Research meth

FACULTY OF ECONOMICS AND MANAGEMENT
DEPARTMENT OF MANAGEMENT
MASTER OF SCIENCE IN ICT POLICY AND
REGULATION
(MSCICTPR)

Prepared and submitted by BWANAKWELI Chantal

RESEARCH METHODOLOGY ASSIGNMENT

Table of Contents
Question 1- Answer ................................................................................................................................. 3
Question 2- Answer ................................................................................................................................. 7
Question 3- Answer ............................................................................................................................... 19
Question 4- Answer ............................................................................................................................... 22
Question 5- Answer ............................................................................................................................... 24

- Assignment: 2012 by BWANAKWELI Chantal Page 2

Question 1- Answer
What is the purpose of research? Outline the types of research

WHAT IS RESEARCH?

"Research is a process of steps used to collect and analyze information to increase our
understanding of a topic or issue". It consists of three steps: Pose a question, collect data
to answer the question, and present an answer to the question. (By Creswell, J. W.
(2008))

Research and experimental development is formal work undertaken systematically to
increase the stock of knowledge, including knowledge of humanity, culture and society,
and the use of this stock of knowledge to devise new applications.

Research is finding out what you don't already know. No one knows everything, but
everybody knows something. However, to complicate matters, often what you know, or
think you know, is incorrect.( http://public.wsu.edu/~taflinge/research.html)

There are two basic purposes for research: to learn something, or to gather evidence. The
first, to learn something, is for your own benefit. It is almost impossible for a human to
stop learning. It may be the theory of relativity or the RBIs of your favorite ball player,
but you continue to learn. Research is organized learning, looking for specific things to
add to your store of knowledge.
What you've learned is the source of the background information you use to communicate
with others. In any conversation you talk about the things you know, the things you've
learned. If you know nothing about the subject under discussion, you can neither
contribute nor understand it. (This fact does not, however, stop many people from joining
in on conversations, anyway.) When you write or speak formally, you share what you've
learned with others, backed with evidence to show that what you've learned is correct. If,


however, you haven't learned more than your audience already knows, there is nothing
for you to share. Thus you do research.

The purpose and role of Research

Research can be conceptualized as exhibiting one or more of the following four purposes:

1. Exploratory: e.g., discovering, uncovering, exploring
2. Descriptive: e.g., summarizing, gathering info, mapping
3. Explanatory: e.g., testing and understanding causal relations
4. Predictive: e.g., predict what might happen in various scenarios

Briefly the main purpose and role of research is to help plan and gather information on a
certain topic before carrying it out .It helps to test and create a theory on a certain thing
and with the information given this helps to gather to generate a topic to find out more
on. By carrying out research this helps to gather and explore more into a certain topic
which helps to backup your opinions with the findings.

By researching you are able to backup and give others views and opinions in order to
help to justify your findings.

Research also helps to monitor something before carrying it out example an activity in a
childcare setting research helps to identify how the activity can help children ,what use
the activity will come to how the activity may have an effect on others and this helps you
to investigate more before carrying out something.

Research also helps to discover new things by gathering and looking out for what others
around have done this can helps in childcare setting as it helps to learn from others and
allows developing on your learning.

Research helps to test a hypothesis or theory by looking up on what others may say and
statistic that are given can strengthen and weaken your hypothesis by the information that
your may have gathered.


Research helps people finding result. It illuminates people: They see what have been
hidden or what has been missed.

Types of research

There are three types of research, pure, original, and secondary. Each type has the goal of
finding information and/or understanding something. The difference comes in the
strategy employed in achieving the objective.

1. Pure Research

Pure research is research done simply to find out something by examining anything. For
instance, in some pure scientific research scientists discover what properties various
materials possess. It is not for the sake of applying those properties to anything in
particular, but simply to find out what properties there are. Pure mathematics is for the
sake of seeing what happens, not to solve a problem.

The fun of pure research is that you are not looking for anything in particular. Instead,
anything and everything you find may be joined with anything else just to see where that
combination would lead, if anywhere.

2. Original Research

Original or primary research is looking for information that nobody else has found.
Observing people's response to advertising, how prison sentences influence crime rates,
doing tests, observations, experiments, etc., are to discover something new.


Original research requires two things: 1) knowing what has already been discovered,
having a background on the subject; and 2) formulating a method to find out what you
want to know. To accomplish the first you indulge in secondary research.

For the second, you decide how best to find the information you need to arrive at a
conclusion. This method may be using focus groups, interviews, observations,
expeditions, experiments, surveys, etc.

3. Secondary Research

Secondary research is finding out what others have discovered through original research
and trying to reconcile conflicting viewpoints or conclusions, find new relationships
between normally non-related researches, and arrive at your own conclusion based on
others' work. This is, of course, the usual course for college students.

