Research Assignment

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 Variables have different properties and to these properties we assign numerical values. If the value valuess of a vari variabl ablee can can be divided into fractions then then we we call call it it a continuous variable. Such a variable can take infinite nuber of values . !"a#les: Income, temperature, age, or a test score

These variables may tae on values within a given range or, in some cases, an infinite set. !ny variable variable that has a limited limited number of distinct distinct values and which cannot be divided into fractions, is a discontinuous variable. Such a variable is also called as cate$orical cate$orical variable or classifica classificator% tor% variable, variable, or discret discretee variable variable . Some variables have only two values,

reflecting the presence or absence of a property. "xample: employed#unemployed or male#female have two values These variables are referred to as dichotoous. There are others that can tae added categories such as the demographic variables of race, religion. !n automotive variable, for example, where $%hevrolet$ is assigned a & and $'onda$ is assigned a (, provides no option for a &.&)i.e. the values cannot be divided into fractions*. Continuous and Discontinuous Variables (continuation fro other source)

Cate$orical variables are also nown as discrete or &ualitative variables.

%ategorical variables can be further categori+ed as either: nominal, ordinal or dichotomous. 'oinal variables are variables that have two or more categories, but which do not

have an intrinsic order. or 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 - categories called houses, condos, co#ops and bungalows. f note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. !nother example of a nominal variable would be classifying where people live in the /S! by state. In this case there will be many more levels of the nominal variable )&0 in fact*. Dichotoous variables are nominal variables which have only two categories or 

levels. or example, if we were looing at gender, we would most probably categori+e somebody as either $male$ or $female$. This is an example of a dichotomous variable )and also a nominal variable*. !nother example might be if we ased a person if they owned a mobile phone. 'ere, we may categorise mobile phone ownership as either $1es$ or $2o$. 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 3ust lie nominal

variables only the categories can also be ordered or raned. So if you ased someone if they lied the policies of the 4emocratic 5arty and they could answer either $2ot very much,6 $They are 7$ or $1es, a lot$ then you have an ordinal variable. 8hy9 ecause you have  categories, namely $2ot very much,6 $They are 7$ and $1es, a lot$ and you can ran them from the most positive )1es, a lot*, to the middle response )They are 7*, to the least positive

)2ot very much*. 'owever, whilst we can ran the levels, we cannot place a $value$ to them; we cannot say that $They are 7$ is twice as positive as $2ot very much$ for example. Others includes Du% Variables fro uantitative Variables

=* described that a &uantitative variable can be transfored into a cate$orical variable , called a dummy variable by recoding the values.

%onsider the following example: the ?uantitative variable !ge can be classified into five intervals. The values of the associated categorical variable, called dummy variables, are >, =,,-,&:

• • • • •

@/p to =&A @=&, -0 A @-0, &0A @&0, (0A @!bove (0A

> =  &

Preference Variables

 =* described that preference variables are specific discrete variables, whose values are either in a decreasin$ or increasin$ order . or example, in a survey, a respondent may be ased to indicate the importance of the following nine sources of  information in his research and development wor, by using the code @>A for the most important source and @BA for the least important source:

• • • • • • •

Citerature published in the country Citerature published abroad Scientific abstracts /npublished reports, material, etc. 4iscussions with colleagues within the research unit 4iscussions with colleagues outside the research unit but within institution 4iscussions with colleagues outside the institution

• •

Scientific meetings in the country Scientific meetings abroad

 2ote 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. Multi#le Res#onse Variables

=* described that multiple response variables are those, which can assue ore than one value . ! typical example is a survey ?uestionnaire about the use of computers in research. The respondents were ased to indicate the purpose)s* for  which they use computers in their research wor. The respondents could score ore than one cate$or% .

• • • • • • • •

Statistical analysis Cab automationD process control 4ata base management, storage and retrieval Eodeling and simulation Scientific and engineering calculations %omputer aided design )%!4* %ommunication and networing Fraphics

Continuous variables are also nown as &uantitative variables. %ontinuous

variables can be further categori+ed as either interval or ratio variables. *nterval 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 %elsius or ahrenheit*. So the difference between =0% and 0% is the same as 0% to -0%. 'owever, temperature measured in degrees %elsius or ahrenheit is  2T a ratio variable.

