Analysing Quantitative Data

Variable-based and Case-based Approaches to Non-experimental Datasets

Raymond A Kent

• Publisher: SAGE Publications Ltd
• Year: 2015
• Print ISBN: 9781446273418, 9781446273401
• eBook ISBN: 9781473917910
• Edition: 1
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

This innovative book provides a fresh take on quantitative data analysis within the social sciences. It presents variable-based and case-based approaches side-by-side encouraging you to learn a range of approaches and to understand which is the most appropriate for your research.

Using two multidisciplinary non-experimental datasets throughout, the book demonstrates that data analysis is really an active dialogue between ideas and evidence.  Each dataset is returned to throughout the chapters enabling you to see the role of the researcher in action; it also showcases the difference between each approach and the significance of researchers' decisions that must be made as you move through your analysis.

The book is divided into four clear sections:

• Data and their presentation
• Variable-based analyses
• Case-based analyses
• Comparing and combining approaches

Clear, original and written for students this book should be compulsory reading for anyone looking to conduct non-experimental quantitative data analysis.

1 Data structure

Learning objectives

In this chapter you will learn that:

• data are constructed rather than collected and result from a process of systematic record-keeping;
• records are created in a social, economic and political context and for purposes specific to individuals or groups within organizations;
• qualitative data consist of words, phrases, narrative, text and visual images, while quantitative data arise as numbers that result from the systematic capture of classified, ordered, ranked, counted or calibrated characteristics of a specified set of cases;
• all quantitative data have a structure that consists of cases, properties and values;
• the construction of data of any kind is likely to give rise to errors from various sources;
• the dataset used throughout this text consists of 61 properties for 920 cases, but they do not constitute a random sample and there are many potential sources of error in the data.

Introduction

All research involves analysing data at some point – but what do we mean by ‘data’? What kinds of data are there? How are they constructed? How are quantitative data structured? This chapter provides an overview of the nature and characteristics of data in general and shows how quantitative data in particular are constructed and structured.

The procedures used by researchers to structure and analyse a dataset are illustrated throughout this text with a study carried out by the Institute of Social Marketing at the University of Stirling, which studies the impact of alcohol marketing on the drinking behaviour of young people aged between 12 and 14. The findings are based on a survey that involves an interview-administered questionnaire measuring awareness and involvement with alcohol marketing and a self-completion questionnaire measuring alcohol drinking and associated behaviours. The homes of all second-year pupils attending schools in three local authority areas in the west of Scotland were contacted, generating a sample of 920 respondents (Gordon et al., 2010a).

The key research hypotheses are that the more aware of and involved in alcohol marketing that young people are, the more likely they are to have consumed alcohol, and the more likely they are to think that they will drink alcohol in the next year. To measure awareness, respondents are asked if they have seen any adverts for alcohol in any of 15 channels, for example television, cinema, newspapers, websites or sponsorships. Responses are recorded into ‘Yes’, ‘No’ and ‘Don’t know’. To measure involvement in alcohol marketing, pupils are asked whether they have, for example, received free samples of alcohol products, free gifts showing alcohol brand logos or promotional mail or email.

Drinking behaviour is measured in four main ways. Drinking status is assessed by asking whether pupils have ever had a proper alcoholic drink, not just a sip. Future drinking intention is assessed by asking about the likelihood that they would drink alcohol during the next year – ‘Definitely not’, ‘Probably not’, ‘Probably yes’ and ‘Definitely yes’. They are also given a ‘Not sure’ option. Initiation is measured by asking how old they were when they took their first drink, plus a measure of the number of alcoholic units last consumed. The study uses a range of control variables suggested in the literature, for example parental attitudes towards drinking and alcohol consumption, perceived parental drinking approval, sibling and peer drinking behaviour, liking of school and rating of school work. Demographic controls include gender, social grade (based on the occupation of the head of household), ethnicity and religion. The data from the alcohol marketing research are available online from the Sage website, https://study.sagepub.com/kent.

Data and their construction

Data are often thought of as ‘the facts’ – things that are known to be true. The dictionary tells us that the word is a plural noun (although commonly treated as singular) and derives from the Latin word that translates literally as ‘things given’. Data are thus portrayed as a form of knowledge – sheer, plain, unvarnished, untainted by social values or ideology and, for the most part, unchallengeable. The assumption is that they exist independently of our research activities and that we can simply go out and discover or ‘collect’ them like so many tadpoles in a pond.