Secondary research should not be belittled simply because it is not original research.
Fresh insights and viewpoints, based on a wide variety of facts gleaned from original
research in many areas, has often been a source of new ideas. Even more, it has provided
a clearer understanding of what the evidence means without the influence of the original
researcher's prejudices and preconceptions.


Question 2- Answer
Write comprehensive notes to show understanding on the following

a) Primary data
Primary data is the specific information collected by the person who is doing the
research. It can be obtained through clinical trials, case studies, true experiments and
randomized controlled studies. This information can be analyzed by other experts who
may decide to test the validity of the data by repeating the same experiments.
Primary data is important for all areas of research because it is unvarnished information
about the results of an experiment or observation. It is like the eyewitness testimony at a
trial. No one has tarnished it or spun it by adding their own opinion or bias so it can form
the basis of objective conclusions.
Primary data is data gathered for the first time by the researcher. Primary data is a direct
report from someone who was actively involved in whatever it is you are discussing. The
merit of primary data is that it is direct information, uncontaminated by being transmitted
through another source. The demerits of primary data are that sometimes the person who
is on the field sees only part of the action.

Using primary data

An advantage of using primary data is that researchers are collecting information for the
specific purposes of their study. In essence, the questions the researchers ask are tailored
to elicit the data that will help them with their study. Researchers collect the data
themselves, using surveys, interviews and direct observations

For example in a recent Institute study, researchers wanted to find out about workers’
experiences in return to work after a work-related injury. Part of the research involved
interviewing workers by telephone and asking them questions about how long they were
off work and about their experiences with the return-to-work process.

The workers’ answers are considered primary data. From this, the researchers got
answers to specific information about the return-to-work process including the rates of
work accommodation offers, and why some workers refused such an offer.

Advantage and disadvantage of using Primary data is that Primary data offers tailored
information but tends to be expensive to conduct and takes a long time to process.


b) Secondary data
Secondary data is data taken by the researcher from secondary sources, internal or
external. Secondary data is of two kinds, internal and external. Secondary data – whether
internal or external – is data already collected by others, for purposes other than the
solution of the problem on hand. The merit of secondary data is that it can be gathered
from a number of primary sources and weighed together to put together an overall
assessment of what has happened.
In research, Secondary data is collecting and possibly processing data by people other
than the researcher in question. Common sources of secondary data for social science
include censuses, large surveys, and organizational records.
Advantages to the secondary data collection method are:
1) It saves time that would otherwise be spent collecting data,
2) Provides a larger database (usually) than what would be possible to collect on ones
own However there are disadvantages to the fact that the researcher cannot personally
check the data so it's reliability may be questioned.

Using secondary data

There are several types of secondary data. They can include information from the Census,
a company’s health and safety records such as their injury rates, or other government
statistical information such as the number of workers in different sectors

Secondary data tends to be readily available and inexpensive to obtain. In addition,
secondary data can be examined over a longer period of time. For example, you can look
at a company’s lost-time rates over several years to see at trends.
Advantage and disadvantage of using Secondary data is that Secondary data is usually
inexpensive to obtain and can be analyzed in less time. However, because it was gathered
for other purposes, you may need to tease out the information to find what you’re looking
for.
c) Random sampling
What Is a Random Sample?
A random sample is a subset of individuals that are randomly selected from a population.
Because researchers usually cannot obtain data from every single person in a group, a
smaller portion is randomly selected to represent the entire group as a whole. The goal is
to obtain a sample that is representative of the larger population.


In statistics, a sample is a subject chosen from a population for investigation; a random
sample is one chosen by a method involving an unpredictable component.

Random sampling can also refer to taking a number of independent observations from
the same probability distribution, without involving any real population. The sample
usually is not a representative of the population of people from which it was drawn— this
random variation in the results is termed as sampling error. In the case of random
samples, mathematical theory is available to assess the sampling error. Thus, estimates
obtained from random samples can be accompanied by measures of the uncertainty
associated with the estimate. This can take the form of a standard error, or if the sample is
large enough for the central limit theorem to take effect, confidence intervals may be
calculated. (http://en.wikipedia.org/wiki/Random_sample)

Random sampling is one of the most popular types of random or probability sampling.

In this technique, each member of the population has an equal chance of being selected as
subject. The entire process of sampling is done in a single step with each subject selected
independently of the other members of the population. (Random Sampling - Probability
Sampling. )

There are many methods to proceed with simple random sampling. The most primitive
and mechanical would be the lottery method. Each member of the population is assigned
a unique number. Each number is placed in a bowl or a hat and mixed thoroughly. The
blind-folded researcher then picks numbered tags from the hat. All the individuals
bearing the numbers picked by the researcher are the subjects for the study. Another way
would be to let a computer do a random selection from your population. For populations
with a small number of members, it is advisable to use the first method but if the
population has many members, a computer-aided random selection is preferred.

