Statistical Analysis as a Function of Time

Paper Summary:

This paper discusses statistical analysis as a dynamic form of study that evolves over time to meet developing needs and to exploit developing capabilities and technologies. The author points out that statistical analysis is the process through which data becomes knowledge and is a science to assist one in making decisions under conditions of uncertainty. The paper relates that the most appropriate logic bases for the discipline of statistical analysis in the contemporary period are rational, quantitative, positivist and causality.

Table of Contents
Introduction: Reflections on Statistics
Reviewing Statistical Analysis
Defining Statistical Analysis
Alternative Logic Bases for Statistical Analysis
Rational Model versus Naturalistic Model.
Quantitative Model versus Qualitative Model.
Positivist Model versus Normative Model.
Causality Model versus Plausibility Model
Exploratory Model versus Confirmatory Model.
Randomization Model.
Conclusion: Reviewing Statistical Analysis.
Examining the Classical Model of Statistical Analysis
Descriptive Statistical Analysis
Exploratory Statistical Analysis
Inferential Statistical Analysis
Probability Theory and Classical Statistical Analysis
Conclusion: Classical Statistical Analysis

From the Paper:

"Descriptive statistical analysis describes the performance or activity of one group or class, without attempting to generalize about other groups or classes. Classification, description, and measurement are activities applicable to variables associated with social research. The classification of variables is based on an assumption that social units are comparable within the context of specific definitional criteria. A social researcher attempts to control variation through the classification of variables. The description of variables is an effort to assign some degree of uniqueness to each variable, in order to provide a basis for the establishment of relationships among variables. The measurement of the extent of the uniqueness of variables generates the quantitative indicators of the strength of the relationships between variables. The process of classification, description, and measurement facilitates the development of causal explanations for both regularities and variations in empirical phenomena. Comparisons are made according to the degree of differentiation of structure in data in relation to a common and less differentiated point of origin. Such comparability is dependent upon both the classification of the social unit and the dimension of that social unit that is being measured. The dimension is the variable being measured."

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