Objectives: Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with : Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). Methods: A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) curve analyses and to defi ne an optimum cut-off score for the LJQ. curve analyses and to defi ne an optimum cut-off score for the LJQ. Results: The 19-item LJQ had satisfactory internal : The 19-item LJQ had satisfactory internal consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. Conclusions: Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. KEYWORDS Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern

Development and Validation of A

Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference: Discrete Variables. 4. More Inference Algorithms. 5. Influence Diagrams. III. LEARNING. 6. Parameter Learning: Binary Variables. 7. More Parameter Learning. 8. Bayesian Structure Learning. 9. Approximate Bayesian Structure Learning. 10. Constraint-Based Learning. 11. More Structure Learning. IV. APPICATIONS. 12. Applications. Bibliography. Index.

Learning Bayesian networks

Centering Decisions in Hierarchical Linear Models: Implications for Research in Organizations

Despite past entreaties to organizational theorists and researchers to address levels issues more carefully, levels issues continue to arouse confusion and controversy within the organizational literature. We highlight three alternative assumptions that underlie the specification of levels of theory throughout organizational behavior: (a) homogeneity within higher level units, (b) independence from higher level units, and (c) heterogeneity within higher level units. These assumptions influence the nature of theoretical constructs and propositions and should, ideally, also influence data collection, analysis, and interpretation. Greater attention to levels issues will strengthen organizational theory development and research.

/pdf/levels-issues-in-theory-development-data-collection-and-2nkjjgt6a2.pdf

Levels Issues in Theory Development, Data Collection, and Analysis

data are becoming quite common in political science and provide numerous opportunities for theory testing and development. Unfortunately this type of data typically generates a number of statistical problems, of which clustering is particularly impor? tant. To exploit the opportunities of? fered by multilevel data, and to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics and sociology?multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, and determine how layers interact and impact a dependent variable of interest. Our objective in this article is to introduce the logic and statistical theory behind multilevel models, to illustrate how such models can be applied fruitfully in political science, and to call atten? tion to some of the pitfalls in multilevel analysis.

http://www.jstor.org/stable/pdfplus/3088424.pdf

Modeling Multilevel Data Structures

Contextual Analysis: Concepts and Statistical Techniques

On the existence of forces restoring party competition

An experimenter threw individually 219 different dice of four different brands and recorded even and odd outcomes for one block of 20,000 trials for each die—4,380,000 throws in all. The resulting data on runs offer a basis for comparing the observed properties of such a physical randomizing process with theory and with simulations based on pseudo-random numbers and RAND Corporation random numbers. Although generally the results are close to those forecast by theory, some notable exceptions raise questions about the surprise value that should be associated with occurrences two standard deviations from the mean. These data suggest that the usual significance level may well actually be running from 7 to 15 percent instead of the theoretical 5 percent.

Bias and runs in dice throwing and recording: A few million throws

According to the statistical properties of the chi-square distribution, a variable which is distributed according to the chi-square distribution with p degrees of freedom can be written as the sum ofp chi-square variables, each with one degree of freedom This means that a contingency table which gives rise to a chi-square with p degrees of freedom can be decomposed into as many as p smaller tables The magnitudes of the chi-squares for these smaller tables will often give a better understanding of the relationship between the two variables than was possible to obtain from the overall chi-square for the original table This paper gives explicit rules for how to decompose a table into smaller tables, and the rules are illustrated with an example from a content analysis of value orientations in different maga zines

Decomposing Chi-Square A Forgotten Technique

The ecological fallacy of relating variables on the group level, when the individual-level relationship is desired, can only be avoided by using individual-level data. This paper gives some conditions for occasions when individual-level data can successfully be recovered from grouped data. Such a recovery is illustrated using data on urban or rural residence and participation or not in the labor force as an example. The conditions are given in terms of the distinction between individual-and group-level effects of one variable on another. Recovering individual data, on the one hand, and the study of individual and group-level effects, on the other hand, epresent two separate areas of thought that have received considerable attention. Here a link is made between the two lines of development to facilitate the recovery of individual-level data. Some consequences of the models for research design and recovery of historical data are explored.

/pdf/recovering-individual-data-in-the-presence-of-group-and-2a5jzs2au1.pdf

Gudmund R. Iversen

Papers

Contextual Analysis: Concepts and Statistical Techniques

On the existence of forces restoring party competition

Bias and runs in dice throwing and recording: A few million throws

Decomposing Chi-Square A Forgotten Technique

Recovering Individual Data in the Presence of Group and Individual Effects