Advantages of Simple Random Sampling

One of the best things about simple random sampling is the ease of assembling the
sample. It is also considered as a fair way of selecting a sample from a given population
since every member is given equal opportunities of being selected.

Another key feature of simple random sampling is its representativeness of the
population. Theoretically, the only thing that can compromise its representativeness is


luck. If the sample is not representative of the population, the random variation is called
sampling error.

An unbiased random selection and a representative sample is important in drawing
conclusions from the results of a study. Remember that one of the goals of research is to
be able to make conclusions pertaining to the population from the results obtained from a
sample. Due to the representativeness of a sample obtained by simple random sampling,
it is reasonable to make generalizations from the results of the sample back to the
population.

Disadvantages of Simple Random Sampling

One of the most obvious limitations of simple random sampling method is its need of a
complete list of all the members of the population. Please keep in mind that the list of the
population must be complete and up-to-date. This list is usually not available for large
populations. In cases as such, it is wiser to use other sampling techniques.

d) Systematic sampling
System Sampling is a method of selecting sample members from a larger population
according to a random starting point and a fixed, periodic interval. Typically, every "nth"
member is selected from the total population for inclusion in the sample population.
Systematic sampling is still thought of as being random, as long as the periodic interval is
determined beforehand and the starting point is random.
( http://www.investopedia.com/terms/s/systematic-sampling.asp#ixzz2CwGnZAFp)

Systematic sampling is a statistical method involving the selection of elements from an
ordered sampling frame.

Systematic sampling is to be applied only if the given population is logically
homogeneous, because systematic sample units are uniformly distributed over the
population. The researcher must ensure that the chosen sampling interval does not hide a
pattern. Any pattern would threaten randomness.

Example: Suppose a supermarket wants to study buying habits of their customers, then
using systematic sampling they can choose every 10th or 15th customer entering the
supermarket and conduct the study on this sample.

A common way of selecting members for a sample population using systematic sampling
is simply to divide the total number of units in the general population by the desired


number of units for the sample population. The result of the division serves as the marker
for selecting sample units from within the general population.

For example, if you wanted to select a random group of 1,000 people from a population
of 50,000 using systematic sampling, you would simply select every 50th person, since
50,000/1,000 = 50.

In systematic random sampling, the researcher first randomly picks the first item or
subject from the population. Then, the researcher will select each n'th subject from the
list.

The procedure involved in systematic random sampling is very easy and can be done
manually. The results are representative of the population unless certain characteristics of
the population are repeated for every n'th individual, which is highly unlikely.

Advantages of Systematic Sampling

 The main advantage of using systematic sampling over simple random sampling is
its simplicity. It allows the researcher to add a degree of system or process into the
random selection of subjects.
 Another advantage of systematic random sampling over simple random sampling
is the assurance that the population will be evenly sampled. There exists a chance
in simple random sampling that allows a clustered selection of subjects. This is
systematically eliminated in systematic sampling.

Disadvantage of Systematic Sampling

 The process of selection can interact with a hidden periodic trait within the
population. If the sampling technique coincides with the periodicity of the trait, the
sampling technique will no longer be random and representativeness of the sample
is compromised.

e) Stratified sampling

"Stratified sampling" is a way of getting an 'average' which represents the entire universe,
or everything that exists that somebody wants to count or measure. The entire universe is
broken down into groups that don’t overlap and a 'sample' is taken from each group.

A stratified sample is a probability sampling technique in which the researcher divides
the entire target population into different subgroups, or strata, and then randomly selects


the final subjects proportionally from the different strata. This type of sampling is used
when the researcher wants to highlight specific subgroups within the population.

For example, to obtain a stratified sample of university students, the researcher would
first organize the population by college class and then select appropriate numbers of
freshmen, sophomores, juniors, and seniors. This ensures that the researcher has adequate
amounts of subjects from each class in the final sample.

It is important to note that the strata used in stratified sampling must not overlap. Having
overlapping subgroups will give some individuals a higher chance of being selected as
subjects in the sample. If this happened, it would not be a probability sample.

Some of the most common strata used in stratified random sampling are age, gender,
religion, educational attainment, socioeconomic status, and nationality.

When to Use Stratified Sampling

There are many situations in which researchers would choose stratified random sampling
over other types of sampling. First, it is used when the researcher wants to highlight a
specific subgroup within the population. Stratified sampling is good for this because it
ensures the presence of key subgroups within the sample.

Researchers also use stratified random sampling when they want to observe relationships
between two or more subgroups. With this type of sampling, the researcher is guaranteed
subjects from each subgroup are included in the final sample,

Advantages of Stratified Sampling

Using a stratified sample will always achieve greater precision than a simple random
sample, provided that the strata have been chosen so that members of the same stratum
are as similar as possible in terms of the characteristic of interest. Administratively, it is
often more convenient to stratify a sample than to select a simple random sample.

