Interpreting Quantitative Data with IBM SPSS Statistics

Rachad Antonius

• Publisher: SAGE Publications Ltd
• Year: 2012
• Print ISBN: 9781446207437, 9781446207420
• eBook ISBN: 9781446290873
• Edition: 2
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

The second edition of Interpreting Quantitative Data with IBM SPSS Statistics is an invaluable resource for students analysing quantitative data for the first time. The book clearly sets out a range of statistical techniques and their common applications, explaining their logic and links to the research process. It also shows how SPSS can be used as a tool to aid analysis.

Key features of the second edition include:

- new chapters on one-way and two-way ANOVA, the Chi-square test and linear regression.

- SPSS lab sessions following each chapter which demonstrate how SPSS can be used in practice

- sets of exercises and ?real-life? examples to aid teaching and learning

- lists of key terms to aid revision and further reading to enhance students? understanding

- an improved text design making the book easier to navigate

- a companion website with answers to the labs and exercises, along with additional data sets and powerpoint slides

Part One Introduction to Quantitative Methods

One The Basic Language of Statistics

This chapter is an introduction to statistics and to quantitative methods. It explains the basic language used in statistics, the notion of a data file, the distinction between descriptive and inferential statistics, and the basic concepts of statistics and quantitative methods.

After studying this chapter, you should know:

• the basic vocabulary of statistics and of quantitative methods;
• what an electronic data file looks like, and how to identify cases and variables;
• the different uses of the term ‘statistics’;
• the basic definition of descriptive and inferential statistics;
• the type of variables and of measurement scales;
• how concepts are operationalized with the help of indicators.

Introduction: Social Sciences and Quantitative Methods

Social sciences aim to study social phenomena – that is, human collective behavior, the culture that sustains it, the relationships between people that make it possible, and the organizations and institutions that regulate it – as rigorously as possible. This involves describing some aspect of social reality, analyzing it to see whether causal or explanatory links can be established between its various parts, and, whenever possible, predicting future outcomes, or at least a range of possible outcomes.

The general objective of such studies is to understand the patterns of individual or collective behavior, the constraints that affect it, the causes and explanations that can help us understand our societies and ourselves better and predict the consequences of certain situations. Such studies are never entirely objective, as they are inevitably based on certain assumptions and beliefs that cannot be demonstrated. Our perceptions of social phenomena are themselves subjective to a large extent, as they depend on the meanings we attribute to what we observe. Thus, we interpret social and human phenomena much more than we describe them, but we try to make that interpretation as objective as possible.

Some of the phenomena we observe can be quantified, which means that we can translate some aspects of our observations into numbers and make use of their properties. For instance, we can quantify population change: we can count how many babies are born every year in a given country, how many people die, and how many people migrate in or out of the country. Such figures allow us to estimate the present size of the population, and maybe even to predict how this size is going to change in a near future. We can quantify psychological phenomena such as the degree of stress or the rapidity of response to a stimulus; demographic phenomena such as population sizes or sex ratios (the ratio of men to women); geographic phenomena such as the average amount of rain over a year or over a month; economic phenomena such as the rate of employment; we can also quantify social phenomena such as the changing patterns of marriage or of unions, and so on.

When a social or human phenomenon is quantified in an appropriate way, we can ground our analysis of it on figures, or statistics. This allows us to describe the phenomenon with some accuracy, to establish whether there are links between some of the variables, and even to predict the evolution of the phenomenon. If the observations have been conducted on a sample (i.e. a group of people smaller than the whole population), we may even be able to generalize to the whole population what we have found on that sample.

When we observe a social or human phenomenon in a systematic, scientific way, the information we gather about it is referred to as data. In other words, data is information that is collected in a systematic way, and organized and recorded in such a way that it can be interpreted correctly. Data is not collected haphazardly, but in response to some questions that the researchers would like to answer. Sometimes, we collect information (i.e. data) about a character or a quality that has no numerical value, such as the mother tongue of a person. Sometimes, the data is measurable with numbers, such as a person's age. In both cases, we can treat this data numerically: for instance, we can count how many people speak a certain language, or we can find the average age of a group of people. The procedures and techniques used to analyze data numerically are called quantitative methods. In other words, quantitative methods are procedures and techniques used to analyze data numerically; they include a study of the valid methods used for collecting data in the first place, as well as a discussion of the limits of validity of any given procedure (i.e. an understanding of the situations when a given procedure yields valid results), and of the ways the results are to be interpreted.

