Part One

Variables in Research

The purpose of all research is to describe and explain variance in the world. Variance

is simply the difference; that is, variation that occurs naturally in the world or change that we

create as a result of a manipulation. Variables are names that are given to the variance we

wish to explain.

Continuous and Discontinuous Variables (“Research Methods,” n.d.)

Variables have different properties and to these properties we assign numerical

values. If the values of a variable can be divided into fractions then we call it a continuous

variable. Such a variable can take infinite number of values.

Examples: Income, temperature, age, or a test score

These variables may take on values within a given range or, in some cases, an infinite

set.

Any variable that has a limited number of distinct values and which cannot be divided into

fractions, is a discontinuous variable. Such a variable is also called as categorical variable

or classificatory variable, or discrete variable. Some variables have only two values,

reflecting the presence or absence of a property.

Example: employed-unemployed or male-female have two values

These variables are referred to as dichotomous. There are others that can take added

categories such as the demographic variables of race, religion.

An automotive variable, for example, where "Chevrolet" is assigned a 5 and "Honda" is

assigned a 6, provides no option for a 5.5(i.e. the values cannot be divided into fractions).

Continuous and Discontinuous Variables (continuation from other source)

Categorical variables are also known as discrete or qualitative variables.

Categorical variables can be further categorized as either: nominal, ordinal or dichotomous.

Nominal variables are variables that have two or more categories, but which do not

have an intrinsic order. For example, a real estate agent could classify their types of property

into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is

a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note,

the different categories of a nominal variable can also be referred to as groups or levels of the

nominal variable. Another example of a nominal variable would be classifying where people

live in the USA by state. In this case there will be many more levels of the nominal variable

(50 in fact).

Dichotomous variables are nominal variables which have only two categories or

levels. For example, if we were looking at gender, we would most probably categorize

somebody as either "male" or "female". This is an example of a dichotomous variable (and

also a nominal variable). Another example might be if we asked a person if they owned a

mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In

the real estate agent example, if type of property had been classified as either residential or

commercial then "type of property" would be a dichotomous variable.

Ordinal variables are variables that have two or more categories just like nominal

variables only the categories can also be ordered or ranked. So if you asked someone if they

liked the policies of the Democratic Party and they could answer either "Not very much,”

"They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3

categories, namely "Not very much,” "They are OK" and "Yes, a lot" and you can rank them

from the most positive (Yes, a lot), to the middle response (They are OK), to the least

positive (Not very much). However, whilst we can rank the levels, we cannot place a "value"

to them; we cannot say that "They are OK" is twice as positive as "Not very much" for

example.

Others includes:

Dummy Variables from Quantitative Variables

“Types of Variables” (2012) described that a quantitative variable can be

transformed into a categorical variable, called a dummy variable by recoding the values.

Consider the following example: the quantitative variable Age can be classified into five

intervals. The values of the associated categorical variable, called dummy variables, are 1,

2,3,4,5:

[Up to 25] 1

[25, 40 ] 2

[40, 50] 3

[50, 60] 4

[Above 60] 5

Preference Variables

“Types of Variables” (2012) described that preference variables are specific discrete

variables, whose values are either in a decreasing or increasing order. For example, in a

survey, a respondent may be asked to indicate the importance of the following nine sources of

information in his research and development work, by using the code [1] for the most

important source and [9] for the least important source:

Literature published in the country

Literature published abroad

Scientific abstracts

Unpublished reports, material, etc.

Discussions with colleagues within the research unit

Discussions with colleagues outside the research unit but within institution

Discussions with colleagues outside the institution

Scientific meetings in the country

Scientific meetings abroad

Note that preference data are also ordinal. The interval distance from the first

preference to the second preference is not the same as, for example, from the sixth to the

seventh preference.

Multiple Response Variables

“Types of Variables” (2012) described that multiple response variables are those,

which can assume more than one value. A typical example is a survey questionnaire about

the use of computers in research. The respondents were asked to indicate the purpose(s) for

which they use computers in their research work. The respondents could score more than

one category.

