Types of Data

9 Key excerpts on "Types of Data"

• 

  eBook - ePub

  Got Data? Now What?

  Creating and Leading Cultures of Inquiry

  • Laura Lipton, Bruce Wellman(Authors)
  • 2012(Publication Date)
  • Solution Tree Press

    (Publisher)

  This chapter explains data fundamentals—data types, sources, terms, and tools—to support groups in effectively implementing the collaborative learning cycle and to broaden perspectives on the Types of Data that might further their work. Schools and school districts are rich in data. Determining how much and what Types of Data will best serve collaborative inquiry and the group’s ultimate outcomes is critical to effective application of the collaborative learning cycle. It is important that the data involved provide both a broad and a deep view of the present picture but are not so complex that the process becomes overwhelming and unmanageable. By examining multiple sources of data, groups glean insights from several angles on the issues under study.

  Two Types of Data: Qualitative and Quantitative

  Fundamentally, there are two major Types of Data: (1) qualitative and (2) quantitative. Qualitative data rely on description, while quantitative data rely on numbers.

  Qualitative data tend to be narrative, holistic, and longitudinal. Schools may gather qualitative data from the classroom, grade, department, or school level. Classroom-, grade level–, or department-based data include anecdotal records, student work samples, portfolios, student interviews, checklists, and homework assignments. School-level data include meeting agendas, teacher demographics (years of experience, education, and so on), memos, schedules, and curriculum maps.

  Quantitative data are expressed numerically and statistically. Quantitative sources include test scores of all types, performance grades, attendance records, and enrollment data. Quantitative results are intended for comparisons between students, groups of students, schools, districts, states or provinces, and nations. Thus, they are expressed and described using stanines, quartiles, norm-curve equivalents, means, medians, and modes.

  Each type of data is organized differently for analysis. Because quantitative data use numbers, percentiles, and other mathematical configurations, and they are organized based on frequency distributions, central tendencies, variabilities, and dispersions, it is easier to create tables, charts, and graphs to discuss and analyze quantitative data. For example, isolating, or disaggregating, norm-referenced test scores by variables such as gender, race, or socioeconomic status allows teams to identify relationships and patterns within the blur of numbers.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Practical Research Methods in Education

  An Early Researcher's Critical Guide

  • Mike Lambert(Author)
  • 2019(Publication Date)
  • Routledge

    (Publisher)

  It is still common for researchers working in the education field to regard ‘quantitative-qualitative’ as a methodological divide, in which they have to choose a side. This chapter rejects this view, taking as its starting point the notion that researchers should view them merely as different Types of Data and that they therefore need to understand how both are collected, analysed and interpreted. As using the quantitative option can seem particularly problematic, here are three reasons why readers who are nervous of numbers should consider familiarizing themselves with quantitative data processes:

  • Having some understanding of quantitative data will improve your ability to assess investigations which use quantitative approaches, particularly if you are new to research.

  • Being able to incorporate quantitative data into your own research could strengthen its impact, particularly if you are interested in influencing aspects of education practice or policy. Although they often use them carelessly and unreflectively, policymakers like numbers.

  • Having an improved understanding of quantitative data and research will put you in a better position to evaluate or conduct mixed-methods investigations.

  What is quantitative research? What are quantitative data?

  The application of quantitative methods in social-science research derives from interest in the 19th century in experimental investigation in the natural sciences. This sought to understand the processes of cause and effect through manipulation and controlled testing. However, it became clear that many of the issues explored in social sciences, such as education, do not lend themselves well to experimental approaches, so researchers began from the 1950s to apply them to what became known as ‘quasi-experimental’ and ‘non-experimental’ situations – these approaches are considered in more detail later in the chapter.

