An Analysis of the Historical Regression Method of Predicting Posttest Grade Equivalents for Categorically-Aided Programs [electronic resource] / Thomas L. Hick and David J. Irvine.
To eliminate maturation as a factor in the pretest-posttest design, pretest scores can be converted to anticipate posttest scores using grade equivalent scores from standardized tests. This conversion, known as historical regression, assumes that without specific intervention, growth will continue a...
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| Format: | Electronic eBook |
| Language: | English |
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[S.l.] :
Distributed by ERIC Clearinghouse,
1978.
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MARC
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| 099 | |f ERIC DOC # |a ED160632 | ||
| 100 | 1 | |a Hick, Thomas L. | |
| 245 | 1 | 3 | |a An Analysis of the Historical Regression Method of Predicting Posttest Grade Equivalents for Categorically-Aided Programs |h [electronic resource] / |c Thomas L. Hick and David J. Irvine. |
| 260 | |a [S.l.] : |b Distributed by ERIC Clearinghouse, |c 1978. | ||
| 300 | |a 11 p. | ||
| 500 | |a ERIC Document Number: ED160632. | ||
| 500 | |a ERIC Note: Paper presented at the Annual Meeting of the American Educational Research Association (62nd, Toronto, Ontario, Canada, March 27-31, 1978). |5 ericd. | ||
| 500 | |a Educational level discussed: Elementary Education. | ||
| 520 | |a To eliminate maturation as a factor in the pretest-posttest design, pretest scores can be converted to anticipate posttest scores using grade equivalent scores from standardized tests. This conversion, known as historical regression, assumes that without specific intervention, growth will continue at the rate (grade equivalents per year of schooling) obtained at the time of pretest. Data were taken from reports of 213 Title I compensatory education programs in New York State to examine the predictive ability of the historical regression model. The approach was to: (1) express historical regression in algebraic terms; (2) produce a linear model with assigned weights from the algebraic formula; (3) produce a least squares historical regression model whose weights best fit the data; (4) compare the historical regression model with the least squares model; and (5) develop an alternative model. When compared with program-level data, historical regression underestimated final achievement for short programs with older children. It overestimated for younger children in long programs. An alternative method was developed which eliminated the bias, removed half of the error, and eliminated much computation since an expected achievement level for each child was not required. (Author/CP) | ||
| 650 | 0 | 7 | |a Academic Achievement. |2 ericd. |
| 650 | 1 | 7 | |a Achievement Gains. |2 ericd. |
| 650 | 0 | 7 | |a Elementary Education. |2 ericd. |
| 650 | 1 | 7 | |a Grade Equivalent Scores. |2 ericd. |
| 650 | 0 | 7 | |a Least Squares Statistics. |2 ericd. |
| 650 | 1 | 7 | |a Mathematical Models. |2 ericd. |
| 650 | 0 | 7 | |a Maturation. |2 ericd. |
| 650 | 1 | 7 | |a Predictive Measurement. |2 ericd. |
| 650 | 0 | 7 | |a Pretesting. |2 ericd. |
| 650 | 1 | 7 | |a Pretests Posttests. |2 ericd. |
| 650 | 1 | 7 | |a Program Length. |2 ericd. |
| 650 | 0 | 7 | |a Statistical Analysis. |2 ericd. |
| 650 | 0 | 7 | |a Weighted Scores. |2 ericd. |
| 700 | 1 | |a Irvine, David J. | |
| 856 | 4 | 0 | |z Full Text (via ERIC) |u http://files.eric.ed.gov/fulltext/ED160632.pdf |
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| 952 | f | f | |p Can circulate |a University of Colorado Boulder |b Online |c Online |d Online |e ED160632 |h Other scheme |i web |n 1 |