Statistical analysis

Lab automation/ process control

Data base management, storage and retrieval

Modeling and simulation

Scientific and engineering calculations

Computer aided design (CAD)

Communication and networking

Graphics

Continuous variables are also known as quantitative variables. Continuous

variables can be further categorized as either interval or ratio variables.

Interval variables are variables for which their central characteristic is that they can

be measured along a continuum and they have a numerical value (for example, temperature

measured in degrees Celsius or Fahrenheit). So the difference between 20C and 30C is the

same as 30C to 40C. However, temperature measured in degrees Celsius or Fahrenheit is

NOT a ratio variable.

Ratio variables are interval variables, but with the added condition that 0 (zero) of

the measurement indicates that there is none of that variable. So, temperature measured in

degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no

temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often

called absolute zero) indicates that there is no temperature whatsoever. Other examples of

ratio variables include height, mass, distance and many more. The name "ratio" reflects the

fact that you can use the ratio of measurements. So, for example, a distance of ten metres is

twice the distance of 5 metres.

Other include:

Continuous Ordinal Variables

They occur when the measurements are continuous, but one is not certain whether

they are on a linear scale, the only trustworthy information being the rank order of the

observations. For example, if a scale is transformed by an exponential, logarithmic or any

other nonlinear monotonic transformation, it loses its interval - scale property. Here, it would

be expedient to replace the observations by their ranks (“Types of Variables,” 2012).

Dependent and Independent Variables (“Research Methods,” n.d.)

Researchers who focus on causal relations usually begin with an effect, and then

search for its causes. The cause variable, or the one that identifies forces or conditions that

act on something else, is the independent variable. This is also called explanatory

variable (“Variable Types,” 2011). The variable that is the effect or is the result or

outcome of another variable is the dependent variable (also referred to as outcome variable

or effect variable). This is also called response variable (“Variable Types,” 2011). The

independent variable is "independent of" prior causes that act on it, whereas the dependent

variable "depends on" the cause. It is not always easy to determine whether a variable is

independent or dependent. Two questions help to identify the independent variable.

First, does it come before other variable in time?

Second, if the variables occur at the same time, does the researcher suggest that one

variable has an impact on another variable?

Independent variables affect or have an impact on other variables. When independent

variable is present, the dependent variable is also present, and with each unit of increase in

the independent variable, there is an increase or decrease in the dependent variable also.

In other words, the variance in dependent variable is accounted for by the independent

variable. Dependent variable is also referred to as criterion variable.

In the research vocabulary different labels have been associated with the independent

and dependent variables like:

Independent variable Dependent variable

Presumed cause presumed effect

Stimulus Response

Predicted from ... Predicted to ...

Antecedent Consequence

Manipulated Measured outcome

Predictor Criterion

Example:

“Types of Variable” (n.d.) give example as such Imagine that a tutor asks 100

students to complete a maths test. The tutor wants to know why some students perform better

than others. Whilst the tutor does not know the answer to this, she thinks that it might be

because of two reasons: (1) some students spend more time revising for their test; and (2)

some students are naturally more intelligent than others. As such, the tutor decides to

investigate the effect of revision time and intelligence on the test performance of the 100

students. The dependent and independent variables for the study are:

Dependent Variable: Test Mark (measured from 0 to 100)

Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ

score)

Moderating Variable (“Research Methods,” n.d.)

A moderating variable is one that has a strong contingent effect on the independent

variable-dependent variable relationship. That is, the presence of a third variable (the

moderating variable) modifies the original relationship between the independent and the

dependent variable.

Example:

A strong relationship has been observed between the quality of library facilities (X)

and the performance of the students (Y).

Although this relationship is supposed to be true generally, it is nevertheless

contingent on the interest and inclination of the students. It means that only those students

who have the interest and inclination to use the library will show improved performance in

their studies. In this relationship interest and inclination is moderating variable i.e. which

moderates the strength of the association between X and Y variables.

Intervening Variable (“Research Methods,” n.d.)

