scholarly journals Validation Studies: Matters Of Dimensionality, Accuracy, And Parsimony With Predictive Discriminant Analysis And Factor Analysis

2004 ◽  
Vol 3 (1) ◽  
pp. 186-199
Author(s):  
David A. Walker
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Validation Studies ◽  
Predictive Discriminant Analysis

Multivariate consumption profiling (MCP) for intelligent meter systems: a methodology to define categories and levels

2010 ◽  
Vol 10 (5) ◽  
pp. 710-720 ◽  
Author(s):  
J. L. Solanas ◽  
M. R. Cussó
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Reference Population ◽  
Statistical Analyses ◽  
Conceptual Schema ◽  
Data Variability ◽  
Water Companies ◽  
Analyse Data

Multivariate Consumption Profiling (MCP) is a methodology to analyse the readings made by Intelligent Meter (IM) systems. Even in advanced water companies with well supported IM, full statistical analyses are not performed, since no efficient methods are available to deal with all the data items. Multivariate Analysis has been proposed as a convenient way to synthesise all IM information. MCP uses Factor Analysis, Cluster Analysis and Discriminant Analysis to analyse data variability by categories and levels, in a cyclical improvement process. MCP obtains a conceptual schema of a reference population on a set of classifying tables, one for each category. These tables are quantitative concepts to evaluate consumption, meter sizing, leakage and undermetering for populations and groupings and individual cases. They give structuring items to enhance “traditional” statistics. All the relevant data from each new meter reading can be matched to the classifying tables. A set of indexes is computed and thresholds are used to select those cases with the desired profiles. The paper gives an example of a MCP conceptual schema for five categories, three variables, and five levels, and obtains its classifying tables. It shows the use of case profiles to implement actions in accordance with the operative objectives.

The Influence of Fruit and Bud Volumes on Eucalypt Flowering—An Exploratory Analysis

1998 ◽  
Vol 46 (2) ◽  
pp. 281 ◽  
Author(s):  
Marie R. Keatley ◽  
Irene L. Hudson
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Development Time ◽  
Growing Season ◽  
Exploratory Analysis ◽  
Large Individual ◽  
Bud Development ◽  
Seed Maturity ◽  
Development Period

The appearance of buds and development of fruits in 51 species of Eucalyptus L’Hér. were examined to determine whether bud and fruit volume had an influence on flowering. These variables exert an influence on eucalypt flowering as individuals and as members within an umbel. Flowering earlier in the growing season is characteristic of species with large, individual bud and fruit volume and those that have a large umbel volume. The number of buds and fruits in an umbel is influenced by volume of the individuals. Development time of buds was longer in umbels which had a larger total volume. Bud and fruit umbel volume was positively correlated with the timing of bud appearance as was seed maturity with individual fruit and fruit umbel volume. A threshold of bud and fruit volume was indicated below which seed maturity and time of flowering is not reflected by volume. Discriminant analysis used nine predictors to assign 80% (overall) of section members of Adnataria (91%), Bisectaria (60%), Maidenaria (67%) and Renantheria (87%) to their true groups. Across groups examination adduced the possibility of each group having a distinct bud development period and a discernible cessation of flowering. Five clusters were revealed whose members were significantly differentiated on seed maturity, section, individual fruit volumes, bud and fruit umbel volumes. Factor analysis outlined four factors—buds, fruits, timing and development—which accounted for 78.9% of the variance.

Do all roads lead to Rome?: Modeling register variation with factor analysis and discriminant analysis

2018 ◽  
Vol 14 (2) ◽  
pp. 233-273 ◽  
Author(s):  
Jesse Egbert ◽  
Douglas Biber
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Canonical Discriminant Analysis ◽  
Statistical Techniques ◽  
Systematic Relationship ◽  
Case Study Analysis ◽  
Register Variation ◽  
Analysis Of Variation ◽  
Occurrence Patterns

Abstract Previous theoretical and empirical research on register variation has argued that linguistic co-occurrence patterns have a highly systematic relationship to register differences, because they both share the same functional underpinnings. The goal of this study is to test this claim through a comparison of two statistical techniques that have been used to describe register variation: factor analysis (as used in Multi-Dimensional analysis, MDA) and canonical discriminant analysis (CDA). MDA and CDA have different statistical bases and thus give priority to different analytical considerations: linguistic co-occurrence in the case of MDA and the prediction of register differences in the case of CDA. Thus, there is no statistical reason to expect that the two techniques, if applied to the same corpus, will produce similar results. We hypothesize that although MDA and CDA approach register variation from opposite sides, they will produce similar results because both types of statistical patterns are motivated by underlying discourse functions. The present paper tests this claim through a case-study analysis of variation among web registers, applying MDA and CDA to analyze register variation in the same corpus of texts.

