4-Variable-Nature-of-Data.pdf

RESEARCH METHODS DR. JUDY ANN FERRATER-GIMENA Director, Research Center University of Cebu-Banilad  VARIABLES Defining Variables An important step when beginning a research project is to define the variables in the study. Some variables are fairly easy to define, ma...

RESEARCH METHODS DR. JUDY ANN FERRATER-GIMENA Director, Research Center University of Cebu-Banilad  VARIABLES Defining Variables An important step when beginning a research project is to define the variables in the study. Some variables are fairly easy to define, manipulate, and measure. Researchers must operationally define all variables: those measured (dependent variables) and those manipulated (independent variables). One reason for so doing is to ensure that the variables are measured or manipulated consistently during the course of the study. Another reason is to help communicate ideas to others.  VARIABLES Properties of Measurement In addition to operationally defining independent and dependent variables, we must consider the level of measurement of the dependent variable. There are four levels of measurement, each based on the characteristics, or properties, of the data. These are the following: 1. Identity – a property of measurement in which objects that are different receive different scores. 2. Magnitude – a property of measurement in which the ordering of 1 is the same amount ordering of the variable. 3. Equal unit size – a property of measurement in which a difference of 1 is the same amount through the entire scale. 4. Absolute zero – a property of measurement in which assigning a score of zero indicated an absence of the variable being measured. Scales of Measurement The level, or scale, of measurement depends on the properties of the data. There are four scales of measurement. 1. Nominal – a scale in which objects or individuals are assigned to categories that have no numerical properties 2. Ordinal – a scale in which objects or individuals are categorized and the categories form a rank order along a continuum. 3. Interval – a scale in which the units of measurement (intervals) between the numbers on the scale are all equal in size. 4. Ration – a scale in which in addition to order and equal units of measurement there is an absolute zero that indicated an absence of the variable being measured. Discrete and Continuous Variables Another means of classifying variables is in terms of whether they are discrete or continuous in nature. Discrete variables usually consist of whole number units or categories. They are made up of chunks or units that are detached and distinct from one another. A change in value occurs a whole unit at a time; decimals do not make sense in discrete scales. Most nominal and ordinal data are discrete. For example, gender, political party, and ethnicity are discrete scales. Some interval or ratio data can be discrete. For instance, the number of children someone has is reported as a whole number (discrete data), yet it is also ratio data (you can have a true zero and form ratios). Continuous variables usually fall along a continuum and allow for fractional amount. The term continuous means that it continues between the whole number units. Example of continuous variables are age (22.7 years), height (64.5 inches), and weight (113.25 pounds). Most interval and ratio data are continuous in nature. Types of Measures When researchers collect data, the types of measures used can be classified into four basic categories: self-report measures, tests, behavioral measures, and physical measures. 1. Self-report measures are usually questionnaires or interview that measure how people report that they act, think, or fell. 2. Tests are measurement instruments used to assess individual differences in various content areas. 3. Behavioral measures are measures taken by carefully observing and recording behavior. 4. Physical measures are measures of bodily activity such as pulse or blood pressure that may be taken with a piece of equipment. NATURE OF DATA We are living in the information age with which the amount of information in the world is doubling every second. Data is another word for bits of information (singular – datum). Research uses data as the raw material in order to come to conclusions about some issue. It depends on the issue being investigated what data needs to be collected. Data do not permanently represent the solid fact but it is true for a time and for a particular place as observed by a particular person. It is ephemeral. It may be true for a time but not forever. Data are not only ephemeral but also corruptible. Hearsay, second hand reports and biased views are often paraded as facts. The further the information was taken, the more it is subjected to inconsistencies and inaccuracies. NATURE OF DATA Memory fades, details are lost, recording methods do not allow a full picture to the given, and distortions of interpretations occur. Because it is dangerous for a researcher to insist that his or her data, and findings derived form them are infallible, the outcomes of research are often couched in ‘soft’ statements, such as ‘it seems that’, ‘it is likely that’, ‘one is lead to believe that’, etc. This does not mean, however, that the knowledge gained is useless, only that it is not absolutely certain, like most thins in life. Data relate to knowledge as a whole in the sense that they are part of hierarchy of information, going from the general to the particular, from abstract to concrete. This hierarchy makes if possible to break down research problems expressed in theoretical language to more practical components that can be measured in some