  Provocatively, Berliner (2002:19) has described what social scientists, and particularly educational researchers, do as the ‘hardest to do science […] because humans in schools are embedded in complex and changing networks of social interaction’. It is engaging with this social complexity that makes the application of experimental research to education so challenging (Hadfield and Jopling, 2018), and it is partly as a consequence of the need to address this complexity and the limitations of the persistent quantitative-qualitative divide, manifested in the so-called ‘paradigm wars’, that mixed-methods research design (Tashakkori and Teddlie, 1998) gained ground in the 1990s.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Conducting Research in Human Geography

  theory, methodology and practice

  • Rob Kitchin, Nick Tate(Authors)
  • 2013(Publication Date)
  • Routledge

    (Publisher)

  8 , this myth needs to be firmly quashed.

  3.2 Classifying data types and measurement scales

  The quantitative analysis methods discussed in Chapters 4 and 5 require the collection of measured data. Such data represent one type of information which we can collect about phenomena in our environment. Here, measurements relate both to the fundamental properties of objects, where quantities like length extend through space, and to measurement operations performed on individual objects. In the former, measurements are known as extensive properties which form the basis of standard measuring systems such as the SI system in use by most scientists today (Chrisman, 1997: 8-9). However, this view of measurement is only appropriate for physical properties, and is not really applicable to the subject matter of most social scientists. The alternative latter view sees the process of measurement as being separate from the object being measured. This is usually referred to as a representative view of measurement. It was this view that was adopted in the levels of measurement developed by Stevens (1946) and listed in Box 3.1 . He identified four levels of measurement: nominal,

  Box 3.1 Levels of measurement

  Nominal	Observations are placed in categories, symbolised by numerals or symbols (e.g., A, B, C, D).
  Ordinal	Observations can be placed in a rank order, where certain observations are greater than others. Assigned numerals cannot be taken literally (e.g., first, second, third, fourth).
  Interval	Each observation is in the form of a number in relation to a scale which possesses a fixed but arbitrary interval and an arbitrary origin. Addition or multiplication by a constant will not alter the interval nature of the observations (e.g., 1°C, 2°C, 3°C, 4°C).

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Methods of Criminological Research

  • Victor R Jupp(Author)
  • 2012(Publication Date)
  • Routledge

    (Publisher)

  We shall look at Types of Data not just to reinforce once again—but in a different way—the plurality and diversity within the criminological enterprise, but, more fundamentally, to consider some of the assumptions about the nature of crime and criminality which are implicit in different Types of Data. These include assumptions about whether crime can be legitimately measured; assumptions about the appropriate unit and level of analysis—individual, social group or society; and assumptions about the primacy which should be given to antecedents and causality in any such analysis. Distinctions between Types of Data are not hard and fast. However, making such distinctions helps to portray the range of data available and to uncover implicit assumptions, particularly as they relate to types of theory and types of method in criminological research.

  Types of Data

  Quantitative and qualitative data

  First, we can distinguish data which are quantitative and data which are qualitative (sometimes also referred to as non-quantitative). Whether or not the use of the term ‘qualitative data’ is indicative of superiority vis-à-vis quantitative data is one of the fundamental issues implicit in the distinction. Quantitative research in criminology is founded on the assumption that the objects of inquiry—whether these be the characteristics of individuals or features of whole societies— can be defined and delineated unambiguously. What is more, this is linked to assertions that particular features of these objects can be categorized ‘objectively’ by the researcher and can be measured by the application of numbers to such categories and also to the number of cases within each of the categories.

  The emphasis which is placed upon measurement in quantitative criminology is closely associated with a strong investment in statistical analysis and particularly the use of ‘statistics of association’ which provide an indication of the extent to which variables co-vary. A typical example is the correlation coefficient , the size of which measures the strength of relationship between two specified variables. Correlation analysis will be considered in greater detail in Chapter 3

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Practical Research Methods in Education

  An Early Researcher's Critical Guide

  • Mike Lambert(Author)
  • 2019(Publication Date)
  • Routledge

    (Publisher)

