Abstract : A study is reported of the variations in organizational commitment and job satisfaction, as related to subsequent turnover in a sample of recently-employed psychiatric technician trainees. A longitudinal study was made across a 10 1/2 month period, with attitude measures collected at four points in time. For this sample, job satisfaction measures appeared better able to differentiate future stayers from leavers in the earliest phase of the study. With the passage of time, organizational commitment measures proved to be a better predictor of turnover, and job satisfaction failed to predict turnover. The findings are discussed in the light of other related studies, and possible explanations are examined. (Modified author abstract)

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Organizational Commitment, Job Satisfaction and Turnover Among Psychiatric Technicians. Technical Report No. 16.

The History of Statistics: The Measurement of Uncertainty before 1900

Factor analysis is particularly suitable to extract few factors from the large number of related variables to a more manageable number, prior to using them in other analysis such as multiple regression or multivariate analysis of variance. It can be beneficial in developing of a questionnaire. Sometimes adding more statements in the questionnaire fail to give clear understanding of the variables. With the help of factor analysis, irrelevant questions can be removed from the final questionnaire. This study proposed a factor analysis to identify the factors underlying the variables of a questionnaire to measure tourist satisfaction. In this study, Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of Sphericity are used to assess the factorability of the data. Determinant score is calculated to examine the multicollinearity among the variables. To determine the number of factors to be extracted, Kaiser’s Criterion and Scree test are examined. Varimax orthogonal factor rotation method is applied to minimize the number of variables that have high loadings on each factor. The internal consistency is confirmed by calculating Cronbach’s alpha and composite reliability to test the instrument accuracy. The convergent validity is established when average variance extracted is greater than or equal to 0.5. The results have revealed that the factor analysis not only allows detecting irrelevant items but will also allow extracting the valuable factors from the data set of a questionnaire survey. The application of factor analysis for questionnaire evaluation provides very valuable inputs to the decision makers to focus on few important factors rather than a large number of parameters.

Factor Analysis as a Tool for Survey Analysis

Multivariate Analysis: Techniques for Educational and Psychological Research

Thematic analysis is frequently used to analyse qualitative data in psychology, healthcare, social research and beyond. An important stage in planning a study is determining how large a sample size may be required, however current guidelines for thematic analysis are varied, ranging from around 2 to over 400 and it is unclear how to choose a value from the space in between. Some guidance can also not be applied prospectively. This paper introduces a tool to help users think about what would be a useful sample size for their particular context when investigating patterns across participants. The calculation depends on (a) the expected population theme prevalence of the least prevalent theme, derived either from prior knowledge or based on the prevalence of the rarest themes considered worth uncovering, e.g. 1 in 10, 1 in 100; (b) the number of desired instances of the theme; and (c) the power of the study. An adequately powered study will have a high likelihood of finding sufficient themes of the desired prevalence. This calculation can then be used alongside other considerations. We illustrate how to use the method to calculate sample size before starting a study and achieved power given a sample size, providing tables of answers and code for use in the free software, R. Sample sizes are comparable to those found in the literature, for example to have 80% power to detect two instances of a theme with 10% prevalence, 29 participants are required. Increasing power, increasing the number of instances or decreasing prevalence increases the sample size needed. We do not propose this as a ritualistic requirement for study design, but rather as a pragmatic supporting tool to help plan studies using thematic analysis.

Supporting thinking on sample sizes for thematic analyses: A quantitative tool (vol 18, pg 669, 2015)

Chapter 1: Data Management.- Chapter 2: Descriptive Analysis.- Chapter 3: Chi-Square test and its Application.- Chapter 4: Correlation Matrix and Partial Correlation - Explaining Relationships.- Chapter 5: Regression Analysis and Multiple Correlations - For Estimating a Measurable Phenomenon.- Chapter 6: Hypothesis testing for decision making.- Chapter 7: One Way ANOVA - For testing the variability among group Means.- Chapter 8: Two Way Analysis of Variance - For Understanding the Causes of Variations.- Chapter 9: Analysis of Covariance- To study the role of covariate in Experimental Research.- Chapter 10: Cluster Analysis- For segmenting the population.- Chapter 11: Application of Factor Analysis - To study the Factor Structure among Variables.- Chapter 12: Application of Discriminant Analysis - For developing a classification model.- Chapter 13: Logistic Regression - Developing a model for Risk Analysis.- Chapter 14: Multidimensional Scaling for product positioning.

Data Analysis in Management with SPSS Software

Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework. We have implemented Mahout Interfaces for analyzing the data provided by review and rating site for movies.

Big Data Analysis: Recommendation System with Hadoop Framework

Repeated Measures Design for Empirical Researchers presents comprehensive coverage of the formation of research questions and the analysis of repeated measures using IBM SPSS and also includes the solutions necessary for understanding situations where the designs can be used. In addition to explaining the computation involved in each design, the book presents a unique discussion on how to conceptualize research problems as well as identify appropriate repeated measures designs for research purposes.

Repeated measures design for empirical researchers

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Jai Prakash Verma

Papers

Data Analysis in Management with SPSS Software

Big Data Analysis: Recommendation System with Hadoop Framework

Repeated measures design for empirical researchers

Big Data Analytics: Challenges And Applications For Text, Audio, Video, And Social Media Data

Testing Statistical Assumptions in Research