Cross-Classification Analysis Using Prediction Logic Versus Theory-Testing Logic: Comments on the Use of the DEL-Technique

2007 ◽  
Vol 42 (4) ◽  
pp. 491-511
Author(s):  
Robert A. W. Kok ◽  
Theo J. B. M. Postma ◽  
A. G. M. Steerneman
Keyword(s):  
Theory Testing ◽  
Classification Analysis ◽  
Prediction Logic ◽  
Cross Classification

Prediction Analysis in Political Research

1976 ◽  
Vol 70 (2) ◽  
pp. 509-535 ◽  
Author(s):  
David K. Hildebrand ◽  
James D. Laing ◽  
Howard Rosenthal
Keyword(s):  
Formal Language ◽  
A Priori ◽  
Measures Of Association ◽  
Prediction Logic ◽  
Specific Prediction ◽  
Cross Classification ◽  
Empirical Tests ◽  
Inference Methods ◽  
Post Hoc ◽  
Prediction Statement

Procedures for empirical tests of political theory should be designed to evaluate the success of the specific prediction being tested. This paper introduces; (1) a formal language, termed “prediction logic,” for stating an important class of predictions, including predictions that imply there will be relatively few cases in certain cells of a cross-classification; (2) a population measure, (“del”), that indicates the degree of success achieved by a statement in the language; (3) partial measures for the multivariate case; (4) bivariate statistical inference methods when the data arise from a sample rather than a population, both for an a priori prediction statement and for a statement selected post hoc. A number of well-known measures of “association” are measures for specific prediction logic statements. Research applications are indicated through the use of contingency tables appearing in APSR articles by Eulau and Eyestone, Goldberg, Muller, Riker and Zavoina, Rosenthal, Sawyer and MacRae, Sickles, Wolfinger and Field, and Wolfinger and Heifetz.

scholarly journals SARC-F Capacity to Tracking Sarcopenia in Women From Manaus-Am

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 144-144
Author(s):  
Myrian Faber ◽  
Fatima Baptista
Keyword(s):  
Body Mass Index ◽  
Body Weight ◽  
Grip Strength ◽  
Gait Speed ◽  
Kappa Statistic ◽  
Weighted Kappa ◽  
Objective Measures ◽  
Classification Analysis ◽  
Men And Women ◽  
Cross Classification

Abstract This study aimed to analyze the performance of SARC-F for the identification of sarcopenia in women from Manaus. For the analysis of the performance of the SARC-F, the criteria defined by the EWGSOP (2018) and the SDOC (2019) were considered. The sample consisted of 236 women aged 66.27±5.76 years. In addition to the SARC-F, a dynamometer was used to assess the muscle strength and gait speed was determined on a 2.44 m course. To identify sarcopenia, the following cut-off valueswere used: SARC-F ≥ 4 points; grip strength: EWGSOP <27kg for men and 16kg for women, SDOC <35.5kg for men and <20.0 kg for women; grip over body mass index (BMI, kg/m2) <1.05 for men and 0.79 for women; grip over body weight (BW, kg) <0.45 for men and <0.34 for women; gait speed ≤ 0.8 m/s for both men and women. The results revealed a prevalence of sarcopenia in 54% of the sample. The Kappa statistic intended to analyze the agreement between the SARC-F and the grip strength, the grip strength corrected for BMI or for BM, and the gait speed. The cross-classification analysis showed linear weighted Kappa coefficients near 0 with exception of gait speed (0.264±0.054, agreement in 61.2% of the participants) and grip strength when the cut off is 20 kg (0.248±0.062 - agreement in 63% of the participants). Cross-classification analysis between SARC-F and objective measures of physical capacity (grip strength and gait speed) showed linear weighted Kappa coefficients with slight or fair agreement in women from Manaus.

