scholarly journals Influence of sex, age, and education on mood profile clusters

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245341
Author(s):  
Peter C. Terry ◽  
Renée L. Parsons-Smith ◽  
Rachel King ◽  
Victoria R. Terry
Keyword(s):  
Cluster Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Normative Data ◽  
Function Analysis ◽  
Negative Mood ◽  
Heterogeneous Sample ◽  
Highly Educated ◽  
Education Status ◽  
Mood Scale

In the area of mood profiling, six distinct profiles are reported in the literature, termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. We investigated if the prevalence of the six mood profiles varied by sex, age, and education among a large heterogeneous sample. The Brunel Mood Scale (BRUMS) was completed via the In The Mood website by 15,692 participants. A seeded k-means cluster analysis was used to confirm the six profiles, and discriminant function analysis was used to validate cluster classifications. Significant variations in the prevalence of mood profiles by sex, age, and education status were confirmed. For example, females more frequently reported negative mood profiles than males, and older and more highly educated participants had a higher prevalence of the iceberg profile than their younger and lesser educated counterparts. Findings suggest that refinement of the existing tables of normative data for the BRUMS should be considered.

A Technique for Identifying the Subgroup Membership of Certain Misarticulating Children

1982 ◽  
Vol 25 (2) ◽  
pp. 162-166 ◽  
Author(s):  
Anita F. Johnson ◽  
Ralph L. Shelton ◽  
William B. Arndt
Keyword(s):  
Cluster Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Auditory Processing ◽  
Function Analysis ◽  
Cluster Membership ◽  
Reading Measures

Cluster analysis was used to identify two homogenous clusters of 8–9½-year-old children who misarticulated /s/, /r/, or both. The analysis was based on the children's scores on 40 measures of language, reading, auditory processing, and other variables. Discriminant function analysis was then used to identify a subset of five measures and a means of computing classification scores. These measures and the classification scores can be used to identify the cluster membership of new subjects. The use of classification scores for identifying cluster membership was cross-validated against cluster analysis of a second group of children. The two clusters are described in terms of their performance on language and reading measures.

scholarly journals Statistical Analyses: Possible Reasons for Unreliability of Source Tracking Efforts

2005 ◽  
Vol 71 (8) ◽  
pp. 4690-4695 ◽  
Author(s):  
Clarivel Lasalde ◽  
Roberto Rodríguez ◽  
Gary A. Toranzos
Keyword(s):  
Cluster Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Microbiological Quality ◽  
Statistical Analyses ◽  
Indicator Organisms ◽  
E Coli ◽  
Quality Of Water

ABSTRACT Analyses for the presence of indicator organisms provide information on the microbiological quality of water. Indicator organisms recommended by the United States Environmental Protection Agency for monitoring the microbiological quality of water include Escherichia coli, a thermotolerant coliform found in the feces of warm-blooded animals. These bacteria can also be isolated from environmental sources such as the recreational and pristine waters of tropical rain forests in the absence of fecal contamination. In the present study, E. coli isolates were compared to E. coli K12 (ATCC 29425) by restriction fragment length polymorphism using pulsed-field gel electrophoresis. Theoretically, genomic DNA patterns generated by PFGE are highly specific for the different isolates of an organism and can be used to identify variability between environmental and fecal isolates. Our results indicate a different band pattern for almost every one of the E. coli isolates analyzed. Cluster analysis did not show any relations between isolates and their source of origin. Only the discriminant function analysis grouped the samples with the source of origin. The discrepancy observed between the cluster analysis and discriminant function analysis relies on their mathematical basis. Our validation analyses indicate the presence of an artifact (i.e., grouping of environmental versus fecal samples as a product of the statistical analyses used and not as a result of separation in terms of source of origin) in the classification results; therefore, the large genetic heterogeneity observed in these E. coli populations makes the grouping of isolates by source rather difficult, if not impossible.

Academic Goal Profiles: A Comparison of Blind and Sighted Students

2012 ◽  
Vol 15 (3) ◽  
pp. 1043-1054 ◽  
Author(s):  
Francisco Pablo Holgado Tello ◽  
Leandro Navas Martínez ◽  
Irene Jover Mira
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Visually Impaired ◽  
Function Analysis ◽  
Goal Theory ◽  
Academic Goals ◽  
Visually Impaired Students

Based on academic goal theory, we compared the goals of visually impaired and sighted students. Participants included 171 students affiliated with ONCE (the National Spanish Organization for the Blind) and 163 sighted students, ranging from 8 to 27 years of age, who responded to the Spanish adaptation (González, Torregrosa, & Navas, 2002) of the goals questionnaire created by Hayamizu, Ito, and Yohiazaki (1989) and Hayamizu and Weiner (1991). Factor analysis indicated that goals varied among these students and that new ones arose in this study; furthermore, the model remained invariable regardless of students' status (visually impaired versus sighted). Cluster analysis revealed three distinct profiles in terms of academic goals, while discriminant function analysis suggested these profiles vary as a function of students' status (visually impaired versus sighted). It seems that visually impaired students hold more learning-oriented academic goals.