Another advantage that stratified random sampling has is that is guarantees better
coverage of the population. The researcher has control over the subgroups that are
included in the sample,

Disadvantages
Stratified sampling is not useful when the population cannot be exhaustively partitioned
into disjoint subgroups. It would be a misapplication of the technique to make subgroups'


sample sizes proportional to the amount of data available from the subgroups, rather than
scaling sample sizes to subgroup sizes
Again it Stratified sampling can be difficult to identify appropriate strata for a study. A
last disadvantage is that it is more complex to organize and analyze the results compared
to simple random sampling

f) Multistage sampling
Multistage Sampling: Multistage Sampling is a sampling strategy (e.g., gathering
participants for a study) used when conducting studies involving a very large population.
The entire population is divided into naturally-occurring clusters and sub-clusters, from
which the researcher randomly selects the sample.

For example, you want to conduct a survey of salespeople for a nationwide retail chain
with stores all over the country. You could randomly select states, randomly select
counties in each state, randomly select stores in each county, and randomly select
salespeople in those stores

(http://www.alleydog.com/glossary/definition.php?term=Multistage%20Sampling#ixzz2CwN8SuOO)

A multi-stage sample is one in which sampling is done sequentially across two or more
hierarchical levels, such as first at the county level, second at the census track level, third
at the block level, fourth at the household level, and ultimately at the within-household
level. Many probability sampling methods can be classified as single-stage sampling
versus multi-stage sampling. Single-stage samples include simple random sampling,
systematic random sampling, and stratified random sampling. In single-stage samples, the
elements in the target population are assembled into a sampling frame; one of these
techniques is used to directly select a sample of elements In contrast, in multi-stage
sampling, the sample is selected in stages, often taking into account the hierarchical
(nested) structure of the population. The target population of elements is divided into
first-stage units, often referred to as primary sampling units which are the ones sampled
first. The selected first-stage secondary...

Multistage sampling is a complex form of cluster sampling.

Advantages

 cost and speed that the survey can be done in
 convenience of finding the survey sample
 normally more accurate than cluster sampling for the same size sample

Disadvantages

 Is not as accurate as SRS if the sample is the same size
 More testing is difficult to do

Using all the sample elements in all the selected clusters may be prohibitively expensive
or not necessary. Under these circumstances, multistage cluster sampling becomes useful.
Instead of using all the elements contained in the selected clusters, the researcher
randomly selects elements from each cluster. Constructing the clusters is the first stage.
Deciding what elements within the cluster to use is the second stage. The technique is
used frequently when a complete list of all members of the population does not exist and
is inappropriate.

g) Independent variable

The independent variable is the characteristic of a psychology experiment that is
manipulated or changed.
For example, in an experiment looking at the effects of studying on test scores, studying
would be the independent variable. Researchers are trying to determine if changes to the
independent variable result in significant changes to the dependent variable (the test
results)
An independent variable is a factor that can be varied or manipulated in an experiment
(e.g. time, temperature, concentration, etc). It is usually what will affect the dependent
variable.

There are two types of independent variables, which are often treated differently in
statistical analyses:

 quantitative variables that differ in amounts or scale and can be ordered (e.g.
weight, temperature, time).
 qualitative variables which differ in "types" and can not be ordered (e.g. gender,
species, method). By convention when graphing data, the independent variable
is plotted along the horizontal X-axis with the dependent variable on the vertical
Y-axis.

h) Dependent variable


A dependent variable is also known as a "response variable", "regressand", "measured
variable", "observed variable", "responding variable", "explained variable", "outcome
variable", "experimental variable", and "output variable. (By Dodge, Y. (2003) The
Oxford Dictionary of Statistical Terms, OUP. ISBN)

The dependent variable is the variable that is simply measured by the researcher. It is the
variable that reflects the influence of the independent variable. For example, the
dependent variable would be the variable that is influenced by being randomly assigned
to either an experimental condition or a control condition.

A dependent Variable is a factor or phenomenon that is changed by the effect of an
associated factor or phenomenon called the independent variable.

For example, consumption is a dependent variable because it is caused and influenced by
another variable: income. In a mathematical equation or model, the dependent variable is
the variable whose value is to be determined by that equation or model. In an experiment,
it is the variable whose behavior under controlled conditions (that are allowed to change
in an organized manner) is studied.(
http://www.businessdictionary.com/definition/dependent-
variable.html#ixzz2CwoqMYEg)

The dependent variable is the variable that is being measured in an experiment. For
example, in a study on the effects of tutoring on test scores, the dependent variable
would be the participants test scores.

In a psychology experiment, researchers are looking at how changes in the
independent variable cause changes in the dependent variable.