Ratio variables are interval variables, but with the added condition that 0 )+ero* of 

the measurement indicates that there is none of that variable. So, temperature measured in degrees %elsius or ahrenheit is not a ratio variable because 0% does not mean there is no temperature. 'owever, temperature measured in 7elvin is a ratio variable as 0 7elvin )often called absolute +ero* indicates that there is no temperature whatsoever. ther 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 & 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 ran order of the observations. or example, if a scale is transformed by an exponential, logarithmic or any other nonlinear monotonic transformation, it loses its interval # scale property. 'ere, it would  be expedient to replace the observations by their rans )=*. De#endent and *nde#endent Variables (“Research Methods,” n.d.)

Gesearchers 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 soethin$ else , i s the inde#endent variable. +his is also called e"#lanator% variable (“Variable +%#es,” -). The variable that is the effect or is the result or outcoe of another variable is the de#endent variable  )also referred to as outcome variable

or effect variable*. +his is also called res#onse variable (“Variable +%#es,” -). 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 ?uestions help to identify the independent variable. /irst, does it come before other variable in time9 0econd, if the variables occur at the same time, does the researcher suggest that one

variable has an impact on another variable9 Independent variables affect or have an impact on other variables. 8hen independent variable is present, the dependent variable is also present, and with each unit of i ncrease in the inde#endent variable , there is an increase or decrease in the de#endent variable also .

In other words, the variance in dependent varia ble is accounted for by the independent variable. 4ependent variable is also referred to as criterion variable. In the research vocabulary different labels have been associated with the independent and dependent variables lie: *nde#endent variable

De#endent variable

5resumed cause

presumed effect

Stimulus

Gesponse

5redicted from ...

5redicted to ...

!ntecedent

%onse?uence

Eanipulated

Eeasured outcome

5redictor

%riterion

!"a#le

“Types of Variable” (n.d.) give example as such  Imagine that a tutor asks !!  students to complete a maths test. The tutor "ants to kno" "hy some students perform better  than others. #hilst the tutor does not kno" the ans"er to this$ she thinks that it might be because of t"o reasons% () some students spend more time revising for their test& and (')  some students are naturally more intelligent than others. s such$ the tutor decides to

investigate the effect of revision time and intelligence on the test performance of the !!  students. The dependent and independent variables for the study are% De#endent Variable Test Ear )measured from 0 to >00* *nde#endent Variables  Gevision time )measured in hours* Intelligence )measured using IH

score* Moderatin$ Variable (“Research Methods,” n.d.)

! moderating variable is one that has a strong contin$ent effect on the inde#endent variable1de#endent variable relationshi# . That is, the presence of a third variable )the

moderating variable* modifies the original relationship between the independent and the dependent variable. !"a#le

  strong relationship has been observed bet"een the uality of library facilities (*) and the performance of the students (+). !lthough 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  and 1 variables. *ntervenin$ Variable (“Research Methods,” n.d.)

! basic causal relationship re?uires only independent and dependent variable. ! third type of variable, the intervening variable, appears in more complex causal relationships. It comes between the independent and dependent variables and sho2s the link or echanis bet2een the . !dvances in nowledge 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 de#endent variable 2ith res#ect to inde#endent variable and acts as an inde#endent variable to2ard the de#endent variable. !"a#le

  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). ,ence a ma-or cause of one type of suicide "as 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)$ "hich affects suicide (dependent variable). /pecifying the chain of causality makes the linkages in theory clearer and helps a researcher  test complex relationships.

!"traneous Variables (“Research Methods,” n.d.)

!n almost infinite number of extraneous variables )"V* 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 e"traneous variables a% have to be controlled . This is necessary if we are conducting an experiment where the effect of the confounding factors has to be controlled. Confoundin$ factors  is another name used for extraneous variables. Other e"#lanator% ters (“Variables in research,” -3) 4ttributes are sub#values of a variable, such as JmaleJ and JfemaleJ. !n exhaustive list

contains all possible answers, for example gender could also include Jmale transgenderJ and Jfemale transgenderJ )and both can be pre# or post#operative*.

Mutuall% e"clusive attributes are those that cannot occur at the same time. Thus in a

survey a person may be re?uested to select one answer from a list of alternatives )as opposed to selecting as many that might apply*. 5nits are the ways that variables are classified. These include: individuals, groups,

social interactions and ob3ects.

Part +2o Definin$ +ers 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 )=*. 'ere are a few reasons why it is important to define , =0>&, from http:DDwww.artofcraftsmanship.comDhow#to#define#terms#in#a#research#paper#an# academic#manual

%hapter L: 4efining Terms. )=0>*. Getrieved http:DDstevevincent.infoDITS>>NL.htm

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