scholarly journals Statistical analysis: a practical guide for psychiatrists

2016 ◽  
Vol 22 (4) ◽  
pp. 251-259 ◽  
Author(s):  
Vasilis Nikolaou

SummaryThis article is a practical guide for psychiatrists who want to apply basic and straightforward statistics in their research. It describes ways of summarising data and provides an overview of statistical tests for comparing patients' characteristics. Measures of association such as correlation and regression are also explained, along with principal components analysis, a method for reducing the dimensionality of data. Explanations are clarified using data from the published studies.

Author(s):  
Sofia D Anastasiadou

Nowadays, there is a substantial improvement and utilisation of pattering methods in the science of educational research, a comparison of multivariate methods in group/cluster identification in the scientific domain of quantitative research has not been carried out. This study analyses two different statistical techniques: i.e., the principal components analysis (PCA) and the implicative statistical analysis (ASI). A survey was carried out using a structured questionnaire for a sample of 135 nurses which studied in the School of Pedagogical and Technological Education in order to be qualified in respect The study focuses on the presentation of the two main types of clustering methods, της ASI and L’ Analysee Factorielle des Correspondances (AFC). AFC’s results made it evident that Emotionality, Self-control, Sociability, General items of EI constructs are shaped attitudes and reveal the latent dimension of respondents psychological attributes related to EI conceptual constructs. Keywords: L’ Analysee Factorielle des Correspondances, principal components analysis, implicative statistical analysis, research.


2002 ◽  
Vol 32 (3) ◽  
pp. 535-544 ◽  
Author(s):  
J. H. MacCABE ◽  
E. ALDOURI ◽  
T. A. FAHY ◽  
P. C. SHAM ◽  
R. M. MURRAY

Background. Many people who develop schizophrenia have impairments in intellectual and social functioning that are detectable from early childhood. However, some patients do not exhibit such deficits, and this suggests that they may have suffered less neurodevelopmental damage. We hypothesized that the aetiology and form of schizophrenia may differ in such patients. We therefore studied a group of schizophrenic patients who were functioning well enough to enter university prior to illness onset.Methods. The casenotes of 46 university-educated patients and 48 non-university-educated patients were rated on several schedules including the OPCRIT checklist, and the two groups were compared using univariate statistical techniques. Principal components analysis was then performed using data from all patients, and the factor scores for each principal component were compared between groups.Results. Univariate analyses showed the university-educated patients had an excess of depressive symptoms, and a paucity of core schizophrenic symptoms. Four principal components emerged in the principal components analysis: mania, biological depression, schizophrenic symptoms, and a reactive depression. University-educated patients scored significantly higher on the reactive depression principal component, and lower on the schizophrenic symptoms principal component, than the non-university-educated patients.Conclusions. University-educated patients may have a non-developmental subtype of schizophrenia.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

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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