scholarly journals Expression of Depressive Symptoms in a Nonclinical Brazilian Adolescent Sample

2005 ◽  
Vol 50 (3) ◽  
pp. 129-136 ◽  
Author(s):  
Clarice Gorenstein ◽  
Laura Andrade ◽  
Elaine Zanolo ◽  
Rinaldo Artes
Keyword(s):  
Factor Analysis ◽  
Public Schools ◽  
Depressive Symptoms ◽  
Discriminant Analysis ◽  
Private School ◽  
Depressive Symptomatology ◽  
Preventive Strategy ◽  
International Studies ◽  
School Students ◽  
Student Sample

Objective: This study aimed to detect the prevalence of depressive symptomatology and its expression in a nonclinical Brazilian adolescent student sample. Method: A sample of students from private and public schools ( n = 1555, aged 13 to 17 years) answered the Beck Depression Inventory (BDI). We performed factor analysis of the BDI as an indicator of the expression of depressive symptomatology. The following cut-off scores defined nonclinical subgroups: “nondepressed,” BDI < 15; “dysphoria,” BDI 16 to 20; and “depressed,” BDI > 20. We used discriminant analysis to test whether these subgroups could be separated by the depression-specific and nonspecific items. Results: The point prevalence of depression was 7.6%, according to the BDI cut-off of 20. Girls had higher scores than boys in several items. Scores increased with age. Students from public schools had higher scores than did private school students. Factor analysis showed 2 common factors for the total sample and for each sex: the cognitive affective dimension and the somatic nonspecific dimension. In the adolescents showing clinical depression, items related to self-depreciation, sense of failure, guilty feelings, self-dislike, suicidal wishes, and distortion of body image were common components of BDI factors. Discriminant analysis showed that the BDI highly discriminates depressive symptomatology in adolescent students and also measures specific aspects of depression. Conclusions: The BDI is useful as a measure of specific aspects of depression in nonclinical adolescent samples; it was able to detect depression in approximately 7% of the surveyed population. The expression of depressive symptoms in a Brazilian adolescent population is compatible with international studies in this age group. Detecting depressive symptoms in a school population is a critical preventive strategy; to avoid damage to the learning process, it should be followed with further referral to treatment when needed.

Identification of Personality Groups in a Comprehensive Rehabilitation Center Population

1973 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
B. Douglas Rice ◽  
Vernon L. Glenn
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Rehabilitation Services ◽  
Personality Factor ◽  
Rehabilitation Center ◽  
Sixteen Personality Factor Questionnaire ◽  
Sixteen Personality Factor ◽  
Personality Factor Questionnaire ◽  
Comprehensive Rehabilitation ◽  
Components Method

Recent legislation has extended rehabilitation services from the groups traditionally served to the socially and economically disadvantaged. Since many of these clients cannot be classified in the traditional modes, this study developed a system of classification according to personality traits intended to be useful to counselors in dealing with all types of clients.A sample of 135 clients was administered Form E of the Sixteen Personality Factor Questionnaire. Factor analysis (principle components method) and discriminant analysis differentiated three personality groups: (1) normal adjusted, (2) assertive-aggressive, and (3) passive-dependent. Appropriate counseling techniques for each group are suggested.

scholarly journals PERBEDAAN KEPUTUSAN MAHASISWA DALAM MEMILIH STIE DI JAWA TIMUR

2017 ◽  
Vol 11 (3) ◽  
pp. 321
Author(s):  
Nur Handayani
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Learning System ◽  
Internal Factor ◽  
Analysis Technique ◽  
Clean Environment ◽  
Two Factors ◽  
Boarding House ◽  
Research Population ◽  
System 2

This research aim is to know the factors which influence student in selecting STIE in east java and to know factor differences which influence student’s decision in selecting STIESIA Surabaya,STIE Perbanas and STIE Malangkucecwara.This research population is college student in the second semester of 2003/2004. Data analysis technique uses .factor analysis and discriminant analysis. Factor analysis result indicates that proposed hypothesis is can not be rejected or there are several factors which influence the student’s decision differences in  selecting STIE (13 variables in 12 .factors).  Discriminant analysis result indicates that the variables which are differenciate the decision. The variables are divided in two factors, which are internal factor and eksternal  factor. Internal  factor consists of (1) reguler learning system, (2) school  fee credit, while eksternal factor consists of (I) health facility, clean environment and (3) boarding house location.

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