable results. 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