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Kaplan, David; Huang, Mingya – Large-scale Assessments in Education, 2021
Of critical importance to education policy is monitoring trends in education outcomes over time. In the United States, the National Assessment of Educational Progress (NAEP) has provided long-term trend data since 1970; at the state/jurisdiction level, NAEP has provided long-term trend data since 1996. In addition to the national NAEP, all 50…
Descriptors: Educational Policy, Educational Trends, National Competency Tests, Bayesian Statistics
Oranje, Andreas; Kolstad, Andrew – Journal of Educational and Behavioral Statistics, 2019
The design and psychometric methodology of the National Assessment of Educational Progress (NAEP) is constantly evolving to meet the changing interests and demands stemming from a rapidly shifting educational landscape. NAEP has been built on strong research foundations that include conducting extensive evaluations and comparisons before new…
Descriptors: National Competency Tests, Psychometrics, Statistical Analysis, Computation
Li, Tiandong – ProQuest LLC, 2012
In large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating…
Descriptors: National Competency Tests, Statistical Analysis, Educational Assessment, Test Theory
Fields, Ray – National Assessment Governing Board, 2014
Indicators of many kinds are used to monitor critical aspects of national life and inform public policy. These include economic indicators, health indicators, and demographic indicators. For more than a decade, the National Assessment Governing Board has been conducting research to determine the potential of the National Assessment of Educational…
Descriptors: National Competency Tests, Educational Indicators, College Preparation, College Readiness
Mislevy, Robert J.; And Others – 1994
It is a common practice in item response theory (IRT) to treat estimates of item parameters, say "B" circumflex, as if they were the known, true quantities, "B." However, ignoring the uncertainty associated with item parameters can lead to biases and over-confidence in subsequent inferences such as ability estimation,…
Descriptors: Ability, Bias, Estimation (Mathematics), Item Response Theory
Oranje, Andreas; Freund, David; Lin, Mei-jang; Tang, Yuxin – ETS Research Report Series, 2007
In this paper, a data perturbation method for minimizing the possibility of disclosure of participants' identities on a survey is described in the context of the National Assessment of Educational Progress (NAEP). The method distinguishes itself from most approaches because of the presence of cognitive tasks. Hence, a data edit should have minimal…
Descriptors: Student Surveys, Risk, National Competency Tests, Data Analysis
Sheehan, Kathleen M.; Mislevy, Robert J. – 1988
In many practical applications of item response theory, the parameters of overlapping subsets of test items are estimated from different samples of examinees. A linking procedure is then employed to place the resulting item parameter estimates onto a common scale. It is standard practice to ignore the uncertainty associated with the linking step…
Descriptors: Error of Measurement, Estimation (Mathematics), Item Response Theory, Measurement Techniques

Cohen, Jon; Snow, Stephanie – Journal of Educational Measurement, 2002
Studied the impact of changes in item difficulty on National Assessment of Educational Progress (NAEP) estimates over time through a Monte Carlo study that simulated the responses of 1990 NAEP mathematics respondents to 1990 and 1996 NAEP mathematics items. Results support the idea that these changes have not affected the NAEP trend line.…
Descriptors: Change, Difficulty Level, Estimation (Mathematics), Mathematics Tests

Johnson, Eugene G.; Rust, Keith F. – Journal of Educational Statistics, 1992
The use of sampling weights in deriving population estimates for the National Assessment of Educational Progress (NAEP) and the effects of nonresponse and undercoverage on those estimates are described. The estimation of sampling variability from complex sample surveys is also reviewed, concentrating on the jackknife repeated replication…
Descriptors: Educational Assessment, Elementary Secondary Education, Estimation (Mathematics), Mathematical Models