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Communications Tool

Statistics is a tool for simple and effective communication of information, especially new information. Statistics can be compared to using a microscope, telescope, overhead projector, videotape, film, textbook, or a computer. A researcher makes a decision about the best way to present quantitative and qualitative information. One statistical formula may be as effective as twenty pages of text in reporting quantitative information.

Definition: Statistics is an advanced form of communication -- a specialized form of communication using numbers, letters, and symbols as a medium. Although the word statistics may refer to numerical data, it will be used more generally in the course of study to refer to the body of principles and procedures developed for the collection, classification, and summarization of data. [A descriptive statistic is an index number that summarizes or describes a set of data….Inferential statistics is a method that takes chance factors into account when samples are used to reach conclusions about populations. (Spatz, 1997) ]

Statistics is a substantial component of research language in all the behavioral sciences. Statistics is involved in the research effort from the beginning to end. At the outset statistical terms play a large part in the definition of problems for research. Specific plans for an investigation are formulated to permit statistical evaluations. Data from the investigation are analyzed by statistical methods. Finally, reports of findings are often expressed in statistical terms. Because of accuracy, versatility, and effectiveness in communication, statistics figures prominently in the language, in which researchers theorize, plan, evaluate, speak, and write.

Definition: Data is a collection of observed values representing one or more characteristics of objects or units. Most social data are in the form of categorical frequencies, cases in defined classes or categories.

Language and Academic Studies

Early in the development of the university, most academic material was written in Latin and later in German and French. Graduate education, especially at the doctoral level, required a working knowledge of Latin, German, or French. Since major academic works were in languages other than English, language study was required. Because of advances in the translation of primary data and because the intellectual center of the world shifted to the United States around 1940, the study of other languages in graduate schools was minimized.

In education and industry, participants encounter statistical information in reading previous research. Further, participants present and interpret their research using statistics in order to understand the findings and to communicate the findings clearly. The use of statistics to report research is not as much a mathematical use as a language use. The course of study is not intended to make participants expert statisticians, rather the study is intended to make participants communicators of statistics, both as receivers and senders.

The language of statistics is a part of the culture of academia. In the academic culture, statistics is the primary means of communicating research data. As in any language, the language of statistics has grown and evolved. Early English has evolved dynamically into present-day English. In the same way, early statistical language changed dynamically. While the symbols of the language remained the same, the language has progressed from manual mathematics to computer assisted analysis.

The dynamic of change is similar to the changes in the culture of the automobile. At one time mechanical knowledge of the car was necessary in order to own and drive a car. The dynamic change in ownership of automobiles has changed all of that. The typical driver of today does not need to know details of the mechanics of automobiles in order to drive. All a driver needs to know is where to put in the gas and how to manipulate the controls.

A pilot in ground school learning to fly is taught to compute fuel usage, navigation, weight and balance data, and airspeed by means of a circular slide rule. When the student pilot steps into an airplane, most data management is preformed by a computer.

Ordinarily statistics had the same dichotomy between what was taught in the classroom and what was expected in actual use of statistics in a research project. Knowing how to compute a standard deviation or Chi square did not help when a researcher was confronted with a totally different problem such as converting questionnaire responses or quality control information into data for computer analysis. A researcher may have a brilliantly designed project that was approved by a committee, and be without sufficient logistical knowledge to bridge from questionnaire or production information to statistical analysis and presentation. The bridge from design to analysis is missing in research literature. The resulting gap has frustrated would-be students of statistics for generations. That gap is perceived as a lack of understanding of the mathematics of statistics, but it is really a gap in the language of statistics. Math is no longer a major issue. Just as an experienced pilot throws away the circular slide rule, the statistician lays aside the calculator, pen, and pencil and uses the computer.

The computer does the math; the researcher needs primarily to know the language of statistics. The language of statistics is not difficult. Statistical language can be broken down into three basic components: type of data, number of groups studied, number of measurements. When the data, groups, and measurements are known, the resulting statistical analysis is largely pre-determined, and the computer can easily work the statistical math. The key is understanding the language of data, groups, and measurements.

Behavior as a Statistic

Behavior can be expressed in statistical values, such as sums, means, proportions, differences, or ratios. The student of behavioral science must think about the behavior of people and animals in terms of statistical values. Understanding of behavior as a statistic must develop, not around isolated acts of an individual, but rather around the mean as an evaluation of a number of acts, whether from an individual or a group, or around the proportion of acts that are observed to be in a certain category or around the differences among individuals and the differences between groups.

Functions of Statistics

Statistical methods in behavioral and industrial sciences serve four related functions. The functions of summarizing, describing, generalizing and experimenting describe the acquisition and communication of new knowledge through research.