Research and Statistical Methods for Business

This article discusses the importance of sample size, current method of sampling, reliability and validity of measures, importance of demographic data, and the use of descriptive research design in the context of research and statistical methods for business.

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Running Title: RESEARCH AND STATISTICAL METHODS FOR BUSINESS
RESEARCH AND STATISTICAL METHODS FOR BUSINESS
STUDENT’S NAME:
INSTITUTION NAME:

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RESEARCH AND STATISTICAL METHODS FOR BUSINESS
1. Is the sample size necessary?
Sample size is the observations or the number of participants included in the study. The
sample size is usually indicated by letter n has two major importance; for the precision of the
estimates and it gives the power for drawing a conclusion.
Choosing to work with data of 15,000 employees instead of 69,000 employees can give
some sense of precision when dealing with such data. 15000 sample size is able to give the
needed information of all the employees because no matter how accurate the data collection
might be, the study results can have some margin of error. This is because not everyone was
interviewed. There could be no absolute precision in all the variables collected about the
employees, how these variables affect each other, which is why there is an error considered. This
error is called sampling error and it influences the description on the factors affecting employees
in their various areas of work, but a point will reach, known as diminishing returns, where the
increase in sample size will not have any effect on the sampling error. In most cases sampling
error is an unavoidable. Large sample size has small sampling error compared to small sample
size because when clear picture of the data is needed, more and more observations needed to be
done for comparison.
Sample size also helps in building the confidence of the researcher. At one point the
researcher might need to build his confident interval on the estimates of the data. The confidence
of the researcher can be increased with the increase in sample size. When the interval between
the confident estimates is smaller then the researcher is more confident with the data.
2. What is the current method of sampling? What are the advantages and
disadvantages of the current sampling method?
Random sampling
Random sampling, which sometimes is known as method of chances, is a sampling where
each item in the population has an even chance and likelihood of being selected. This method of
sampling entirely depends on the probability or a chance. It is fair in terms of establishing the
size of the sample.
Advantages of random sampling.
There is less risk of carrying an error with random sampling: Since random sampling
gives an equal chance to every item in the population to be selected, the sampling size will be

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reflecting the entire population giving data the ability to give clear insights into some specific
matters.
It is easy to form a sample groups: Random sampling takes only a few items from the
population makes it easy to form sampling group from large population. This makes it very
simple to use or apply.
Random sampling requires less knowledge to complete the research: Random
sampling can be used even if the researcher do not have proper knowledge of the population in
question. Once the researcher has developed the question, he doesn’t have to have a vast
knowledge on the population in question.
Random sampling is the simplest form of collecting data: Once the researcher has the
basic skills on the observation and recording, he will require no basic skills in out of the base of
population or the item being investigated. This reduces the errors associated with classifying the
population under study according to some variability.
Findings of the random sampling is able to reflect the whole population: Random
sampling gives every item the probability of being chosen, this can reduce the biasness and
represents the entire population without the researcher worrying about some part of the
population left out.
Several randomness types can be used to reduce the bias of the researcher: the two
approaches common with random sampling to reduce biasness are lottery method and random
numbers methods. Lottery method which involves drawing a population group and see the ones
included and the ones who will not be included can be used with random sampling. Research
numbers can also be applied where random numbers are given to some specific individuals and
then collect these random numbers randomly.
Disadvantages of Random Sampling.
Random sampling does not take into consideration the additional knowledge: Although
random sampling removes the unconscious bias, sometimes dealing with intentional bias can be
very difficult. The researcher can choose a specific group to carry out sampling on, a population
he knows has specific characters he needs. This do not consider any additional knowledge which
can be gathered from the population.
Random sampling is complex and can consume a lot of time: For the proper collection
of data, each individual selected must be interviewed. The people from the same group can have

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their response influenced by the response of others hence making the researcher use more
resources and time and can also reduce the efficiencies.
There is additional monetary value attached to random sampling: Collecting data at a
personal level will require more additional costs compared to other sampling methods. The
researcher can do that with aim of achieving high accurate data for the benefit of those who are
going to utilize the data.
Sample size required in random sampling can be large: For a successfully random
sampling, the researcher must consider gathering large sample size. This means that the
population from which that sample size is going to be taken from must be very large (Witte,
Witte, & Taillieu, 1978). This will be very costly and it can take a lot of time of the researcher to
achieve that sample size. This will even render some groups or demographics not to support
random sampling at all.
3. Give your comments on the reliability and validity of measures of the variables
The measure variables used in this kind of study are not reliable. For example, when we
take the case of quantitative job insecurity where the respondents responded by either “strongly
disagree”, “disagree”, “agree”, “Strongly disagree”, finding the measure for these responses are
very difficult. Establishing the difference between strongly agree and disagree is not possible.
We cannot get the mean, measures of dispersion, point estimates from these responses.
Therefore, we cannot in any way, subject the findings from this research to statistical analysis.
Once statistical analysis is not involved the results become unreliable and unscientific.
It also goes for the response on the qualitative job insecurity variables. Dissatisfaction
and satisfaction of someone cannot be measured because they are just but the feelings, attitudes,
habits and behaviors of people they can’t be measured making it difficult to involve statistical
analysis.
In terms of validity, the variables are valid in sense that they lean towards answering the
researcher’s questions. When the research is finding the reasons why employees feel that their
jobs are insecure quantitatively and qualitatively, the variables are doing that. For example, when
the researcher is trying to establish the feelings employees have towards things like the job
content, employment conditions, conditions of work and the relationship the employees has
amongst themselves Likert scale of strongly deteriorate and strongly improve will be very valid
(De Witte H. D., 2010)..