Predictive Effectiveness of Mood Measures

2002 ◽  
Vol 94 (3) ◽  
pp. 785-791 ◽  
Author(s):  
Andrew M. Lane ◽  
Helen J. Lane
Keyword(s):  
High School Students ◽  
Negative Affect ◽  
Discriminant Function ◽  
Junior High School ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Positive And Negative Affect ◽  
School Students ◽  
Fitness Test ◽  
Mood Scale

The present study compared the predictive effectiveness of mood states and the Positive and Negative Affect Schedule models of mood. 38 junior high school students completed the Brunel University Mood Scale to assess anger, confusion, depression, fatigue, tension, and vigor and the PANAS 5 min. before a fitness test. Performance was self-referenced by comparing fitness test scores with a previous best score assessed four weeks earlier. Participants who equaled or bettered their previous best score were classified as Successful. Those who did not match their previous score were classified as Unsuccessful. Discriminant function analysis indicated that 31 participants (81.6%) could be correctly classified as Successful or Unsuccessful based on preperformance Brunel Mood scores. Discriminant function coefficients indicated that Confusion, Fatigue, and Vigor contributed significantly to the discrimination. For the Positive and Negative Affect Schedule, discriminant function analysis indicated that 26 participants (68.4%) could be correctly classified as Successful or Unsuccessful, with Negative Affect and Positive Affect contributing significantly to the discrimination. Summarizing negative mood into a single factor may lead to a substantial loss of information. Research on mood and performance relationships should use the mood dimensions assessed in the Brunel University Mood Scale rather than the Positive and Negative Affect Schedule.

scholarly journals A New Postclassic Chronology for Yautepec, Morelos

1996 ◽  
Vol 7 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Timothy S. Hare ◽  
Michael E. Smith
Keyword(s):  
Cluster Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Postclassic Period ◽  
Radiocarbon Dates ◽  
Early Colonial ◽  
Late Postclassic ◽  
Yautepec Valley ◽  
Calendar Dates

AbstractWe describe the derivation of a new archaeological chronology for the Postclassic period at Yautepec, Morelos. We first apply cluster analysis to ceramic type frequencies for 47 excavated contexts to identify groups of related ceramic collections. This classification is then extended to several hundred additional collections using discriminant-function analysis. The groups are evaluated successfully against stratigraphy and radiocarbon dates, resulting in their designation as chronological phases. Radiocarbon determinations are then used to assign calendar dates to the phases. The new chronology consists of one Middle Postclassic phase (Pochtla), two Late Postclassic phases (Atlan, Molotla), and one Early Colonial phase (Santiago). We also explore issues concerning the extension of this chronology to other Postclassic sites in the Yautepec Valley.

Morphometric differentiation of five biotypes of Lysiphlebus fabarum (Marshall) (Hymenoptera: Braconidae: Aphidiinae) in Iran

Zootaxa ◽  
2011 ◽  
Vol 2745 (1) ◽  
pp. 43 ◽  
Author(s):  
HOSSEIN BARAHOEI ◽  
SEYED MASSOUD MADJDZADEH ◽  
MOHSEN MEHRPARVAR
Keyword(s):  
Cluster Analysis ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Aphis Gossypii ◽  
Size Variation ◽  
Morphological Characters ◽  
Aphis Fabae ◽  
Group Method ◽  
Melanaphis Sacchari

Morphometric analyses were carried out on five biotypes of Lysiphlebus fabarum (Marshall) (Hymenoptera: Braconidae: Aphidiinae) associated with five host aphid species, Aphis craccivora Koch, Aphis fabae Scopoli, Aphis gossypii Glover, Brachyunguis zygophylli (Nevsky) and Melanaphis sacchari (Zehntner). Sixteen morphological characters were measured on female specimens of L. fabarum. Two multivariate discriminant analyses, discriminant function analysis and Unweighted Pair Group Method with Arithmetic mean cluster analysis, were employed to discriminate among L. fabarum biotypes on a morphological basis. Both discriminant function analysis and cluster analysis revealed significant morphological differences. Specimens of L. fabarum reared on Melanaphis sacchari/ Sorghum halepense association showed clear differentiation from other L. fabarum biotypes. A stepwise discriminant function analysis selected Distance between R1-R2 junction and outer margin of wing, Metacarpus length, Width of first tergit, Basal vein length and Femur length that showed the highest contribution to the separation of host-adapted biotypes. A dendrogram of the cluster analysis based on data of squared Euclidean distances classified biotypes in different groups. This study demonstrates that morphometric analysis of morphological characters can efficiently discriminate among host-associated groups based on size variation and clearly separate biotypes of L. fabarum. We concluded that L. fabarum biotypes are not homogeneous morphological entities, and represent different host-associated forms.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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