Psychology has been defined differently by various authors. Dewey defines psychology as the science of facts or self phenomena (1). On the other hand, statistics is defined as the process of collecting, analyzing, interpreting and presenting data (Clark 40). The application of statistical methods in psychology enables psychologist to make informed decisions after analyzing and interpreting data. Statistical methods can be categorized as descriptive or inferential.
Descriptive methods encompass techniques that describe a set of data in an effort to condense or better understand what is there. For example, the average age group of 100 individuals might give a much better image of the group as compared to individual listing of each of the 100 ages. Moreover, inferential methods are a body of techniques which allows us to draw a conclusion about a larger group of objects by examining only a portion of those objects.
The importance of statistics in psychology can be illustrated through the following example. Consider a therapist who deals with sexual abuse. The therapist received approximately 200 patients in a month. His records show that 80 patients had anger management problems, 20 patients had promiscuous tendencies, 30 patients had signs of ADHDA, 10 presented withdrawal symptoms and 60 patients had cognitive challenges. Statistical skills will enable the therapist to analyze this data and draw logical conclusions.
Through concrete analysis of the psychological behaviors, the therapist can conduct research to shade more light on particular patterns of interest. The therapist will require statistical skills in order to conduct research efficiently.
Statistical data serve as a general aid to the managerial decision making process. One cannot make wise decisions without a proper understanding of the facts used in making those decisions.
The most successful decision makers are people that can effectively make use of the information which is available to them. Data are facts on which we perform statistical methods or techniques; i.e., facts which we collect, analyze, interpret and present (Clark 50). Data can either be qualitative or quantitative. Quantitative data are information about the world in the form of numerical data.
On the other hand, qualitative data are information about the world in the form of words. Quantitative data are necessarily structured in terms of the number system and reflect researcher imposed constructs. Qualitative data may range from structured to unstructured, and may or may not involve researcher imposed constructs. The basic difference between the two types of data lies in the process of measurement.
Thus, a study can have all quantitative data, all qualitative data or both. Which of these three apply is not a major concern. The type of data generated is what matters. The type of data intended depends on what is being investigated and practical aspects of the study. In regard to what needs to be studied, it means that substantive issues dictate methodological choices.
Collected data can be analyzed in different ways. With regard to quantitative data, the following methods can be applied (Clark 60). Frequency distributions: when the population size is small the researcher can record the sample observations, arrange them in an increasing order and see the general value and amount of variation in the individual values.
Moreover, for larger samples, frequency distribution is a more compact presentation of the data. Frequency distributions can be used to estimate sample median value and obtain continuous frequency distributions and polygons.
Mean: provides the average of the values of X in a population. Standard Deviation: a measure in the amount of variation among the values X of a population. Variance: measures the degree of difference of a set of data from the mean: Coefficient of variation: statistical way that quantifies scatter. Statisticians usually compute the coefficient of variation by dividing the standard deviation of a given set of data by their mean. Mode: The most frequent value in a population.
Statistics can be applied to a societal problem. This usually starts with the population or a process to be investigated. It is not possible to study an entire population because the study might be costly and time consuming. However, a smaller proportion of the population (sample) is usually collected from the population and used in the analysis of the entire population.
Basically, the sample is the representation of the population under study. Therefore, data collected from the sample can be used to draw inferences about the larger population. The main application of statistical methods in psychology involves the use of statistics to analyze and interpret data.
The collected data are usually put under scrutiny using various analytical methods and eventually serves two main purposes; to describe and infer. Descriptive statistics offer an explanation of what was observed in the sample. Descriptive statistics employ numerical or graphical explanations of the observed data offering a channel for comparison between the sample and population characteristics. Numerical representations of a continuous data may include mean and standard deviation.
However, for discontinuous data, frequency and percentages become essential in offering comparisons between the sample and the population. Inferential statistics provide a means through which statisticians can draw inferences about the population from the sample that was studied. Inferential statistics utilize patterns reflected in the sample to offer a channel for population-sample inferential critique thus, accounting for randomness.
Clark, L. The Essentials of Business Statistics. New Jersey: Research and Education Association, 1991. Print.
Dewey, John. Psychology. Montana: Kessinger Publishing, 2005. Print.