Analyzing Cross-Classification Data: An Improved Method for Predicting Events

1979 ◽  
Vol 16 (4) ◽  
pp. 539-544 ◽  
Author(s):  
Russell S. Winer ◽  
Michael J. Ryan
Keyword(s):  
Categorical Data ◽  
Prediction Methods ◽  
Improved Method ◽  
New Approach ◽  
Prediction Logic ◽  
Cross Classification

The limitations of prediction methods used to analyze categorical data are discussed. A new approach, prediction logic, is introduced that overcomes these limitations and provides a more precise test of complex theories.

scholarly journals Pola Jaringan Angkutan Laut Gugusan Pulau dalam Wilayah Kabupaten Pangkajene Kepulauan

2019 ◽  
Vol 23 (2) ◽  
pp. 195-207
Author(s):  
Petrus Filippus Latumahina ◽  
Syamsul Asri ◽  
Andi Sitti Chaerunnisa
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Regional Growth ◽  
Network System ◽  
Analysis Method ◽  
Local Growth ◽  
Classification Analysis ◽  
Sea Transportation ◽  
Cross Classification ◽  
Sea Area

The Pattern of Sea Transportation Network in the Region of Pangkajene and Islands Regency. Geographically, Pangkajene Islands Regency consist of land and islands, with a sea area of 11,464.44 km2, with 115 islands, 73 inhabited islands and 42 uninhabited islands. These conditions require a reliable sea transportation network to improve connectivity between the regional growth centers and the local growth centers. This study discusses the pattern of passenger movements, and plans for developing a sea transportation network system in the island cluster of Pangkajene Islands Regency using the method of cross classification analysis or category analysis to obtain the pattern of trip generation, as well as the gravity model analysis method for calculating the number of passenger movements. The results showed that, the high potential for passenger movement in the islands, but the availability of transportation networks and services is still low, identified several islands as the main economic center that serves as a hub port to connect the surrounding islands that function as feeders, including Balang Port Lompo and Sarappo Lompo Port in Liukang Tupabbiring District; Gondong Island Bali and Sabutung Island in Liukang Tupabbiring Utara District; Matalaang Port, Sapuka Lompo Harbor, and Sailus Harbor in Liukang Tangayya District; and Dewakang Lompo Port, Kalukalukuang Port and Pamantawang Port in Liukang Kalmas District.

scholarly journals Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records (Preprint)

2018 ◽  
Author(s):  
Luis María Béjar ◽  
Óscar Adrián Reyes ◽  
María Dolores García-Perea
Keyword(s):  
Dietary Intake ◽  
Weighted Kappa ◽  
Classification Analysis ◽  
Food Frequency ◽  
Dietary Recall ◽  
Average Percentage ◽  
Exact Agreement ◽  
Dietary Records ◽  
Cross Classification ◽  
Food And Drink

BACKGROUND One of the greatest challenges in nutritional epidemiology is improving upon traditional self-reporting methods for the assessment of habitual dietary intake. OBJECTIVE The aim of this study was to evaluate the relative validity of a new method known as the current-day dietary recall (or current-day recall), based on a smartphone app called 12-hour dietary recall, for determining the habitual intake of a series of key food and drink groups using a food frequency questionnaire (FFQ) and four dietary records as reference methods. METHODS University students over the age of 18 years recorded their consumption of certain groups of food and drink using 12-hour dietary recall for 28 consecutive days. During this 28-day period, they also completed four dietary records on randomly selected days. Once the monitoring period was over, subjects then completed an FFQ. The two methods were compared using the Spearman correlation coefficient (SCC), a cross-classification analysis, and weighted kappa. RESULTS A total of 87 participants completed the study (64% women, 56/87; 36% men, 31/87). For e-12HR versus FFQ, for all food and drink groups, the average SCC was 0.70. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 51.5%; exact agreement + adjacent was 91.8%, and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.51. For e-12HR versus the four dietary records, for all food and drink groups, the average SCC was 0.63. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 47.1%; exact agreement + adjacent was 89.2%; and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.47. CONCLUSIONS Current-day recall, based on the 12-hour dietary recall app, was found to be in good agreement with the two reference methods (FFQ & four dietary records), demonstrating its potential usefulness for categorizing individuals according to their habitual dietary intake of certain food and drink groups.

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