Descriptor
| Mathematical Models | 15 |
| Program Evaluation | 15 |
| Research Problems | 15 |
| Research Design | 9 |
| Evaluation Methods | 7 |
| Achievement Gains | 5 |
| Control Groups | 5 |
| Statistical Analysis | 5 |
| Norm Referenced Tests | 4 |
| Program Effectiveness | 4 |
| Academic Achievement | 3 |
| More ▼ | |
Source
| Evaluation Review | 2 |
| Economics of Education Review | 1 |
| Educational Evaluation and… | 1 |
| Journal of Educational… | 1 |
| Journal of Experimental… | 1 |
| New Directions for Program… | 1 |
Author
| Magidson, Jay | 3 |
| Sorbom, Dag | 2 |
| Bible, Thomas D. | 1 |
| Burstein, Leigh | 1 |
| Conklin, Jonathan E. | 1 |
| Dwyer, James H. | 1 |
| Echternacht, Gary | 1 |
| Horst, Donald P. | 1 |
| Kirjavainen, Tanja | 1 |
| Leviton, Laura C., Ed. | 1 |
| Linn, Robert L. | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 11 |
| Journal Articles | 7 |
| Speeches/Meeting Papers | 4 |
| Reports - Evaluative | 2 |
| Collected Works - General | 1 |
| Opinion Papers | 1 |
| Reports - General | 1 |
Education Level
Audience
Location
| Finland | 1 |
Laws, Policies, & Programs
| Elementary and Secondary… | 4 |
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewedMagidson, Jay; Sorbom, Dag – Educational Evaluation and Policy Analysis, 1982
LISREL V computer program is applied to a weak quasi-experimental design involving the Head Start program, as a multiple analysis attempt to assure that differences between nonequivalent control groups do not confound interpretation of a posteriori differences. (PN)
Descriptors: Achievement Gains, Early Childhood Education, Mathematical Models, Program Evaluation
Conklin, Jonathan E.; Burstein, Leigh – 1979
Educational outcomes are affected by student level, classroom level, and school level characteristics. The fact that educational data are multilevel in nature poses serious analysis questions. Though strong arguments can be made for focusing on a single level of analysis, such studies have several basic limitations: the choice of analytic level…
Descriptors: Analysis of Covariance, Correlation, Data Analysis, Mathematical Models
Yap, Kim Onn; And Others – 1979
The effects of using different data analysis methods on estimates of treatment effects of educational programs were investigated. Various regression models, such as those recommended for Title I program evaluations, were studied. The first effect studied was the amount of bias that might be expected to occur in the various settings. Results…
Descriptors: Bias, Compensatory Education, Evaluation Methods, Mathematical Models
Peer reviewedMoffitt, Robert – Evaluation Review, 1991
Statistical methods for program evaluation with nonexperimental data are reviewed with emphasis on circumstances in which nonexperimental data are valid. Three solutions are proposed for problems of selection bias, and implications for evaluation design and data collection and analysis are discussed. (SLD)
Descriptors: Bias, Cohort Analysis, Equations (Mathematics), Estimation (Mathematics)
Peer reviewedDwyer, James H. – Evaluation Review, 1984
A solution to the problem of specification error due to excluded variables in statistical models of treatment effects in nonrandomized (nonequivalent) control group designs is presented. It involves longitudinal observation with at least two pretests. A maximum likelihood estimation program such as LISREL may provide reasonable estimates of…
Descriptors: Control Groups, Mathematical Models, Maximum Likelihood Statistics, Monte Carlo Methods
Peer reviewedBible, Thomas D. – Journal of Educational Statistics, 1979
Two related problems are discussed: developing a methodology which (1) will not require full commitment of a local education agency to new techniques which may be ineffective in local settings and (2) will allow graceful abandonment of a new technology that may be proved ineffective. An example is discussed. (CTM)
Descriptors: Cost Effectiveness, Demonstration Programs, Educational Innovation, Evaluation Methods
Magidson, Jay – 1977
In evaluation research studies, it often occurs that several program participants (experimentals) drop out of the program prior to completion. Since noncompleters generally differ substantially from completers in many respects, a control group which originally was representative of the participant group will most likely not be representative of…
Descriptors: Attrition (Research Studies), Career Education, Control Groups, Discriminant Analysis
Peer reviewedKirjavainen, Tanja; Loikkanen, Heikki A. – Economics of Education Review, 1998
Studied efficiency differences in Finnish senior secondary schools by Data Envelopment Analysis, using four variant models. Average efficiencies in the most extensive models were 82% to 84%. Considering parents' educational level increased average efficiency to 91%. Tobit analysis showed school size did not affect efficiency. Input and output…
Descriptors: Class Size, Diversity (Student), Efficiency, Foreign Countries
Peer reviewedLeviton, Laura C., Ed.; And Others – New Directions for Program Evaluation, 1990
Seven essays on efforts of evaluate prevention programs aimed at the acquired immune deficiency syndrome (AIDS) are presented. Topics include public health psychology, mathematical models of epidemiology, estimates of incubation periods, ethnographic evaluations of AIDS prevention programs, an AIDS education model, theory-based evaluation, and…
Descriptors: Acquired Immune Deficiency Syndrome, Community Programs, Educational Psychology, Epidemiology
Murray, Stephen L. – 1978
The norm-referenced evaluation model (RMC Model A) for Title I project evaluation, consists of procedures whereby the expected posttest standing of a treatment group under the null condition is generated from their pretest standing. It is assumed that the treatment group is not selected on the basis of their pretest scores and can be considered…
Descriptors: Achievement Gains, Educational Assessment, Elementary Secondary Education, Evaluation Methods
Echternacht, Gary; Swinton, Spencer – 1979
Title I evaluations using the RMC Model C design depend for their interpretation on the assumption that the regression of posttest on pretest is linear across the cut score level when there is no treatment; but there are many instances where nonlinearities may occur. If one applies the analysis of covariance, or model C analysis, large errors may…
Descriptors: Achievement Gains, Analysis of Covariance, Educational Assessment, Elementary Secondary Education
Magidson, Jay; Sorbom, Dag – 1980
Evaluations of social programs based upon quasi-experimental designs are typically plagued by problems of nonequivalence between the experimental and comparison group prior to the experiment. In such settings it is extremely difficult, if not impossible, to isolate the effects of the program from the confounding effects associated with the…
Descriptors: Control Groups, Correlation, Evaluation Methods, Experimental Groups
Linn, Robert L. – 1978
The three RMC models endorsed by the U.S. Office of Education for the evaluation of Elementary and Secondary Education Act Title I programs are based on narrowly conceived approaches to evaluation--the measurement of cognitive achievement gains. Each model requires the comparison of observed student performance with an estimate of what level of…
Descriptors: Academic Achievement, Achievement Gains, Compensatory Education, Control Groups
Peer reviewedMarascuilo, Leonard A. – Journal of Experimental Education, 1979
The utility of the biomedical model of adjusted statistics is demonstrated. The model is recommended for use by educational researchers to randomize subjects for a more accurate estimate of school programs' success or failure when compared across classrooms or other units. (Author/MH)
Descriptors: Academic Achievement, Analysis of Variance, Comparative Analysis, Criterion Referenced Tests
Horst, Donald P.; Tallmadge, G. Kasten – 1976
The orientation of this report is that of identifying educational projects which can be considered clearly exemplary. The largest section consists of a 22-step procedure for validating the effectiveness of educational projects using existing evaluation data. It is not intended as a guide for conducting evaluations but rather for interpreting data…
Descriptors: Academic Achievement, Achievement Gains, Compensatory Education, Control Groups