  This chapter rejects this view, taking as its starting point the notion that researchers should view them merely as different Types of Data and that they therefore need to understand how both are collected, analysed and interpreted. As using the quantitative option can seem particularly problematic, here are three reasons why readers who are nervous of numbers should con- sider familiarizing themselves with quantitative data processes: • Having some understanding of quantitative data will improve your ability to assess investigations which use quantitative approaches, particularly if you are new to research. • Being able to incorporate quantitative data into your own research could strengthen its impact, particularly if you are interested in influencing aspects of education practice or policy. Although they often use them carelessly and unreflectively, policymakers like numbers. • Having an improved understanding of quantitative data and research will put you in a better position to evaluate or conduct mixed-methods investigations. 56 Michael Jopling What is quantitative research? What are quantitative data? The application of quantitative methods in social-science research derives from interest in the 19th century in experimental investigation in the natural sciences. This sought to understand the processes of cause and effect through manipulation and controlled testing. However, it became clear that many of the issues explored in social sciences, such as educa- tion, do not lend themselves well to experimental approaches, so researchers began from the 1950s to apply them to what became known as ‘quasi-experimental’ and ‘non-experimental’ situations – these approaches are considered in more detail later in the chapter. Provocatively, Berliner (2002:19) has described what social scientists, and particularly educational researchers, do as the ‘hardest to do science […] because humans in schools are embedded in complex and changing networks of social interaction’.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Essential Environmental Science

  Methods and Techniques

  • Simon Watts(Author)
  • 2003(Publication Date)
  • Routledge

    (Publisher)

  Social surveys can produce both quantitative (numeric) and qualitative (non-numeric) data. There is no absolute division between the two, but for general purposes qualitative data can be said to consist essentially of words – descriptions and discourse. You may find this kind of data useful in providing information on feelings, such as people's values, emotions, attitudes, predictions, hopes and aspirations. It is useful in illuminating changes through space and time. It is suitable, for example, for illustrating changing attitudes of the nation, or groups within the nation, towards ‘green’ issues over the past few years.

  Qualitative data are produced by what are known as ‘open-ended’ questions. These allow people to give general ‘free’ answers because responses are not guided into defined categories, Open-ended questions can supply rich insights into a person's feelings or life history but can be difficult to analyse, especially in a way that allows comparability between different respondents.

  Quantitative data are generally considered most useful in providing information on people's reasons for doing things, past events and preferences – things that can validly be expressed in numeric terms. Questions aimed at obtaining quantitative data provide fixed numeric values and are known as ‘closed’ or ‘precoded’ questions. Responses to them are restricted by the range of permissible answers that you supply. Such data are easier to analyse and can indicate strength of opinion or attitude and specific reasons. However, the restricted range of specific responses permitted to respondents may restrict or bias the answers received, notably because the specified responses may be quite different from those which the respondent would have said unprompted (Fink and Kosecoff, 1985).

  Questionnaires tend to rely more on the use of the closed type of questions and quantitative data. Nevertheless data of both quantitative and qualitative nature are useful in different situations, providing the researcher with a variety of perspectives from which to address the research brief. Table 9.2 compares the merits of qualitative and quantitative data although its coverage is far from exhaustive. In Table 9.2

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Statistics for the Social Sciences

  A General Linear Model Approach

  • Russell T. Warne(Author)
  • 2017(Publication Date)
  • Cambridge University Press

    (Publisher)

