The difficulties inherent in obtaining consistent and adequate diagnoses for the purposes of research and therapy have been pointed out by a number of authors. Pasamanick12in a recent article viewed the low interclinician agreement on diagnosis as an indictment of the present state of psychiatry and called for "the development of objective, measurable and verifiable criteria of classification based not on personal or parochial considerations, but on behavioral and other objectively measurable manifestations." Attempts by other investigators to subject clinical observations and judgments to objective measurement have resulted in a wide variety of psychiatric rating scales.4,15These have been well summarized in a review article by Lorr11on "Rating Scales and Check Lists for the Evaluation of Psychopathology." In the area of psychological testing, a variety of paper-and-pencil tests have been devised for the purpose of measuring specific

/pdf/an-inventory-for-measuring-depression-245h2atfgv.pdf

An inventory for measuring depression

The results of a multi-year research program to identify the factors associated with variations in subjective workload within and between different types of tasks are reviewed. Subjective evaluations of 10 workload-related factors were obtained from 16 different experiments. The experimental tasks included simple cognitive and manual control tasks, complex laboratory and supervisory control tasks, and aircraft simulation. Task-, behavior-, and subject-related correlates of subjective workload experiences varied as a function of difficulty manipulations within experiments, different sources of workload between experiments, and individual differences in workload definition. A multi-dimensional rating scale is proposed in which information about the magnitude and sources of six workload-related factors are combined to derive a sensitive and reliable estimate of workload.

Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

Inductive Representation Learning on Large Graphs

An eleven item clinician-administered Mania Rating Scale (MRS) is introduced, and its reliability, validity and sensitivity are examined. There was a high correlation between the scores of two independent clinicians on both the total score (0.93) and the individual item scores (0.66 to 0.92). The MRS score correlated highly with an independent global rating, and with scores of two other mania rating scales administered concurrently. The score also correlated with the number of days of subsequent stay in hospital. It was able to differentiate statistically patients before and after two weeks of treatment and to distinguish levels of severity based on the global rating.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/34DE3C8ED1EB65C54E87970C87BC0528/S0007125000198551a.pdf/div-class-title-a-rating-scale-for-mania-reliability-validity-and-sensitivity-div.pdf

A rating scale for mania: reliability, validity and sensitivity.

Click on the arrow button to move the variable name into Chapter 4 Creating a data file and entering data 39 Step 2: Enter the data Spss survival. The SPSS Survival Manual throws a lifeline to students and researchers grappling with this data analysis software.In this thoroughly revised edition of her. Download PDF SPSS Survival Manual A step by step guide to data analysis using SPSS. SPSS SURVIVAL MANUAL. Are you come upon Spss Survival Manual? Great! We have the file you need: spss survival manual. The spss survival manual. To cite this article: Yeung, Polly. SPSS survival manual. A step by step guide to data analysis using IBM SPSS (5th ed) (Book Review) (online). Aotearoa New.

SPSS Survival Manual

This is the revision of the classic text in the field, adding two new chapters and thoroughly updating all others. The original structure is retained, and the book continues to serve as a combined text/reference.

Nonparametric statistics for the behavioral sciences

Bargaining and group decision making

A nonparametric procedure is presented to test the null hypothesis that two independent samples come from the same population against the alternative hypothesis that the samples come from populations differing in variability or “spread.” Extensive tables of critical values are included for n 1≤n 2≤20. Large sample procedures are presented which include a correction for tied observations. The test is entirely distribution-free under the usual randomization procedures against the null hypothesis that the two distributions are identical. The absence of any normality assumption is a particularly important feature of the test, because its parametric alternative, the F test for variance differences, is quite sensitive to departures from normality. The test has the additional advantage of being directly applicable to non-numerical ordinal data.

https://www.jstor.org/stable/pdfplus/2281906.pdf

Sidney Siegel

Papers

Nonparametric statistics for the behavioral sciences

Bargaining and group decision making

A Nonparametric Sum of Ranks Procedure for Relative Spread in Unpaired Samples

Bargaining and Group Decision Making, Experiments in Bilateral Monopoly

Nonparametric Statistics for the Behavioral Sciences