This book constitutes an introduction to quantitative methods for the social sciences. The present chapter covers the basic vocabulary of quantitative methods. This vocabulary should be mastered by the student if the remainder of the book is to be understood properly.

Data Files

One of the first objects we deal with, in quantitative methods, is a data file. This is an electronic file that contains all the data, organized in a systematic way, often using numeric codes to refer to the various observations. When conducting research, we must distinguish between primary data, that is, data that is produced by the researcher or by a research agency, and secondary data, that is, data which is cited in an academic publication but which has been produced by some other researcher, or some agency, or which has been manipulated and summarized. The term raw data designates data that has not been subjected to any kind of statistical treatment, such as grouping, recoding, or selecting.

Figure 1.1 illustrates what an electronic data file looks like when we open it with the SPSS program. We can see in this figure the data window and the menu bar that appears on the top of your screen when you open SPSS.

Figure 1.1 The Data View window in IBM SPSS 19 and the Menu bar (PC version)

This data file was created by version 19 of the statistical software SPSS (Statistical Package for the Social Sciences), now called IBM SPSS. This software is available for both Windows and Mac. Exercise 1 at the end of this chapter will introduce you to SPSS, but we can look at some of its features right away.

On the top of the window, you can read the name of the data file survey_sample.sav.

When we open an SPSS data file, two views can be displayed: the Data View, or the Variable View. Both views are part of the same file, and one can switch from one view to the other by clicking on the tab at the bottom center of the window.

The Data View displays the data itself, and the information is organized in rows and columns. Each row refers to a case, that is, all the information pertaining to one individual. Each column refers to a variable, i.e. a character or quality that was observed and recorded using codes to refer to the various values of the variable. For instance, the second column is a variable called wrkstat, and the third is a variable called marital. Looking at the contents of each of these two columns, we see that the first variable concerns the work status, and the second the marital status of each individual.

It is important to understand that the data is not stored the way it appears in Figure 1.1. Rather it is stored in codes that take less memory space in a computer, making computations much faster. Thus, instead of recording ‘Married’, the program will just store a simple code, such as ‘1’. There is a way of showing the codes instead of the value labels. This is done by ticking off the command Value Labels under the View menu, in the data file. The Data View window now looks as shown in Figure 1.2.

Figure 1.2 The Data View window when the Value Labels command is ticked off the View menu

Here, the codes are displayed rather than the value labels. The meanings of these codes can be seen by clicking on Variables… under the Utilities menu. The resulting window is shown in Figure 1.3.

Figure 1.3 The Variable and Value labels for the variable marital

You can see here that the variable is designated as ‘marital’, that its full label is Marital status, and the various codes are also given:

This information is part of the codebook, which includes a complete listing of all the variables, their labels, their values (the codes) and their value labels. In SPSS, the codebook is referred to as the data file information. You can see it by selecting File → Display Data File Information. You will get a new wind...

Table of contents

• Cover
• Half Title
• Publisher Note
• Title Page
• Copyright Page
• Contents
• About the Author
• Foreword to the Instructor
• Foreword to the Student
• Acknowledgements
• Part One Introduction to Quantitative Methods
• One The Basic Language of Statistics
• Two The Research Process
• Part Two Descriptive Statistics
• Three Univariate Descriptive Statistics
• Four Graphical Representations
• Part Three Methodological Tools
• Five Creating New Variables with SPSS
• Six Normal Distributions and Sampling Distributions
• Seven Sampling Designs
• Part Four Inferential Statistics
• Eight Estimation
• Nine Hypothesis Testing
• Part Five Statistical Association
• Ten Correlation and the Regression Line
• Eleven Two-Way Tables and the Chi-Squared Test
• Twelve T-Tests and Anova
• Appendices
• References
• Glossary
• References
• Index