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- Author: Lawrence B Mohr
- Publisher: SAGE Publications, Inc
- ISBN-10: 0803959354
- ISBN-13: 9780803959354
- Format: 15.2 x 22.9 x 2.2 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
Successful in the first edition for its integration of multiple regression with evaluation design and for its systematic ways to select the proper goals for single - and multiple- outcome evaluations, this new edition is more helpful than ever. Impact Analysis for Program Evaluation, Second Edition has been revised to cover new issues and to further clarify the concepts used in impact analysis. It offers expanded coverage and explanation of quasi-experiments, a new section on the theory of impact analysis, updated information on the use of qualitative research for impact analysis, and expanded coverage of significance testing for program evaluation. It also includes an explanation of why the comparative-change design (i.e., Campbell and Stanley′s "nonequivalent control group" design) is better than an ex post facto design from the standpoint of causal inference.
EXTRA 10 % discount with code: EXTRA
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- Author: Lawrence B Mohr
- Publisher: SAGE Publications, Inc
- ISBN-10: 0803959354
- ISBN-13: 9780803959354
- Format: 15.2 x 22.9 x 2.2 cm, hardcover
- Language: English English
Successful in the first edition for its integration of multiple regression with evaluation design and for its systematic ways to select the proper goals for single - and multiple- outcome evaluations, this new edition is more helpful than ever. Impact Analysis for Program Evaluation, Second Edition has been revised to cover new issues and to further clarify the concepts used in impact analysis. It offers expanded coverage and explanation of quasi-experiments, a new section on the theory of impact analysis, updated information on the use of qualitative research for impact analysis, and expanded coverage of significance testing for program evaluation. It also includes an explanation of why the comparative-change design (i.e., Campbell and Stanley′s "nonequivalent control group" design) is better than an ex post facto design from the standpoint of causal inference.
Reviews