A basic causal relationship requires only independent and dependent variable. A third

type of variable, the intervening variable, appears in more complex causal relationships. It

comes between the independent and dependent variables and shows the link or mechanism

between them. Advances in knowledge depend not only on documenting cause and effect

relationship but also on specifying the mechanisms that account for the causal relation. In a

sense, the intervening variable acts as a dependent variable with respect to independent

variable and acts as an independent variable toward the dependent variable.

Example:

A theory of suicide states that married people are less likely to commit suicide than

single people. The assumption is that married people have greater social integration (e.g.

feelings of belonging to a group or family). Hence a major cause of one type of suicide was

that people lacked a sense of belonging to group (family). Thus this theory can be restated as

a three-variable relationship: marital status (independent variable) causes the degree of

social integration (intervening variable), which affects suicide (dependent variable).

Specifying the chain of causality makes the linkages in theory clearer and helps a researcher

test complex relationships.

Extraneous Variables (“Research Methods,” n.d.)

An almost infinite number of extraneous variables (EV) exist that might conceivably

affect a given relationship. Some can be treated as independent or moderating variables, but

most must either be assumed or excluded from the study. Such variables have to be identified

by the researcher. In order to identify the true relationship between the independent and the

dependent variable, the effect of the extraneous variables may have to be controlled. This

is necessary if we are conducting an experiment where the effect of the confounding factors

has to be controlled. Confounding factors is another name used for extraneous variables.

Other explanatory terms (“Variables in research,” 2015):

Attributes are sub-values of a variable, such as 'male' and 'female'. An exhaustive list

contains all possible answers, for example gender could also include 'male transgender' and

'female transgender' (and both can be pre- or post-operative).

Mutually exclusive attributes are those that cannot occur at the same time. Thus in a

survey a person may be requested to select one answer from a list of alternatives (as opposed

to selecting as many that might apply).

Units are the ways that variables are classified. These include: individuals, groups,

social interactions and objects.

Part Two

Defining Terms in Research

It is important to define the different terms one uses in their research paper because

the reader will have a better understanding of the topic (“Art of Craftsmanship, 2012).

Here are a few reasons why it is important to define “key” terms one uses in their

research paper:

Must define “key” terms one uses in the research paper to ensure that the reader gets a

better understanding of the topic.

Defining the “key” terms in the research paper will help the readers know the

definition of words they may have never heard of.

Defining the “key” terms in the research paper gives the paper more credibility.

Defining the “key” terms in the research paper makes the paper easier to read.

Defining the “key” terms in the research paper lets the reader know that the writer

understands what they wrote and there will be no confusion regarding the main topic

of the research paper.

A formal definition consists of three parts.

1. The term (word or phrase) to be defined

2. The class of object or concept to which the term belongs.

3. The differentiating characteristics that distinguish it from all others of its class

Here are the Guidelines in defining terms in research (Globio, 2013):

1. Only terms, words, or phrases which have special or unique meanings in the study are

defined. For instance, the term non-teaching facilities may be used in the study of the

teaching of science. Non-teaching facilities may be defined as facilities needed by the

students and teachers but are not used to explain the lesson or to make instructions

clearer. Examples are toilets or comfort rooms, electric fans, rest rooms or lounges,

and the like. They may also be called non- instructional facilities.

2. Terms should be defined operationally, that is how they are used in the study. For

instance, a study is made about early marriage. What is meant by early marriage? To

make the meaning clear, early marriage may be defined as one in which the

contracting parties are both below eighteen years of age.

3. The researcher may develop his own definition from the characteristics of the term

defined. Thus, a house of light materials may be defined as one with bamboo or small

wooden posts, nipa, buri, or nipa walls; split bamboo floor and cogon or nipa roof.

This is also an operational definition.

4. Definitions may be taken from encyclopedias, books, magazines and newspaper

articles, dictionaries, and other publications but the researcher must acknowledge his

sources. Definitions taken from published materials are called conceptual or

theoretical definitions.