Examples of Dependent Variables

 Researchers want to discover if listening to classical music helps students earn
better grades on a math exam. In this example, the scores on the math exams are
the dependent variable.
 Researchers are interested in seeing how long it takes people to respond to
different sounds. In this example, the length of time it takes participants to respond
to a sound is the dependent variable.
 Researchers want to know whether first-born children learn to speak at a younger
age than second-born children. In this example, the dependent variable is the age
at which the child learns to speak.

i) Hypothesis testing


A statistical hypothesis is an assumption about a population parameter. This
assumption may or may not be true. Hypothesis testing refers to the formal procedures
used by statisticians to accept or reject statistical hypotheses.

A process by which an analyst tests a statistical hypothesis. The methodology employed
by the analyst depends on the nature of the data used, and the goals of the analysis.
The goal is to either accept or reject the null hypothesis.
( http://www.investopedia.com/terms/h/hypothesistesting.asp#ixzz2Cwr2gOcF)

Hypothesis testing is a common practice in science that involves conducting tests and
experiments to see if a proposed explanation for an observed phenomenon works in
practice. A hypothesis is a tentative explanation for some kind of observed phenomenon,
and is an important part of the scientific method.

Any tentative explanation can be referred to as a hypothesis if it can be submitted to
hypothesis testing. There are, however, a set of guidelines for an explanation to be
considered a true scientific hypothesis. The first major point is testability; a scientific
hypothesis must be able to proceed to the stage of hypothesis testing to be considered a
scientifically legitimate hypothesis. It is generally suggested that a hypothesis be
relatively simple, though this is not always possible. Hypotheses must also be able to
explain the phenomena under any set of conditions; if a hypothesis can only explain a
phenomenon in one set of conditions, it is generally considered unacceptable.

Hypotheses are generally considered useful only if they are likely to improve on the
current body of knowledge on a subject and pave the way for greater knowledge to be
acquired in the future. Also, a hypothesis is generally not acknowledged if it defies other
commonly recognized knowledge. If a hypothesis meets all of these requirements, it will
typically proceed to the hypothesis testing phase.

In hypothesis testing, the testers seek to discover evidence that either validates or
disproves a given hypothesis. Usually, this involves a series of experiments being
conducted in many different conditions. If the hypothesis does not stand up to the tests in
all conditions, something is usually wrong with the hypothesis and a new one must be
formed to take the new information into account. The new hypothesis is submitted to the
same hypothesis testing. If it passes and is not proven wrong, it can eventually be
considered a scientific theory or law, though nothing in science can be proven to be
absolutely true.

One common method of hypothesis testing is known as statistical hypothesis testing, and
typically deals with large quantities of data. Experiments and tests are conducted and the

data is collected. If the data collected shows that it is unlikely that the results occurred by
chance, it is considered statistically significant and can be used to support a hypothesis.

Hypothesis testing is the use of statistics to determine the probability that a given
hypothesis is true. The usual process of hypothesis testing consists of four steps.

1. Formulate the null hypothesis (commonly, that the observations are the result of
pure chance) and the alternative hypothesis (commonly, that the observations show a
real effect combined with a component of chance variation).

2. Identify a test statistic that can be used to assess the truth of the null hypothesis.

3. Compute the P-value, which is the probability that a test statistic at least as significant
as the one observed would be obtained assuming that the null hypothesis were true. The
smaller the -value, the stronger the evidence against the null hypothesis.

4. Compare the -value to an acceptable significance value (sometimes called an alpha
value). If , that the observed effect is statistically significant, the null hypothesis is
ruled out, and the alternative hypothesis is valid.

j) Cause - effect relations

Cause-effect relation is a relation between cause-concept and effect-concept.

Cause-effect relation is represented in the main memory by cause-effect relation table.

Example:

“Sun” is a cause for “heat”.

“Fire” is a cause for “heat”.

“Sun” is a cause for “sunburn”.

So, there are 3 cause-effect relations in this example:

{Sun->heat}

{Fire->heat}

{Sun->sunburn}

Why are cause-effect relations so important?

Cause-effect relations are so important because:

1) Cause-effect relations help to understand what would happen as a result of current
situation. Cause effect relations help to predict the future of current context.

In order to find out what would happen, strong AI should just find all effect concepts for
specified concepts.

2) Cause-effect relations help to understand what strong AI can do in order to achieve
some goals.

In order to figure out what to do, strong AI should just find cause concepts for the
specified goal-concepts (sub goals).

Example (based on diagram above):

1) Let imagine that strong AI wants to find out what would be the result of the sun. In
order to figure that out, strong AI would take a look into cause-effect relations and find
out that probable results are “Heat” and “SunBurn”.

