# 1.4. Identify variables contributing to a problem situation

[responsivevoice_button rate=”0.9″ voice=”UK English Female” buttontext=”Listen to Post”]

At times we need to undertake an inquiry to answer a specific question that has arisen, or to provide very specific information needed to solve a problem; for example we want to investigate the crime of theft in retail stores in order to prevent or minimise it in our own workplace; or perhaps we want to know more about the causes of absenteeism in the workplace. This investigation is called “research”.

**Research**

The term research has been used in so many contexts and with such a variety of meanings that it is difficult for the learner to sort it all out. Much of what we have been taught about research is based on misconceptions. Advertisements on television proudly boast that research has revolutionised a product, when in reality a marketing department has simply made a small change in the product designed to increase its appeal to consumers. Teachers give students an assignment called a “research paper” which mainly consists of gathering information from books and encyclopaedias and reorganising it and regurgitating it on a student -authored paper. These and other activities have been mislabelled research. They are more correctly, information gathering, note taking, library skills, or sales jobs.

In order to understand what research is – we need to understand what research is **not**:

- Research is not just information gathering. A student going to the library and reading information on African Elephants is not research.
- Research is not rearranging facts. A student writing a report on behaviour of pendulums is not research.
- Research is not a sales pitch. A new improved toothpaste developed after years of research is rarely if ever real research.

True research is a quest driven by a specific question which needs an answer. Paul Leedy, in his book “Practical Research: Planning and Design” lists eight characteristics of research which serve us well in defining research for the learner.

Research:

- Originates with a question or a problem.
- Requires a clear articulation of a goal.
- Follows a specific plan of procedure.
- Usually divides the principal problem into more manageable sub-problems.
- Is guided by the specific research problem, question, or hypothesis.
- Accepts certain critical assumptions. These assumptions are underlying theories or ideas about how the world works.
- Requires the collection and interpretation of data in attempting to resolve the problem that initiated the research.
- Is, by its nature, cyclical. New questions arise as you find information.

Research is:

- A systematic investigation towards increasing the sum or knowledge (Chambers 20th Century Dictionary)
- An endeavour to discover new or collate old facts, etc. by the scientific study of a subject or by a course of critical investigation. (The Concise Oxford Dictionary)

**Research:**

- Originates with a question or problem
- Requires a clear articulation of a goal
- Requires a specific plan of procedure
- Usually divides the principal problem into more manageable sub-problems
- Is guided by the specific research problem or question
- Accepts certain critical assumptions
- Requires the collection and interpretation of data in attempting to resolve the problem that initiated the research

**Remember:**

**Work from the General to the Specific:**Find background information first, then use more specific and recent sources.**Record what you find and Where you find it:**Write out a complete citation for each source you find; you may need it again later.**Translate your topic into the subject language of the indexes and catalogues you use:**Check your topic words against a thesaurus or subject heading list.

**Research using statistical methods**

When we do research using statistical methods, we follow these basic steps:

- Identify the problem that needs to be solved and write a research question.
- Collect data.
- Organise the data.
- Represent the data.
- Interpret the data to answer the research question.

Once we have identified the goal or objective of the statistical inquiry (Step 1), so that we know what and how much information we need to enable us to make a decision in order to solve our problem, we need to consider the following:

- Can the required information be given in
**numerical terms**? Most situations / issues can be dealt with through statistical methods when you are able to collect data which can then be analysed with numbers. The incidence of drunkenness at work, for example, can be measured by numbers of warnings, which will depend on the alertness and efficiency of security staff, management or the team leader, or the incidence of theft at POS can be linked to time of day and accessibility, while theft of new stock can be linked to location and processes. - What is the
**precise definition**of the object to be measured? In a wage inquiry, for example, should the data be wage rates or actual earnings, should allowance be made for overtime and bonuses, should earnings be gross or net, before or after tax, etc.? - What should be the
**field of inquiry**? How wide do we spread our net? Are we just looking at employee theft, or are we including supplier and customer theft? - Is there any
**information already available**from routine statistics or published sources, or must we gather all the data ourselves?

*Variables*

*Variables*

We are going to be learning more about the Research Steps as we go along, but first we need to define the concept of “variables”.

The data we gather in the course of our research must be sorted according to the numerical value of some characteristic, called a **variable.**

**Variables** are things that we measure, control, or manipulate in research. They differ in many respects, most notably in the role they are given in our research and in the type of measures that can be applied to them. Thus a number of people might be sorted according to their height, age, weight, or any other characteristic capable of being measured. Retail theft can be sorted into employee, customer and vendor theft; absenteeism can be sorted into scheduled and unscheduled absenteeism.

Variables differ in “how well” they can be measured, i.e. in how much measurable information their measurement scale can provide. The amount of information that can be provided by a variable is determined by the type of measurement scale used.

Specifically variables are classified as:

- Nominal;
- Ordinal;
- Interval; or
- Ratio.

**Nominal variables**allow for only qualitative classification. That is, they can be measured only in terms of whether the individual items belong to some distinctively different categories, but we cannot quantify or even rank order those categories. For example, all we can say is that two individuals are different in terms of variable A (e.g. they are of different races), but we cannot say which one “has more” of the quality represented by the variable. Typical examples of nominal variables are gender and race.**Ordinal variables**allow us to rank order the items we measure in terms of which has less and which has more of the quality represented by the variable, but still they do not allow us to say “how much more.” A typical example of an ordinal variable is the socio-economic status of families. For example, we know that upper-middle is higher than middle, but we cannot say that it is, for example, 18% higher.**Interval variables**allow us not only to rank order the items that are measured, but also to quantify and compare the sizes of differences between them. For example, temperature, as measured in degrees Celsius, constitutes an interval scale. We can say that a temperature of 40 degrees is higher than a temperature of 30 degrees, and that an increase from 20 to 40 degrees is twice as much as an increase from 30 to 40 degrees.**Ratio variables**are very similar to interval variables; in addition to all the properties of interval variables, they feature an identifiable absolute zero point, thus they allow for statements such as x is three times more than y. Typical examples of ratio scales are measures of time or space. For example, as the Kelvin temperature scale is a ratio scale, not only can we say that a temperature of 200 degrees is higher than one of 100 degrees, but we can also correctly state that it is twice as high. Interval scales do not have the ratio property. Most statistical data analysis procedures do not distinguish between the interval and ratio properties of the measurement scales.

*Continuous and discrete variables*

*Continuous and discrete variables*

A variable that can take only discrete (whole) values, is called discrete or discontinuous- a man cannot have 6.3 children, there cannot be 5.2 houses in the street, or 3.5 rooms in a house.

A continuous variable, on the other hand, can take any value within a range: temperature need not be an exact number of degrees, but may be measured to several decimal places, e.g. 37.5°C and every object whose temperature is rising or falling will, during the process, take every possible temperature between the final and initial values. Similar examples are the speed of a vehicle and the height of a growing plant. In other words a continuous variable can take an infinite number of values.