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How to Use Organizational Behavior Analysis in Organizational Behavior Assignments

Different people interact in different ways in a social group. The scientific study that decodes such behavioral patterns is called organizational behavior. Organizational behavior analysis applies this study to corporate environments in order to understand how employees behave in such environment and what can be done to improve such behavioral patterns in order to improve organizational efficiency.

Organizational Behavior Analysis: A disciplinary history

Organizational Behavior is a part of industrial and organizational psychology and emerged in the 1940s and 50s. However, it was not until the 1970s that it developed as an independent field of study. The field became increasingly quantitative in the 1960s and 1970s and produced critical ideas such as bounded rationality, informal organization and resource dependence. Some of the important theorists of this field are Herbert Simon, James G. March, Chester Barnard, Henri Fayol and Victor Vroom.

Today organizational behavior analysis is a major sub-field of general management and is taught in almost all the universities and business schools. The field has three levels of study:
1. Micro level (that studies how employees work)
2. Meso level (that studies how work groups function)
3. Macro level (that studies how organizations in general work) 

Methodology of Organizational Behavior Analysis in Organizational Behavior Assignment Writing

Organizational behavior analysis can be done in three ways, or there are three distinct methodological models you use while writing organizational behavior assignments: 

Quantitative Research

In quantitative analysis statistical methods are used. Some of them are the following: • ANOVA or analysis of variance is a collection of statistical models used to analyze the differences between the means of various employee groups.
• Correlation or the statistical relation between two set of variables.
• Meta-analysis or the statistical method that combines different statistical data to find patterns.
• Multi-level modeling or statistical models of parameters that vary at more than one level. It is beneficial for research designs where data for participants are organized at various levels.
• Multiple Regression exposes the relationship between dependant variable and explanatory variable.
• Non-parametric statistics or those statistical methods which are not dependant on parameterized families of the probability distribution.
• Structural equation modeling which is an alternative to regression equation approaches.
• Time Series Analysis which is used to predict future values based on previously observed values.
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