way. This hierarchy can be expressed like this: Theory – abstract statements that make claims about the world and how it works. Research problems are usually stated at the theoretical level. Concepts – building blocks of the theory which are usually abstract and cannot be directly measured. Indicators – phenomena which point to the existence of the concepts. Variables – components of the indicators which can be measured. Values – actual units of measurement of the variables. These are data in their most concrete form. For example: Ø Theory – poverty leads to poor health. Ø Concepts – poverty, poor health. Ø Indicators of poverty – low income, poor living conditions, restricted diet, etc. Ø Variables of poor living conditions – levels of overcrowding, provision of sanitary facilities, infestations of vermin, levels of litter, etc. Ø Values of levels of overcrowding – numbers of people per room, floor areas of dwellings, numbers of dwellings per hectare, et  Theory. It refers to a statement that makes a claim about a phenomenon. Theories can range from complex, large-scale, well-researched and substantiated claims developed through academic research, to informal guesses or hunches about specific situations. As theories statements tend to be expressed in abstract terms, it is necessary to break them down into their constituent parts in order to examine them. The statements are usually made up of concepts and how they relate. Concepts. A concept is a term for particular phenomenon, often quite abstract, such as alienation, socialism, equilibrium, society, but can also be quite concrete, such as animal, town and income. We use concepts all the time as they are an essential part of understanding the world and communicating with other people. Many common concepts are shared by everyone in a society, though there are variations in meaning between different cultures and languages. Concepts should be clearly defined so that they can be understood in the same way by everyone. This is relatively easy in the natural sciences where precise definitions of concepts such as radio waves, acceleration and elements are possible. In the humanities and social sciences this may be much more difficult – e.g. concepts such as beauty, honour, motivation, kinship etc. – as their meanings are often based on opinions, emotions, values, traditions etc.  Indicators. This helps simplify the abstract idea of concept. For example, hunger is a concept which may be difficult to measure but you can look for indicators of hunger. These indicators are those perceivable phenomena that give an indication that the concept is present. Variables. This is the measureable component of an indicator. In the case of hunger above, it would be very difficulty to measure the level of hunger, but you could easily measure a person’s weight. Values. These are the units of measurement used to gauge the variables. The level of the precision of measurement depends on the nature of the variable and the type of values that are appropriate. Certain scientific studies require that variables are measured incredibly accurately, whilst some social variables might only be gauged on a three- point scale such as ‘agree’, ‘neutral’, ‘disagree;.  Data, seen as bits of information, can be at any level of abstraction. Research projects usually start tat the more abstract end of the spectrum, move to the more concrete during the investigation, and return to the abstract on the conclusions. These levels of abstraction can be related on how to structure the research. The title and main research question will be expressed at a theoretical level, and the sub-questions will be about the separate concepts. In order to investigate these, there is a need to find out what type of measures can be used to assess the existence and scale of the concepts, then the scales that can be used in the measures, i.e. the type of measurements, and finally the actual measurements that provide the basic data for analysis. On the next page is a diagram illustrating the levels of abstraction in the research structure. More abstract Theory Main Problem Concepts Sub-problems Indicators Data Types Variables Data Measures Values Measurements More Concrete Primary and Secondary Data Data come in two main forms, depending on its closeness to the event recorded. They are the following: 1. Primary data – data that has been observed, experienced or recorded close to the event are thenearest one can get to the truth; are the first and most immediate recording of a situation. These are data collected by the investigator himself/herself for a specific purpose. Example for this kind ofdata is the data collected by a student for his/her research project. There are four basic types of primary data, distinguished by the way they are collected: a.) Measurement – collection of numbers indicating amounts, e.g. voting polls, exam results, car mileages, room temperatures, fuel prices. b.) Observation – records of events, situations or things experienced with your own senses and perhaps with the help of an instrument, e.g. camera, tape recorder, microscope c.) Interrogation – data gained by asking and probing, e.g. information about people’s convictions, likes and dislikes. d.) Participation – data gained by experiences of doing things, e.g. the experience of learning to ride a bike tells you different things about balance, dealing with traffic etc., rather that just observing. 2. Secondary data – data that have been collected, interpreted and recorded by someone else for some other purpose (but being utilized by the investigator for another purpose). Example of this type of data is the census data.