  2 Levels of Data In Chapter 1, we saw that most of the social sciences are dominated by quantitative research, which is a form of research in which researchers convert their data into numbers so that they can be analyzed with statistics. This chapter is about the process of converting data into numbers – a process called measurement. For researchers in the physical sciences, the process of measuring data numerically is often relatively straightforward. Physicists, chemists, and biologists all know how to measure the weight, speed, or length of an object, and there are few disagreements in these fields about how to gather numerical data. For social scientists the process of recording data in numbers can be a particularly difficult problem. For example, let’ s imagine a researcher who wants to determine whether parents who are more affectionate to one another are also more attentive to their children. How should the researcher measure “affection” and “attentiveness”? Unlike height or weight, there is no obvious way that these variables should be measured. Indeed, it is possible that researchers – especially from different branches of the social sciences – could disagree vigorously about how to measure these variables. This chapter explains the basic principles of measurement and the main framework for thinking about the measurement of variables. Learning Goals • Explain what an operationalization is and why it is necessary to create operationalizations to conduct research. • Distinguish among the four levels of data in the organization system created by Stevens (1946). • Demonstrate the benefits of collecting data at the highest level possible. • Explain the differences between continuous and discrete data. Defining What to Measure The first step in measuring quantitative variables is to create an operationalization for them.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Applying Theories for Information Systems Research

  • Tiko Iyamu(Author)
  • 2021(Publication Date)
  • Routledge

    (Publisher)

  In Lethbridge (1998), in which the survey research approach was employed, questions such as ‘What is your current knowledge about this, considering what you have learned on the job as well as forgotten? How useful has this specific material been to you in your career?’ were formulated and used in the data collection. Data collection Data collection involves the use of different techniques and approaches. Also, it is a series of interrelated activities aimed at gathering relevant information to answer the research questions that will arise or that were predefined (Cresswell, 2007). The techniques and approaches are selected based on the objectives of the study, and in alignment with the research design that are discussed above. Some of the techniques and approaches are interviews, questionnaires, observation, and document analysis. Data sources Oftentimes, data are collected from different sources that are usually categorised into primary and secondary. The different Types of Data collection techniques that are mostly associated with qualitative studies in IS are shown in Figure 2.2. The use of different methods for data collection enables the researcher to elicit different versions and forms of realities from participants. The possibility of using more than one of the techniques for data collection was suggested by Gillham (2000). In qualitative research, the primary data source is usually the interview technique, and the data from document analysis is considered secondary. The secondary data are usually existing, and may come as sources such as academics’ databases, and companies’ repositories, including websites. The types of materials (data) include peer-reviewed articles, white papers, strategy documents, financial reports, and annual reports

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Research Methods for Language Teaching

  Inquiry, Process, and Synthesis

  • Netta Avineri(Author)
  • 2017(Publication Date)
  • Red Globe Press

    (Publisher)

  Corpus analysis ( http:// www.essex.ac.uk/linguistics/external/clmt/w3c/corpus_ling/ content/introduction.html ) can be used in combination with more qualitative approaches (e.g., coding, discourse analysis, content analysis). Conclusion This chapter provided you with terminology, concepts and approaches necessary to engage in quantitative data collection that may be useful for your research questions. You can use these data collection techniques on their own, in combination with one another, or in combination with qualitative data. These choices depend entirely on the research questions you are attempting to answer with one type or multiple Types of Data. Once you have these data, you can begin the data anal-ysis process, which may include frequency distributions, percentage distributions, descriptive statistics and/or inferential APPROACHES TO COLLECTION OF QUANTITATIVE DATA 157 statistics (with the assistance of online tools in many cases) (to be discussed in Chapter 11 ). Suggested Readings http://study.com/academy/lesson/research-designs-quasi-exper imental-case-studies-correlational.html This straightforward online video provides clear descriptions and distinctions (with visuals) for different types of psychometric research designs. There is also a quiz in case you want to make sure that the various approaches are clear to you. https://statistics.laerd.com This useful resource provides information about basic key concepts in statistics, including variable types and descriptive vs inferential statistics. In order to access all of its functionalities, you need to pay for a subscription. http://www.socialresearchmethods.net/kb/measlevl.php This online resource provides basic information about levels of measurement, which is necessary for the effective analysis of quantitative data. http://psc.dss.ucdavis.edu/sommerb/sommerdemo/scaling/levels. htm This is another useful online tool with clear guidance on levels of measurement for analysis of quantitative data.

  Sign up to read

  Learn more about book