Analyzing Quantitative Data

An Introduction for Social Researchers

Debra Wetcher-Hendricks

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9780470526835
• eBook ISBN: 9781118626115
• Edition: 1
• Topic: Psychology
• Subtopic: Research & Methodology in Psychology
• Index: Psychology

A user-friendly, hands-on guide to recognizing and conducting proper research techniques in data collection

Offering a unique approach to numerical research methods, Analyzing Quantitative Data: An Introduction for Social Researchers presents readers with the necessary statistical applications for carrying out the key phases of conducting and evaluating a research project. The book guides readers through the steps of data analysis, from organizing raw data to utilizing descriptive statistics and tests of significance, drawing valid conclusions, and writing research reports. The author successfully provides a presentation that is accessible and hands-on rather than heavily theoretical, outlining the key quantitative processes and the use of software to successfully draw valid conclusions from gathered data.

In its discussion of methods for organizing data, the book includes suggestions for coding and entry into spreadsheets or databases while also introducing commonly used descriptive statistics and clarifying their roles in data analysis. Next, inferential statistics is explored in-depth with explanations of and instructions for performing chi-square tests, t-tests, analyses of variance, correlation and regression analyses, and a number of advanced statistical procedures. Each chapter contains explanations of when to use the tests described, relevant formulas, and sample computations. The book concludes with guidance on extracting meaningful conclusions from statistical tests and writing research reports that describe procedures and analyses.

Throughout the book, Statistical Resources for SPSS® sections provide fundamental instruction for using SPSS® to obtain the results presented. Where necessary, the author provides basic theoretical explanations for distributions and background information regarding formulas. Each chapter concludes with practice problems, and a related website features derivations of the book's formulas along with additional resources for performing the discussed processes.

Analyzing Quantitative Data is an excellent book for social sciences courses on data analysis and research methods at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians and practitioners working in the fields of education, medicine, business and public service who analyze, interpret, and evaluate data in their daily work.

PART I
SUMMARIZING DATA

1

DATA ORGANIZATION

1.1 INTRODUCTION

High school math teachers must cringe when they hear the age-old question “When am I ever going to need to know this?” Social scientists learn the answer to this question during their first attempts at social research. Early stages of research, including developing a research hypothesis, performing a literature review, creating data-gathering instruments, and actually gathering data certainly challenge novice researchers like you. However, the greatest anxiety seems to surround the anticipation of data analysis.

Those who have become familiar with data analysis, though, would tell you to relax. The challenges posed by data analysis pale in comparison to those already encountered by one who has designed and implemented a means of gathering data. Statistical analysis follows a relatively structured plan that, once recognized, provides a basis for evaluating data in any form. In fact, at the point of statistical analysis, the topic of one’s study becomes somewhat irrelevant. The same protocols and techniques apply to all data, regardless of the issues to which the data pertain or the method used to collect them.

1.2 CONSIDERATION OF VARIABLES

You can refer to anything that changes as a variable. In the research context, a variable is an entity about which you gather data. These entities can change over time, for different people, in different situations, and for many other reasons. In your analysis, you attempt to determine whether these changes follow any particular pattern.

1.2.1 Units of Analysis

Before beginning the analysis process, you must acknowledge the origin points of your data, called the units of analysis. Each data point describes a particular unit of analysis. For social research, the units of analysis are most often human beings. Data indicating the responses to survey questions, behaviors observed during field studies, and performances on pretests and posttests of experiments all pertain to individuals. Social researchers refer to these individuals as subjects and to the compilation of their subjects as a sample. Proper ways to select your sample are discussed in Chapter 4.

Example 1.1: Human Units of Analysis

A researcher who wishes to determine whether a relationship exists between the placement of one’s tattoo on one’s body and the cost of the tattoo, for example, would gather information about individuals who have tattoos. By speaking with these individuals or by observing them while they receive and pay for the tattoos, the researcher would obtain the information that he or she needs. Each data point originates with one individual person and, after data collection the researcher can associate each person with a tattoo placement and cost. Thus, people serve as the unit of analysis.

Like many other aspects of the social sciences, however, the identification of analysis units does not always remain so straightforward. Rather than evaluating individuals, some social research compares and contrasts social institutions or settings. Data points in these situations do not correspond to people. The origin of the data and, thus, the units of analysis, reflect the nonhuman entity that the data describe.

Example 1.2: Nonhuman Units of Analysis

Slightly changing the focus of the study described in Example 1.1 to one that compares the prices of tattoo parlors in urban and in rural areas provides an example of nonhuman units of analysis. A researcher conducting this study would obtain prices from various randomly selected tattoo parlors and would characterize each as located in an urban or a rural area. In this case, the data pertain to locations of and prices at tattoo parlors, making these establishments the units of analysis.

1.2.2 Variables

Data analysis begins with the recognition of variables. In a general sense, the term variable describes anything that changes. This definition provides a foundation for understanding the concept of variables for social research. In this context variables are issues that the researcher measures. Each piece of data (datum) collected by a researcher provides information about a particular unit of analysis. The term variable applies because the information gathered generally addresses behaviors, attitudes, and characteristics that change from subject to subject.

Example 1.3: Variables

For example, a researcher pursuing the study proposed in Example 1.1 would, at the very least, need to note the part of the body on which each individual receives a tattoo as well as the cost for receiving the tattoo. The information recorded about placement of tattoos on the body and cost of tattoos changes with each individual who provides information. These two aspects, then, are variables.

Some studies use more than two variables. The complexity of your study and your intentions determine the number of variables that you need to consider. Some scenarios involving more than two variables receive attention in Section 1.4 and in Chapters 8 and 10 of this book. However, developing an understanding of these situations rests on your recognition and description of the two main variables.

Roles of Variables.

Even if you didn’t realize it, you were likely aware of your study’s independent and dependent variable(s) even before collecting data. However, you must formally address the distinction between the independent and dependent variables at the data analysis stage. When first introduced to the concept of research, you may have learned to regard the independent variable as the causal factor and the dependent variable as the effect of that causal factor. Although these associations may hold true for research in the natural sciences, social scientists should avoid causal terms when describing the roles of the independent and dependent variables. The section of this chapter entitled “Variable Relationships” further explains the importance of doing so.

You should think of the independent variables as a predictor of behaviors, attitudes, and characteristics. The independent variable describes a given condition, either already existing or created by the researcher before the start of data gathering. The dependent variable, then, refers to the behaviors, attitudes, and characteristics predicted by the independent variable.

With this understanding and previous identification of the two main variables for a study, you can simply insert variable names into the sentence, “Data about ______ predict data about ______.” Placing the variable names into the incorrect positions leads to an illogical statement. Once the sentence accurately portrays the researcher’s goal in the study, you know that the variable inserted into the first blank is the independent variable and the variable inserted into the second blank is the dependent variable.

Example 1.4: Independent and Dependent Variables

This technique works well with the tattoo example. To determine whether tattoos cost more in urban or rural areas, the researcher wishes to investigate whether “data about location predict data about cost” and thus, will designate location as the inde...

Table of contents

• COVER
• CONTENTS
• TITLE PAGE
• COPYRIGHT
• PREFACE
• PART I SUMMARIZING DATA
• PART II STATISTICAL TESTS
• PART III APPLYING DATA
• APPENDIXES
• REFERENCES
• ANSWERS TO REVIEW QUESTIONS
• INDEX