In reality, however, data are not collected or discovered, but constructed. They are generated as a result of the human activity of systematic record-keeping, for example in registers of births, marriages and deaths, hospital records, invoices, questionnaires, electronic meters, audio or video recordings. Record-keepers, furthermore, construct data for their own purposes. They have their own agendas and personal circumstances; they have careers to pursue, their own fears and hopes; they have bosses to impress or subordinates to guide or deploy. Data construction is a process, furthermore, that takes place in a social, moral, economic, political and historical context. There are, for example, colleagues or academic peers to consider, respondents or subjects to bear in mind, consumers, clients, funding or sponsoring agencies to take into account.

All this is not to say that data are just concocted – meaningless artefacts, subject to manipulation, doctoring or media spin. They are, however, constructed in a particular context for specific purposes. It has been argued that everyday reality (Berger and Luckmann, 1966), scientific facts (Latour and Woolgar, 1979) and many other things like gender, homosexual culture or ideas about illness are socially constructed. By being specific about what is being socially constructed, there is the implicit admission that not everything (like material objects) is a social construction and that there may be degrees of construction involved (Hacking, 1999). Social reality does, however, both constrain and facilitate data construction; so do the agreed (or disputed) practices and routines of scientific procedure.

Few data, furthermore, are perfect. Errors, to varying degrees, will almost certainly be made in the data construction process. Different researchers will often produce different results, apparently from researching the ‘same’ phenomena. Even government statistics are often based on questionnaire surveys, and there are many things that can go wrong with this process. Issues of error in data construction are taken up later in this chapter. Apart from the absence or presence of error, the quality of data will also vary in their comprehensiveness, the speed or timeliness with which they are delivered, and in the manner of their construction.

Data, in short, are not ‘the facts’ or ‘things given’; they are social products. The records created are not reality itself; rather they are a result of researchers’ attempts to observe or measure traces or evidence of phenomena situated within complex systems (Byrne, 2002). The records that researchers create come in very different forms. The historian likes to think of church registers, diaries of famous people, or transcripts of what was said by politicians as ‘data’. A sociologist with an audio recorder studying women’s emotional reactions to domestic violence, or participating in ‘street corner society’ and making notes of his or her experiences, likes to think that he or she is collecting ‘data’. An anthropologist looking at some unusual, remote tribe of people considers that he or she is generating ‘data’ by making records describing their culture. The archaeologist uses physical traces or remains as evidence or data on past events, conditions or social behaviour. The manager of a business organization may think more in terms of sales data or information on balance sheets and profit and loss statements. The market researcher is more likely to see the results of a questionnaire survey or the record of a focus group discussion as ‘data’.

Data may, in fact, consist of three rather different kinds of constructed record, for example:

• words, phrases or narrative captured in audio tape or digital recordings, interview transcripts or field notes; alternatively, text already recorded in minutes of meetings, reports, historical or literary documents, personnel records or newspaper clippings;
• images, for example paintings, sketches, drawings, photographic stills, DVD recordings, computer-generated images, posters, advertisements;
• numbers that result from the systematic capture of classified, ordered, ranked, counted or calibrated characteristics of a sample or population of cases, for example the number of males and females in an organization or the sizes of supermarkets in square metres of floor space.

Words, phrases, narrative, text and visual images (which are often combined, for example in posters) are usually regarded as ‘qualitative’ data. Data that arise as numbers are ‘quantitative’. What is commonly described as ‘qualitative research’ will usually result in the construction of largely qualitative data, while quantitative research will focus mainly on generating quantitative data, but both types of research will usually be a mixture of both sorts of data. The focus in this text is on the analysis of quantitative data, but Part Three does consider mixed methods and how some forms of qualitative data can be quantified.

Data construction may take place either during the routine capture of information, for example on patients admitted to the accident and emergency department in a hospital, or they may be a result of research activity. Data construction in the latter context will include two key elements: the design of the research, which provides the context within which it is intended to construct data, and the actual capture of the data themselves.

The purpose of any research design is to ensure that the data constructed enable the researcher to address the objectives for which the research was undertaken, for example to answer research questions or to test research hypotheses. Writers of texts on research methods are apt to propose listings of different types of design: for example, there are qualitative designs, quantitative designs and mixed designs (e.g. Creswell, 2009); there are exploratory, descriptive and causal designs (e.g. McGivern, 2009). De Vaus (2002) suggests that all designs in the social sciences fall into one of four main groups: experimental, longitudinal, cross-sectional or case study.

