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Question1. Compare and Contrast between Qualitative and Quantitative research.

Answer :

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Question2: Discuss Experimental Research design and its various types with relevant example.

Answer : Experimental Research

The purpose of experimental research is to study cause and effect relationships. Its defining characteristic is

active manipulation of an independent variable (i.e., it is only in experimental research that manipulation is

present). Also, random assignment (which creates "equivalent" groups) is used in the strongest experimentalresearch designs

Within the realm of experimental research, there are three major types of design:

1. TRUE-EXPERIMENTAL2. QUASI-EXPERIMENTAL3. PRE-EXPERIMENTAL

TRUE-EXPERIMENTAL DESIGNSmust employ the following:

Random selection of subjects

Use of control groups

Random assignments to control and experimental groups

Random assignment of groups to control and experimental conditions

In order for an experiment to follow a true-experimental design, it must meet the preceding criteria.

There is some variation in true-experimental designs, but that variation comes in the time(s) that the

treatment is given to the experimental group, or in the observation or measurement (pre-test, post-

test, mid-test) area.

Advantages of the true-experimental design include:

Greater internal validity

Causal claims can be investigated

Disadvantages:

Less external validity (not like real world conditions)

Not very practical

QUASI-EXPERIMENTAL DESIGNSare usually constructions that already exist in the realworld. Those designs that fall into the quasi-experimental category fall short in some way of the

criteria for the true experimental group. A quasi-experimental design will have some sort of control

and experimental group, but these groups probably weren't randomly selected. Random selection is

usally where true-experimental and quasi-experimental designs differ.

Quasi-experimental designs came about because of:

1) difficulty of applying the classical natural science method to the social sciences

2) overemphasis on theory testing and development

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3) high cost of classic natural science methods

4) development of new statistical tools that allowed for statistical control

There are several types of quasi-experimental designs, including:

time series design

equivalent time series samples

equivalent samples materials design

non-equivalent control group counterbalanced designs

Some advantages of the quasi-experimental design include:

Greater external validity (more like real world conditions)

Much more feasible given time and logistical constraints

Disadvantages:

Not as many variables controlled (less causal claims)

PRE-EXPERIMENTAL DESIGNSare lacking in several areas of the true-experimental criteria.

Not only do they lack random selection in most cases, but they usually just employ a single group.

This group receives the "treatment," there is no control group. Pilot studies, one-shot case studies,

and most research using only one group, fall into this category.

The advantages are:

Very practical

Set the stage for further research

Disadvantages:

Lower validity

Question3 : what are the various levels of measurement. Discuss using relevant example.

Answer : Instatisticsandquantitative researchmethodology, levels of measurementor scales of measureare types of data thatarise in the theory of scale typesdeveloped by the psychologistStanley Smith Stevens.The types arenominal,ordinal,interval,andratio

1. Nominal scale

The nominal type, sometimes also called the qualitative type, differentiates between items or subjects based only on their na mes or

(meta-)categories and other qualitative classifications they belong to. Examples include gender, nationality, ethnicity, language, genre,

style, biological species, and form.

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Central tendency

Themode,i.e. the most commonitem, is allowed as the measure ofcentral tendencyfor the nominal type. On the other hand,

themedian,i.e. the middle-rankeditem, makes no sense for the nominal type of data since ranking is not allowed for the nominal type.

2. Ordinal scale

The ordinal type allows forrank order(1st, 2nd, 3rd, etc.) by which data can be sorted, but still does not allow for relative degree of

differencebetween them. Examples include, on one hand,dichotomousdata with dichotomous (or dichotomized) values such as 'sick'

vs. 'healthy' when measuring health, 'guilty' vs. 'innocent' when making judgments in courts, 'wrong/false' vs. 'right/true' when

measuringtruth value,and, on the other hand, non-dichotomousdata consisting of a spectrum of values, such as 'completely agree',

'mostly agree', 'mostly disagree', 'completely disagree' when measuringopinion.

Central tendency

Themedian,i.e. middle-ranked, item is allowed as the measure ofcentral tendency;however, the mean (or average) as the measure

ofcentral tendencyis not allowed. Themodeis allowed.

In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales;

thus means and standard deviations have novalidity,but they can be used to get ideas for how to improveoperationalizationof

variables used inquestionnaires.Most psychological data collected bypsychometricinstruments and tests, measuringcognitiveand

other abilities, are ordinal, although some theoreticians have argued they can be treated as interval or ratio scales. However, there is

littleprima facieevidence to suggest that such attributes are anything more than ordinal (Cliff, 1996; Cliff & Keats, 2003; Michell,

2008).[2]

In particular,[3]

IQ scores reflect an ordinal scale, in which all scores are meaningful for comparison only.[4][5][6]

There is noabsolute zero, and a 10-point difference may carry different meanings at different points of the scale.

[7][8]

3. Interval scale

The interval type allows for the degree of differencebetween items, but not the ratio between them. Examples include temperaturewith

theCelsius scale,which has an arbitrarily-defined zero point (the freezing point of a particular substance under particular conditions),

and datewhen measured from an arbitrary epoch (such as AD). Ratios are not allowed since 20C cannot be said to be "twice as hot"

as 10C, nor can multiplication/division be carried out between any two dates directly. However, ratios of differencescan be expressed;

for example, one difference can be twice another. Interval type variables are sometimes also called "scaled variables", but the formal

mathematical term is anaffine space(in this case anaffine line).

Central tendency and statistical dispersion

Themode,median,andarithmetic meanare allowed to measure central tendency of interval variables, while measures of statistical

dispersion includerangeandstandard deviation.Since one cannot divide, one cannot define measures that require a ratio, such as the

studentized range or the coefficient of variation. More subtly, while one can define moments about the origin, only central moments are

meaningful, since the choice of origin is arbitrary. One can define standardized moments, since ratios of differences are meaningful, but

one cannot define the coefficient of variation, since the mean is a moment about the origin, unlike the standard deviation, which is (the

square root of) a central moment.

4. Ratio scale

The ratio type takes its name from the fact that measurement is the estimation of the ratio between a magnitude of a continuous

quantity and a unit magnitude of the same kind (Michell, 1997, 1999). A ratio scale possesses a meaningful (unique and non-arbitrary)

zero value. Most measurement in the physical sciences and engineering is done on ratio scales. Examples

includemass,length,duration,plane angle,energyandelectric charge.Ratios are allowed because having a non-arbitrary zero point

makes it meaningful to say, for example, that one object has "twice the length" of another. Very informally, many ratio scales can be

described as specifying "how much" of something (i.e. an amount or magnitude) or "how many" (a count). TheKelvintemperature scale

is a ratio scale because it has a unique, non-arbitrary zero point calledabsolute zero.The zero point of the Celsius scale is at 273.15

kelvins, so Celsius is not a ratio scale.

Central tendency and statistical dispersion

Thegeometric meanand theharmonic meanare allowed to measure the central tendency, in addition to the mode, median, and

arithmetic mean. Thestudentized rangeand thecoefficient of variationare allowed to measure statistical dispersion. All statistical

measures are allowed because all necessary mathematical operations are defined for the ratio scale.

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