Form Of Measurement Error Research Articles

RELIABILITY IS NOT VALIDITY AND VALIDITY IS NOT RELIABILITY

Interrater correlations do provide an index of reliability of job performance ratings. We show that the arguments presented by Murphy and DeShon (2000) lead to the radical conclusion that traditional measurement models–both classical theory and generalizability theory models–can be used neither with job performance ratings nor with other measures used in I‐O and other areas of psychology and the social sciences. We show that this untenable conclusion is based on confusion of validity issues and questions with reliability issues and questions. It is also based on the incorrect belief that classical measurement models are capable of addressing only random response measurement error and cannot address other forms of measurement error. We also show that the solution Murphy and DeShon offer to the problem of measurement error in ratings, as they define this problem, cannot work. Properly understood, the position taken by Murphy and DeShon leaves us with the nihilistic conclusion that no appropriate measurement models are possible in psychological research, thus making meaningful research impossible.

Is the U.S. Stock Market Myopic?

This paper tests whether the U.S. stock market is myopic in the sense of overvaluing short-term earnings and undervaluing long-term earnings (see, e.g., Porter [1992; 1993]). Our tests rely on an accounting-based valuation model that generates predictions about how prices should relate to expected future earnings under the null hypothesis of market efficiency.' Using this model and Value Line earnings forecasts for a sample of firms during the period 1978-93, we estimate the value an efficient market would have assigned to firms' book values as well as near-term and long-term expected earnings. Regressions of actual stock prices against these estimates (Test 1) are used to infer whether market agents underprice long-term earnings, relative to current book value and near-term earnings. Our regression estimates from Test 1 are consistent with either such mispricing orwith certain forms of measurement error in Value Line data. Analyses of trading strategies that would exploit the mispricing caused by market myopia (Test 2) suggest that measurement error is the

Binomial-Beta Hierarchical Models for Ecological Inference

The authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King's ecological inference model. The new approach reveals some features of the data that King's approach does not, can be easily generalized to more complicated problems such as general R × C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the significance of the covariates. It may also be better suited to cases in which the observed aggregate cells are estimated from very few observations or have some forms of measurement error. This article also provides an examples of a hierarchical model in which the statistical idea of “borrowing strength” is used not merely to increase the efficiency of the estimates but to enable the data analyst to obtain estimates.

Risk and Insurance in Village India: Comment

THE PROTECTION OF POOR HOUSEHOLDS in high-risk agrarian economies from shocks to their incomes has often been seen as a compelling motive for various forms of policy intervention including transfers of cash or food, credit subsidies, and public employment schemes (for a survey see Lipton and Ravallion (1995)). The desirability of such safety net policies clearly depends on how well pre-existing risk-sharing arrangements work. While it is commonly thought that, without effective policy intervention, rural households are vulnerable to village-wide shocks such as adverse prices or poor rains, it is less clear to what extent risk-sharing institutions within the village mitigate the effects of idiosyncratic income risk stemming, for instance, from ill-health or localized crop damage.2 Several recent papers have used household-level data to implement tests of risk-sharing that might inform such concerns.3 These tests are based on the proposition that with perfect risk-sharing, consumption at the household level should be insured against idiosyncratic risks and thus depend solely on the realization of the aggregate risk (Wilson (1968); Diamond (1967)). Townsend (1994) tests this implication of perfect intra-village risk-sharing using longitudinal household data on consumptions and incomes for three villages in India. He reports that the full-insurance hypothesis provides a surprisingly good benchmark in that household consumptions co-move and do not appear to be much influenced by contemporaneous own income. Our aim here is to examine the robustness of this potentially important finding. We have two main concerns. The first is that the specification Townsend adopts potentially biases his test towards the null hypothesis of full insurance because it yields inconsistent estimates of the key test parameter under a plausible alternative. We therefore estimate a different specification that generates consistent estimates under both the null and the alternative. Our second concern is that a particular form of measurement error in the consumption data Townsend uses may have further biased his results toward the null hypothesis of full-insurance. To address this concern, we use an instrumental variables procedure when we implement the test of consumption insurance with Townsend's consumption data. We also re-estimate the test equations with a measure of consumption derived from the same underlying primary data by an alternate method. Finally, Townsend reports 1Discussions with Robert Townsend stimulated our interest in this investigation. The staff of the

Detecting Outliers in Deterministic Nonparametric Frontier Models With Multiple Outputs

This article provides a statistical methodology for identifying outliers in production data with multiple inputs and outputs used in deterministic nonparametric frontier models. The methodology is useful in identifying observations that may contain some form of measurement error and thus merit closer scrutiny. When data checking is costly, the methodology may be used to rank observations in terms of their dissimilarity to other observations in the data, suggesting a priority for further inspection of the data.

On the statistical moments transformation in pharmacokinetic models: A study of the rate parameter and the mean residence time estimates

Abstract This paper simulates the sampling distributions of the rate parameter and the mean residence time (MRT) estimators for a specific two-compartment model. Two forms of measurement error, one additive and the other multiplicative, and two forms of weighting structures, one unweighted and the other weighted by the error variances, are investigated. The chief findings are: 1) unweighted estimates generally are highly biased but weighting reduces the bias many fold, 2) the asymptotic standard error approximations are acceptable only with weighted least squares, and 3) the mean squared errors of the MRT estimates compare favorably with those of the rate parameter estimates. Based on the favorable sampling distributions, it is recommended that weighted MRT estimates be calculated routinely as important descriptors of kinetic systems.