2) Let’s imagine that current goal of strong AI is “Heat”. In order to achieve this goal
strong AI should follow cause-effect relation in reverse direction and find out that “Fire”
and “Sun” concepts could help to achieve the current goal “Heat”.


Question 3- Answer
Discuss the major types of data collection

Data collection is any process of preparing and collecting data, for example, as part of a
process improvement or similar project. The purpose of data collection is to obtain
information to keep on record, to make decisions about important issues, or to pass
information on to others. Data are primarily collected to provide information regarding a
specific topic

Data Collection is an important aspect of any type of research study. Inaccurate data
collection can impact the results of a study and ultimately lead to invalid results.

Data collection methods for impact evaluation vary along a continuum. At the one end of
this continuum are quantatative methods and at the other end of the continuum are
Qualitative methods for data collection
(http://www.worldbank.org/poverty/impact/methods/datacoll.htm )

Quantitative and Qualitative Data collection methods

The Quantitative data collection methods, rely on random sampling and structured data
collection instruments that fit diverse experiences into predetermined response categories.
They produce results that are easy to summarize, compare, and generalize.

Quantitative research is concerned with testing hypotheses derived from theory and/or
being able to estimate the size of a phenomenon of interest. Depending on the research
question, participants may be randomly assigned to different treatments. If this is not
feasible, the researcher may collect data on participant and situational characteristics in
order to statistically control for their influence on the dependent, or outcome, variable. If
the intent is to generalize from the research participants to a larger population, the
researcher will employ probability sampling to select participants.

Typical quantitative data gathering strategies include:

 Experiments/clinical trials.
 Observing and recording well-defined events (e.g., counting the number of
patients waiting in emergency at specified times of the day).
 Obtaining relevant data from management information systems.
 Administering surveys with closed-ended questions (e.g., face-to face and
telephone interviews, questionnaires etc).
(http://www.achrn.org/quantitative_methods.htm)

Interviews

In Quantitative research (survey research),interviews are more structured than in
Qualitative research. In a structured interview, the researcher asks a standard set of
questions and nothing more.

Face -to -face interviews have a distinct advantage of enabling the researcher to
establish rapport with potential participants and therefore gain their cooperation. These
interviews yield highest response rates in survey research. They also allow the researcher
to clarify ambiguous answers and when appropriate, seek follow-up information.
Disadvantages include impractical when large samples are involved time consuming and
expensive.(Leedy and Ormrod, 2001)

Telephone interviews are less time consuming and less expensive and the researcher has
ready access to anyone on the planet that has a telephone. Disadvantages are that the
response rate is not as high as the face-to- face interview as but considerably higher than
the mailed questionnaire. The sample may be biased to the extent that people without
phones are part of the population about whom the researcher wants to draw inferences.

Computer Assisted Personal Interviewing (CAPI): is a form of personal interviewing,
but instead of completing a questionnaire, the interviewer brings along a laptop or hand-
held computer to enter the information directly into the database. This method saves time
involved in processing the data, as well as saving the interviewer from carrying around
hundreds of questionnaires. However, this type of data collection method can be
expensive to set up and requires that interviewers have computer and typing skills.

Questionnaires

Paper-pencil-questionnaires can be sent to a large number of people and saves the
researcher time and money. People are more truthful while responding to the
questionnaires regarding controversial issues in particular due to the fact that their
responses are anonymous. But they also have drawbacks. Majority of the people who
receive questionnaires don't return them and those who do might not be representative of
the originally selected sample.(Leedy and Ormrod, 2001)

Web based questionnaires : A new and inevitably growing methodology is the use of
Internet based research. This would mean receiving an e-mail on which you would click
on an address that would take you to a secure web-site to fill in a questionnaire. This type
of research is often quicker and less detailed. Some disadvantages of this method include
the exclusion of people who do not have a computer or are unable to access a computer.
Also the validity of such surveys are in question as people might be in a hurry to


complete it and so might not give accurate responses.
(http://www.statcan.ca/english/edu/power/ch2/methods/methods.htm)

Questionnaires often make use of Checklist and rating scales. These devices help
simplify and quantify people's behaviors and attitudes A checklist is a list of behaviors,
characteristics, or other entities that te researcher is looking for. Either the researcher or
survey participant simply checks whether each item on the list is observed, present or true
or vice versa. A rating scale is more useful when a behavior needs to be evaluated on a
continuum. (Leedy and Ormrod, 2001)