Classifications of different types of research design such as those above imply alternative combinations of elements that are for the most part mutually exclusive. An actual piece of research, however, will usually utilize more than one type of design element. So, any design is usually specific to a particular enquiry and will be a unique combination of elements that involve mixing different types of research in the same project. A design may usefully be seen as a series of ‘sub-designs’, for example a design for the specification and selection of the entities that are to be the focus of the research, a design for the role, construction and measurement of selected characteristics of those entities, a design for the capture of data and proposals for their analysis.

A key element in any research design is the clarification of research objectives. These spell out what the research is designed to show or achieve. The more specific these are, the easier it is to design a piece of research that will construct relevant data and the easier it is to see what kinds of data analysis might be appropriate. Ideally, stated research objectives should consist of two key elements: a statement of the general research area, purpose or aim and more specific research questions or research hypotheses. The general research purpose may broadly be exploratory or verificational, for example it may be ‘to explore, investigate or study the effect of playing background music on consumer behaviour in the retail environment’ or ‘to demonstrate or show that the playing of background music has a significant impact on consumer behaviour in the retail environment’. More specifically, a research question might be ‘What is the effect of playing loud music on the amount spent?’ or, phrased as a hypothesis, ‘The faster the music, the less time customers spend in the retail environment’.

The actual capture of the data will require the use of one or more data capture instruments. For qualitative data, the creation of a record could be by way of manual or electronic notebooks, audio or video recorders, camcorders, still cameras or seeking commentary via open-ended questions in questionnaires, email, web pages, blogs, Facebook, and so on. For quantitative data, the most common way to capture data will be through the use of fixed choice responses in a questionnaire, but these may be of very different types. For example, they may be completed by respondents themselves or by interviewers on behalf of the respondent either in a face-to-face situation or over the telephone. Self-completed questionnaires may be delivered personally, by post or using the Internet. An alternative instrument that is commonly used but seldom explained is the diary. These get respondents to record instances of behaviour as and when they occur and may, for example, relate to records of personal contacts or media use – radio listening, for example, is commonly recorded in this way. Increasingly, however, quantitative data are captured electronically using bar scanners, set meters (for television viewing, for example), passive sensing devices, portable data entry terminals or the Internet.

The data from the alcohol marketing study are largely quantitative and are constructed using an academic cross-sectional survey research design. It was cross-sectional in the sense that the study was treated as a ‘one-off’ with measures taken as a single time period. The alternative would be a longitudinal design where measures are taken at intervals with the express purpose of measuring changes. Although the data used in this book are cross-sectional, in reality they are part of a wider study at the Institute of Social Marketing at the University of Stirling which is a two-wave cohort design, the first study carried out from October 2006 to March 2007 with a follow-up of the same respondents two years later. This nicely makes the point that real designs are combinations of elements. Data capture, too, was a combination of interviewer-completed and self-completed (but personally delivered) questionnaires.

Key points and wider issues

Data do not exist ‘out there’, waiting to be ‘collected’ or ‘discovered’. Rather, they are constructed by individuals within a social, economic, political and moral matrix of possibilities and constraints. They are generated as a result of the human activity of systematic record-keeping using a range of data capture instruments. Data are of very different types. They may be qualitative or quantitative, or some mixture. There are, furthermore, different types of each. Qualitative data may be words, phrases, text, images or a mixture. Quantitative data, as is explained in the next section, may be categorical or calibrated according to some specified metric like years, euros or kilograms. Data come in different qualities (good, adequate and poor), they are all in various ways subject to error and may be judged in different ways, for example in terms of comprehensiveness, accuracy, timeliness, relevance, and so on, so what counts as ‘good’ data may not, in any case, be clear-cut.

Data construction may be a routine process or it may be a result of research activity, in which case the construction entails the design of the research and the purposeful capture of the data. A research design is specific to a ...

Table of contents

• Cover
• Half Title
• Publisher Note
• Title Page
• Copyright Page
• Contents
• Illustration List
• Table List
• Sidebar List
• About the Author
• Companion Website
• Preface
• I Quantitative data: structure, preparation and analysis approaches
• 1 Data structure
• 2 Data Preparation
• 3 Approaches to data analysis
• II Variable-Based Analyses
• 4 Univariate Analysis
• 5 Bivariate analysis
• 6 Multivariate Analysis
• III Case-Based Analyses
• 7 Set-Theoretic Methods and Configurational Data Analysis
• 8 Cluster and Discriminant Analysis
• IV Comparing and Communicating Results
• 9 Comparing and Mixing Methods
• 10 Evaluating Hypotheses, Explaining and Communicating Results
• Answers to Exercises and Questions for Discussion
• Glossary
• References
• Index