5. Definitions should a\be brief, clear, and unequivocal as possible.

6. Acronyms should always be spelled out fully especially if it is not commonly known

or if it is used for the first time

Basic Methods of Defining Terms (“Methods of Defining Terms,” 2010)

The basic methods of defining terms are as follows:

1. Example- Giving an example is often an effective method of defining terms. Giving

your audience a specific example of the type of program you propose.

2. Common Usage- In the interest of accuracy and precision, debate propositions must

sometimes contain technical terms. Often these terms can be defined effectively by

referring to common usage. Establish a definition clear to the audience. To qualify as

common usage a term must be commonly understood across lines of gender, age, and

culture.

3. Authority- Some terms may be defined most effectively by referring to an authority

qualified to state the meaning and usage of the term. Dictionaries, encyclopedias, and

books or articles by recognized scholars are often used as authority for a particular

definition.

4. Operation- Some terms are best defined if the advocate provides an operational

definition and explains the function or special purpose represented by the terms in a

specific context. The use of operation as a method of definition is often linked with

the presentation of a plan and is a helpful way of explaining a complex matter.

5. Negation- Sometimes a term may be defined effectively by indicating what it does

not mean.

Basic Methods of Defining Terms (continuation form other source)

Four categories of definitions are offered (“Chapter 7: Defining Terms,” 2013):

Parenthetical definition – Used when defining a word that the reader will easily

understand if shown a synonym or alterative phrase. It shows the synonym in parentheses

immediately after the word.

Example: The software has received many reviewers' accolades (praises).

Defining phrase - It is sometimes beneficial to use a term as it is used by the

profession you are writing about, and to follow the term with a few words to explain it.

Example: A technical writer should ask a colleague to proof each draft, to read through it for

errors.

Formal sentence - A formula is given for writing a definition in a sentence. Start with

the word or phrase, state the class (the category the word belongs to), and the distinguishing

characteristics that make it different from other members of its class. Example: An

operational definition (phrase) is the specific meaning of a word or phrase (class) given to it

by the group of people who use the word in their specific context (distinguishing

characteristics).

Extended definition - This version of a definition is the longest, offering the freedom

to provide insight into the origin of the term, additional meanings, synonyms and antonyms,

and more. Additional terms are defined here that may be used in definitions:

Denotation - The most basic meaning of a term, usually the first meaning in a

dictionary entry

Connotation - The most familiar meaning of a term to a user

Synonym - Another word or phrase that has the same or almost the same meaning

Antonym - Another word or phrase that has the opposite meaning

Description - As used here, a longer, more detailed description than is provided by a

single formal sentence. This may include a description of each part of a device.

Contrast - A discussion of the term as it compares to a term that has a different

meaning

Comparison - A discussion of the term as it relates to a term similar in meaning

Analogy - This method uses similes or metaphors to explain a thing as being like

something else. Example: a network router is a device that works like an airport

traffic controller, keeping network signals moving toward their destinations, and

avoiding collisions between them.

Origin - An explanation of where a term comes from, who first used it, or the

circumstances in which it was first used.

Etymology - A more formal statement about the language roots of a word. Technical

terms may not have actual roots of this sort. The author explains that they are often

acronyms that are better understood when the letters or parts of the word are

explained.

Common Errors in Definitions

Definition fallacies are errors a technical writer might make when writing definitions.

Most are easily understood:

too technical - the writer has not explained plainly enough;

too broad - the writer has not focused on what the word means in context;

too narrow - the writer has not given a definition that is general enough that the

reader might recognize other cases of the thing being defined; and

circular - typically, the writer has defined a phrase using one of the words in the

phrase, or defined a word using another form of the same word.

Bibliography

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Chapter 7: Defining Terms. (2013). Retrieved December 13, 2015, from http://stevevincent.info/ITS311_7.htm

Globio, E. (2013). Thesis and Dissertation Guidelines: Thesis Writing: Definition of Terms. Retrieved December 13, 2015, from http://thesisadviser.blogspot.com/2013/02/thesis-writing-definition-of-terms.html

Methods of Defining Terms. (2010). Retrieved December 13, 2015, from http://www.angelfire.com/hi2/mango/met.html

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