Qualitative data collection methods play an important role in impact evaluation by
providing information useful to understand the processes behind observed results and
assess changes in people’s perceptions of their well-being .Furthermore qualitative
methods can be used to improve the quality of survey-based quantitative evaluations by
helping generate evaluation hypothesis; strengthening the design of survey questionnaires
and expanding or clarifying quantitative evaluation findings. These methods are
characterized by the following attributes:

 they tend to be open-ended and have less structured protocols (i.e., researchers
may change the data collection strategy by adding, refining, or dropping
techniques or informants)
 they rely more heavily on iterative interviews; respondents may be interviewed
several times to follow up on a particular issue, clarify concepts or check the
reliability of data
 they use triangulation to increase the credibility of their findings (i.e., researchers
rely on multiple data collection methods to check the authenticity of their results)
 generally their findings are not generalizable to any specific population, rather
each case study produces a single piece of evidence that can be used to seek
general patterns among different studies of the same issue

Regardless of the kinds of data involved, data collection in a qualitative study takes a
great deal of time. The researcher needs to record any potentially useful data thoroughly,
accurately, and systematically, using field notes, sketches, audiotapes, photographs and
other suitable means. The data collection methods must observe the ethical principles of
research.

The qualitative methods most commonly used in evaluation can be classified in three
broad categories:

 in-depth interview
 observation methods
 document review

Question 4- Answer
Compare and show appropriateness in use of methods and techniques
of analyzing data

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data
with the goal of highlighting useful information, suggesting conclusions, and supporting
decision making. Data analysis has multiple facets and approaches, encompassing diverse
techniques under a variety of names, in different business, science, and social science
domains.

Data Analysis is the process of systematically applying statistical and/or logical
techniques to describe and illustrate, condense and recap, and evaluate data. According to
Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing
inductive inferences from data and distinguishing the signal (the phenomenon of interest)
from the noise (statistical fluctuations) present in the data”..

While data analysis in qualitative research can include statistical procedures, many times
analysis becomes an ongoing iterative process where data is continuously collected and
analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in
observations through the entire data collection phase (Savenye, Robinson, 2004). The
form of the analysis is determined by the specific qualitative approach taken (field study,
ethnography content analysis, oral history, biography, unobtrusive research) and the form
of the data (field notes, documents, audiotape, and videotape).

An essential component of ensuring data integrity is the accurate and appropriate analysis
of research findings. Improper statistical analyses distort scientific findings, mislead
casual readers (Shepard, 2002), and may negatively influence the public perception of
research. Integrity issues are just as relevant to analysis of non-statistical data as well.

Once have your data, you must ANALYZE it. There are many different ways to analyze
data: some are simple and some are complex. Some involve grouping, while others
involve detailed statistical analysis. The most important thing you do is to choose a
method that is in harmony with the parameters you have set and with the kind of data you
have collected.

With the data in a form that is now useful, the researcher can begin the process of
analyzing the data to determine what has been learned. The method used to analyze data
depends on the approach used to collect the information (secondary research, primary


quantitative research or primary qualitative research). For primary research the selection
of method of analysis also depends on the type of research instrument used to collect the
information.

Essentially there are two types of methods of analysis – descriptive and inferential.

Descriptive Data Analysis

Descriptive analysis, as the name implies, is used to describe the results obtained. In most
cases the results are merely used to provide a summary of what has been gathered (e.g.,
how many liked or dislike a product) without making a statement of whether the results
hold up to statistical evaluation. For quantitative data collection the most common
methods used for this basic level of analysis are visual representations, such as charts and
tables, and measures of central tendency including averages (i.e., mean value). For
qualitative data collection, where analysis may consist of the researcher’s own
interpretation of what was learned, the information may be coded or summarized into
grouping categories.

Inferential Data Analysis

While descriptive data analysis can present a picture of the results, to really be useful the
results of research should allow the researcher to accomplish other goals such as:

 Using information obtained from a small group (i.e., sample of customers) to
make judgments about a larger group (i.e., all customers)
 Comparing groups to see if there is a difference in how they respond to an issue
 Forecasting what may happen based on collected information

To move beyond simply describing results requires the use of inferential data analysis
where advanced statistical techniques are used to make judgments (i.e., inferences) about
some issue (e.g., is one type of customer different from another type of customer). Using
inferential data analysis requires a well-structured research plan that follows the scientific
method. Also, most (but not all) inferential data analysis techniques require the use of
quantitative data collection.

As an example of the use of inferential data analysis, a marketer may wish to know if
North American, European and Asian customers differ in how they rate certain issues.
The marketer uses a survey that includes a number of questions asking customers from all
three regions to rate issues on a scale of 1 to 5. If a survey is constructed properly the
marketer can compare each group using statistical software that tests whether differences
exists. This analysis offers much more insight than simply showing how many customers
from each region responded to each question.

Question 5- Answer
Outline the major parts of a Final Research Report. Briefly explain the
content expected to find in each part.
Writing your research paper requires careful forethought. The major parts of a Final
Research Report are listed as:

- Introduction
- Literature review
- Design/ Methods
- Results
- Conclusion
My Outline should include the following ingredients:

1. INTROCUCTION

The main purpose of the INTRODUCTION is to give a description of the problem that
will be addressed. In this section the researcher might discuss the nature of the research,
the purpose of the research, the significance of the research problem, and the research
question(s) to be addressed.

