Quinn McNemar

Bio: Quinn McNemar is an academic researcher from Stanford University. The author has contributed to research in topics: Wechsler Adult Intelligence Scale & Psychological testing. The author has an hindex of 18, co-authored 32 publications receiving 5664 citations.

Note on the sampling error of the difference between correlated proportions or percentages.

Abstract: Two formulas are presented for judging the significance of the difference between correlated proportions. The chi square equivalent of one of the developed formulas is pointed out.

On Growth Measurement

Abstract: THE RECENT article by Lord in this journal (3) on the measurement of growth prompts this note. We will herein present: (a) a derivation of the basic formulas without the unrealistic and restrictive assumption of equality of error variance for initial and final scores, (b) a simple approximation method for estimating true gains, and (c) a brief examination of Lord’s deduction regarding multiple testings. In our derivations we will approach the problem in a somewhat more direct and simpler fashion than that followed by Lord, and for sake of brevity we will not hesitate to invoke needed relationships which may be found in, say, Gulliksen (i) and/or in statistical textbooks. We will also use a notation which may be easier to follow than that used by Lord.

Lost: Our intelligence? Why?

Abstract: T HE Greeks had a word for it, but the Romans had a word with better survival properties. Regardless of the word, what is now called intelligence has been talked about for at least 2,000 years. And as long as 2,000 years before the advent of attempts to measure intelligence, there seems to have been recognition of the fact that individuals differ in intellectual ability. The earlier attempts at measuring were based on either of two quite distinct conceptions: the GaltonCattell idea that intellectual ability manifests itself in simple, discrimination functioning, and the Binet notion that cognitive ability reflects itself in more complex functioning. The Binet concept proved to be more fruitful, and by 1925 there was on the market, in addition to various versions of the Binet scale, a flood of group tests of so-called general intelligence. A few words about definition may be in order. First, it might be claimed that no definition is required because all intelligent people know what intelligence is—it is the thing that the other guy lacks. Second, the fact that tests of general intelligence based on differing definitions tend to intercorrelate about as highly as their respective reliabilities permit indicates that, despite the diversity of definitions, the same function or process is being measured—definitions can be more confusing than enlightening. Third, that confusion might have been anticipated is evident from a recent reexamination of the problem of definition by Miles (19S7). This British chappie found himself struggling with the awful fact that the word \"definition\" itself has 12 definitions. Perhaps the resolution of this problem should be assigned to the newly formed Division of Philosophical Psychology, or maybe the,problem should be forgotten since psychologists seem to have lost the concept of general intelligence. Why has the concept been abandoned? Was it replaced by something else? By something better?

The scree test for the number of factors

Abstract: (1966). The Scree Test For The Number Of Factors. Multivariate Behavioral Research: Vol. 1, No. 2, pp. 245-276.

Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.

Abstract: A previously described coefficient of agreement for nominal scales, kappa, treats all disagreements equally. A generalization to weighted kappa (Kw) is presented. The Kw provides for the incorpation of ratio-scaled degrees of disagreement (or agreement) to each of the cells of the k * k table of joi

Evaluating the use of exploratory factor analysis in psychological research.

Abstract: Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.

The file drawer problem and tolerance for null results

Abstract: For any given research area, one cannot tell how many studies have been conducted but never reported. The extreme view of the "file drawer problem" is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results. Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed. (15 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)

Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

Abstract: Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.