Delirium in elderly people: a study of a psychiatric liaison service in north India

2011 ◽  
Vol 24 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Sandeep Grover ◽  
Natasha Kate ◽  
Munish Agarwal ◽  
Surendra Kumar Mattoo ◽  
Ajit Avasthi ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Rating Scale ◽  
North India ◽  
Physical Disorder ◽  
Inpatient Stay ◽  
The Mean ◽  
Components Analysis ◽  
Three Factor

ABSTRACTBackground: Very few studies from India have studied the phenomenology of delirium. The aim of the present study was to study the phenomenology as measured using the Delirium Rating Scale-Revised-98 (DRS-R98), the associated etiologies and the outcome of delirium among the elderly participants seen by the consultation-liaison psychiatric service in India. In addition, an attempt was made to study the factor structure of symptoms using principal components analysis.Methods: The case notes of 109 elderly patients referred to psychiatry liaison services were reviewed.Results: The mean age of the sample was 73.35 years (SD: 7.44; range 65–95 years) and two-thirds of the sample had hospital emergent delirium. The mean DRS-R98 severity score was 18.77 and the DRS-R98 total score was 24.81. In 15 patients the DRS-R98 scores were in the subsyndromal range. Among the various symptoms present, most patients had sleep-wake cycle disturbance, disturbance in orientation, attention and short-term memory impairments, fluctuation of symptoms, temporal onset of symptoms and a physical disorder. Principal components analysis identified three factors which explained 43.5% of variance of symptomatology and it yielded a three-factor structure. Endocrine/metabolic disturbances were the commonest associated etiological category with delirium. The mean hospital stay after being referred to psychiatry referral services was 8.89 days, after which delirium improved in 58.7% of cases. The mortality rate during the inpatient stay was 16.5%.Conclusions: Results suggest that the symptoms of delirium as assessed by DRS-R98 separate out into a three-factor structure. Delirium is commonly associated with metabolic endocrine disturbances and about one-sixth of the patients die during the short inpatient stay.

Factor Structure of the Depression-Proneness Rating Scale

1987 ◽  
Vol 61 (3) ◽  
pp. 863-866
Author(s):  
Sung-Mook Hong
Keyword(s):  
University Students ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Rating Scale ◽  
Varimax Rotation ◽  
Cognitive Components ◽  
Depression Proneness ◽  
Components Analysis ◽  
Depression Scales

The Zemore Depression-Proneness Rating Scale was administered to 208 university students. A principal components analysis with varimax rotation identified three factors representing Negative Self-attitude, Performance Difficulty, and Lack of Appetite. Some similarity between the present factors and those of the Beck Depression Inventory was discussed. Because the scale overemphasizes cognitive components, attention to items on the somatic aspects of depression was suggested. However, it should be noted that depression-proneness appears to be the feature of Zemore's scale that sets it apart from other depression scales.

The Reminiscence Functions Scale: A Replication

1997 ◽  
Vol 44 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Jeffrey Dean Webster
Keyword(s):  
Problem Solving ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Age Differences ◽  
Internal Consistency ◽  
Principal Components ◽  
Validation Study ◽  
Scale Construction ◽  
Identity Problem ◽  
Components Analysis

This article reports the findings of a replication and validation study of the factor structure of the recently developed Reminiscence Functions Scale (RFS) [1]. Three hundred and ninety-nine adult subjects ranging in age from seventeen to forty-five years ( M age = 22.7, SD = 5.7) completed the RFS. A principal components analysis indicated the viability of an eight-factor scale which strongly parallels the earlier scale construction. Factors were labeled: Boredom Reduction, Death Preparation, Identity, Problem-Solving, Conversation, Intimacy Maintenance, Bitterness Revival, and Teach/Inform. Internal consistency scores ranged from .74 to .86 and closely duplicated original scores. Age differences on Death Preparation and Teach/Inform were replicated. Potential uses of the RFS are documented.

Factor Structure of the Spanish Version of the Object-Spatial Imagery and Verbal Questionnaire

2011 ◽  
Vol 108 (2) ◽  
pp. 470-476 ◽  
Author(s):  
Alfredo Campos ◽  
María José Pérez-Fabello
Keyword(s):  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Original Version ◽  
Spanish Version ◽  
University Graduates ◽  
Varimax Rotation ◽  
Spatial Imagery ◽  
Components Analysis ◽  
Questionnaire Measures

The reliability and factor structure of the Spanish version of the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ) were assessed in a sample of 213 Spanish university graduates. The questionnaire measures three types of processing preferences (verbal, object imagery, and spatial imagery). Principal components analysis with varimax rotation identified three factors, corresponding to the three scales proposed in the original version, explaining 33.1% of the overall variance. Cronbach's alphas were .72, .77, and .81 for the verbal, object imagery, and spatial imagery scales, respectively.

Allport-Vernon-Lindzey Study of Values: 1. Factor Structure for a Combined Sample of Male and Female College Students

1970 ◽  
Vol 27 (3) ◽  
pp. 955-958 ◽  
Author(s):  
Rio Sciortino
Keyword(s):  
College Students ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
The Self ◽  
Female College Students ◽  
Male And Female ◽  
Female College ◽  
Components Analysis

A principal components analysis was performed on the self-ratings (for a combined sample) obtained from the Allport-Vernon-Lindzey Study of Values ( N = 150 combined sample of 102 male and 48 female college students). The obtained principal components were then rotated according to the varimax procedure. The varimax factors obtained were: esthetic, social, and religious.