Three essential parts of a good introduction are:

 RATIONALE
 PURPOSE
 RESEARCH QUESTION(S)

a) RATIONALE

Somewhere in the introduction you need to inform the reader of the rationale of your
research. This is a brief explanation of why your research topic is worthy of study and
may make a significant contribution to the body of already existing research

b) PURPOSE


The statement of purpose is not simply a statement of why the research is being done.
(That is what the rationale section is for.) Rather, "purpose" refers to the goal or objective
of your research. The purpose statement should answer questions….

 "What are the objectives of my research?" and
 "What do I expect to discover or learn from this research?"

c) RESEARCH QUESTION

The introduction usually ends with a research question or questions. This question should
be. . .

 Related to your research purpose
 Focused
 Clear

2. LITERATURE REVIEW

As part of the planning process you should have done a LITERATURE REVIEW,
which is a survey of important articles, books and other sources pertaining to your
research topic. Now, for the second main section of your research report you need to
write a summary of the main studies and research related to your topic. This review of the
professional literature relevant to your research question will help to contextualize, or
frame, your research. It will also give readers the necessary background to understand
your research.
Evaluating other studies:
In a review of the literature, you do not merely summarize the research findings that
others have reported. You must also evaluate and comment on each study's worth and
validity. You may find that some published research is not valid. If it also runs counter to
your hypothesis, you may want to critique it in your review. Don't just ignore it. Tell how
your research will be better/overcome the flaws. Doing this can strengthen the rationale
for conducting your research.
Selecting the studies to include in the review:
You do not need to report on every published study in the area of your research topic.
Choose those studies which are most relevant and most important
Organizing the review:
After you have decided which studies to review, you must decide how to order them. In
making your selection, keep your research question in mind. It should be your most
important guide in determining what other studies are relevant. Many people simple
create a list of one-paragraph summaries in chronological order. This is not always the
most effective way to organize your review. You should consider other ways, such as...


 By topic
 Problem -> solution
 Cause -> effect

Another approach is to organize your review by argument and counter argument. For
example. You may write about those studies that disagree with your hypothesis, and then
discuss those that agree with it. Yet another way to organize the studies in your review is
to group them according to a particular variable, such as age level of the subjects (child
studies, adult studies, etc.) or research method (case studies, experiments, etc.).
The end of the review:
The purpose of your review of the literature was to set the stage for your own research.
Therefore, you should conclude the review with a statement of your hypothesis, or
focused research question. When this is done, you are ready to proceed with part three of
your research report, in which you explain the methods you used.

3. DESIGN & METHOD

The DESIGN & METHOD section of the report is where you explain to your reader
how you went about carrying out your research. You should describe the subjects, the
instruments used, the conditions under which the tests were given, how the tests were
scored, how the results were analyzed, etc.

Remember that this section needs to be very explicit. A good rule of thumb is to provide
enough detail so that others could replicate all the important points of your research.
Failure to provide adequate detail may raise doubts in your readers' minds about your
procedures and findings.

Make sure you are honest and forthright in this section. For example, if you had some
problems with validity, acknowledge the weaknesses in your study so that others can take
them into account when they interpret it (and avoid them if they try to replicate it).

4. RESULTS

n the RESULTS of your report you make sense of what you have found. Here you not
only present your findings but also talk about the possible reasons for those findings.
Also, if your research approach was deductive, then here is where you accept or reject
your hypothesis (based on your findings). In addition, in this section you should use your
knowledge of the subject in order to make intelligent comments about your results.

Make sure your comments are related to (and based on) your research. Do not go beyond
your data. Also, as you report and interpret your findings do not exaggerate or


sensationalize them. Nor should you minimize them. A straightforward matter-of-fact
style is probably best.

5. CONCLUSION

In the CONCLUSION to your report, you do a number of important things:

1. Summarize the main points you made in your introduction and review of the literature
2. Review (very briefly) the research methods and/or design you employed.
3. Repeat (in abbreviated form) your findings.
4. Discuss the broader implications of those findings.
5. Mention the limitations of your research (due to its scope or its weaknesses)
6. Offer suggestions for future research related to yours

ABSTRACT

Some research reports end (or begin) with an abstract. An abstract is a highly abbreviated
(usually 100-200 words) synopsis of your research. It should describe your rationale and
objectives, as well as your methods and findings.

Because of its limited length, an abstract cannot go into detail on any of these topics. Nor
can it report on the limitations of your research or offer suggestions for future research.
For those, readers will have to read the entire report. But, after reading your abstract,
people unfamiliar with your research should know what it is about and whether they want
to read the entire report.


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