Development of a Short-Form Conservatism Scale Suitable for Mail Surveys

1993 ◽  
Vol 72 (2) ◽  
pp. 419-422 ◽  
Author(s):  
David M. Collins ◽  
Peter F. Hayes
Keyword(s):  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Short Form ◽  
Coefficient Alpha ◽  
Varimax Rotation ◽  
Mail Surveys ◽  
Five Factors ◽  
Components Analysis

Analysis of data from 255 U.S. pharmacists provides support for the consistency and validity of this short-form conservatism scale. The scale returned a coefficient alpha of 0.82, and principal components analysis yielded a strong general conservatism factor. Varimax rotation produced five factors consistent with the factor structure of the original Conservatism Scale.

Further Considerations of the Multidimensionality and Factor Structure of the Rotter Locus of Control Scale

1987 ◽  
Vol 60 (3_part_2) ◽  
pp. 1059-1062
Author(s):  
Howard N. Anderson ◽  
Stephen Madonna ◽  
Glenda K. Bailey ◽  
Andrea L. Wesley
Keyword(s):  
Locus Of Control ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
Total Variance ◽  
Varimax Rotation ◽  
Factor Solution ◽  
Control Scale ◽  
Two Factors ◽  
Components Analysis

The present study was designed to determine the number and nature of factors involved in Rotter's I-E Scale and to examine the question of the multidimensionality of the scale. Subjects were 329 college undergraduates. Principal components analysis was performed on the 23 pertinent items of the scale, followed by Varimax rotation. Multiple criteria indicated a 10-factor solution which accounted for 61% of the total variance. The first two factors were strongly similar to those reported previously. Results support Rotter's (1975) contention that subclasses within the scale will vary from sample to sample.

Planktonic Biomass Trajectories In Lake Ecosystems

1983 ◽  
Vol 40 (10) ◽  
pp. 1752-1760 ◽  
Author(s):  
Michael A. Gates ◽  
Ann P. Zimmerman ◽  
W. Gary Sprules ◽  
Roy Knoechel
Keyword(s):  
Hypothesis Testing ◽  
Principal Components Analysis ◽  
Biomass Allocation ◽  
Principal Components ◽  
Primary Component ◽  
Size Category ◽  
Data Set ◽  
Lake Ecosystems ◽  
The Mean ◽  
Components Analysis

We introduce a method, based on principal components analysis, for studying temporal changes in biomass allocation among 16 size–category compartments of lake plankton. Applied to data from a series of 12 Ontario lakes over three sampling seasons, the technique provides a simple means of visualizing shifts in patterns of biomass allocation, and it allows comparative analyses of biomass fluctuations in different lakes. Each of the primary component axes is interpretable. Furthermore, a large proportion of the variance in both the mean position of a lake and its movement along these axes is interpreted as a function of lake physicochemistry. The analysis also provides weighted scores for use in hypothesis testing which are an improvement over mean biomass values alone, because they take into account the structure of variation in the data set.

Dimensional Structure of the Imaginal Processes Inventory for Children (IPI-C)

1992 ◽  
Vol 12 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Marcel W. Vooijs ◽  
Johannes W. J. Beentjes ◽  
Tom H. A. van der Voort
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Dimensional Structure ◽  
Factor Solution ◽  
Components Analysis ◽  
Three Factor

The dimensional structure of Dutch versions of the Imaginal Processes Inventory for Children (IPI-C) was investigated with children in Grades 3–6. According to Study 1, the reliabilities of the nine subscales distinguished within the American instrument were not satisfactory. A principal components analysis (PCA) performed on the subscales resulted in three factors that paralleled the three fantasy styles found previously in a similar analysis of the American IPI-C. However, two of the three factors found were defined by subscales that had a number of items in common. When these shared items were removed from the subscales, a different three-factor solution emerged. The three fantasy styles found in the latter analysis were encountered again in Study 2, which was conducted with an adjusted version of the IPI-C. Three reliable fantasy scales were constructed, for positive-intense, heroic-aggressive, and dysphoric fantasy. The results of the two studies justify further research into the validity of these three fantasy scales.

Children's Fears: A Survey of Italian Children Ages 6 to 10 Years

2009 ◽  
Vol 104 (3) ◽  
pp. 971-988 ◽  
Author(s):  
Silvia Salcuni ◽  
Daniela di Riso ◽  
Claudia Mazzeschi ◽  
Adriana Lis
Keyword(s):  
Principal Components Analysis ◽  
Factor Structure ◽  
Age Differences ◽  
Principal Components ◽  
Factor Scores ◽  
Mainstream Classrooms ◽  
Average Factor ◽  
Fear Survey Schedule ◽  
Components Analysis ◽  
Italian Children

The aim of this study was to explore children's fears. The article reports on average factor scores of a study carried out in Italy using the Fear Survey Schedule for Children (here, the “Fear Survey”; Ollendick, 1983) with normal 6- to 10-yr.-old children (931 girls, 914 boys). Participants were 1,845 children, recruited in mainstream classrooms. Respondents were asked to complete the schedule indicating their fears and the intensity of such fears. A principal components analysis yielded a four-factor structure (1: Death and Danger, 2: Injury and Animals, 3: Failure and Criticism, 4: Fear of the Unknown). Average factor scores showed significant differences across the factors and according to sex and age. Girls reported significantly higher fearfulness than boys. Age differences were found on some factors.

Robust Statistics: A Survey and Some Prescriptions

1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Standard Deviation ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Robust Statistics ◽  
Robust Methods ◽  
The Mean ◽  
Components Analysis

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.

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