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4. Importance of demographic data in a research
The age, gender, education level and extra income are demographic data about the
respondents. Demographic data are data which cannot be altered, they are very specific on each
respondent and they are independent.
Demographic data enable the researcher to group the employees according to their age so
that he can establish how the other variables behave according to the age of the respondents. This
can bring a proper understanding of each segment and know how qualitative and quantitative job
insecurity affects them (Ashford, Lee, & Bobko, 1989).
Education level can affect how employees view their job securities, for example. This
will enable the researcher to establish which group here feel that their future employment is more
or less secure than the other. It can also establish the employees who group their job insecurity
under qualitative or quantitative.
Other demographic data like gender can also affect the line which a particular data may
take. Each gender may have varied opinion on how their jobs can be affected in one way or
another. This will enable the researcher to establish which gender feels more secure in terms of
their jobs in future and due to surrounding situations and the reasons why they would feel that
way. The researcher on the hand can establish which gender feel less secure in terms of their jobs
in future and in terms of the conditions surrounding them currently and the reasons why they
would feel that way (Witte H. D., 2000).
Therefore, demographic data is very important to the researcher. Although they might
seem to be completely unrelated to the questions the researcher is trying to find, they greatly
influence on how the people responds to the questions under the research.
5. The researcher used Descriptive Research Design
Descriptive Research design.
From the research carried out by the researcher, it can be seen that the researcher was just
concerned with establishing and describe the case or situation under the study he was carrying
out. Therefore, it can be concluded that the researcher used descriptive research design. The
design is based on theory created through collecting, analyzing and presenting the results. The

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research is able to establish and provides the insights on the questions of “how” and “why” of the
research.
Positive sides of descriptive research design.
With descriptive research design, the researcher can carry out the research without
interfering with the normal life of the participants, that is, normal behaviors and activities. He
can observe them in an environment which is not changed and remain natural.
Descriptive research design can help in establishing a future research because it can be a
precursor to many future researches since it can help in identifying testable variables (Casadevall
& Fang, 2009). For example, here the researcher may be able to establish some variable about
certain gender, affecting their working condition.
Descriptive research design allows for quantitative surveys and qualitative case studies to
be studied deeply allowing the varieties of approach towards collection and analysis of data.
It allows large amount of data to be collected and data which is rich with information.
Descriptive research design data can be used by the employer to determine the behaviors, habits,
beliefs, attitude of the employees at large
Negative side of the descriptive research design
Wrong information can be gathered because the participants may not say the truth and
may not behave normally when they realize them being observed.
It is very difficult to correlate variables and establish the effect one variable has on
another variable
The information gathered may not show confidentiality
The researcher may influence a lot the kind of information he wants making him to make
subjective choices bringing a lot of biasness of the data.
It is not easy to manipulate variables hence doing statistical analysis is not possible with
such kind of data making it unscientific and unreliable (Shields & Rangarajan, 2013).

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References
Ashford, S. J., Lee, C., & Bobko, P. (1989). CONTENT, CAUSE, AND CONSEQUENCES OF JOB INSECURITY: A
THEORY-BASED MEASURE AND SUBSTANTIVE TEST. Academy of Management Journal, 32(4),
803-829. doi:10.2307/256569.
Casadevall, A., & Fang, F. C. (2009). Descriptive Science. Infection and Immunity, 3835–3836.
De Witte, H. D. (2010). Associations between quantitative and qualitative job insecurity and well-being:
A test in Belgian banks. International Studies of Management & Organization. 40(1), 40-56.
doi:10.2753.
Shields, P. A. (2013). Integrating Conceptual Frameworks and Project Management. A Playbook for
Research Methods, 49-103.
Witte, H. D. (2000). Arbeidsethos en jobonzekerheid: meting en gevolgen voor welzijn, tevredenheid en
inzet op het werk (Work Ethic and Job Insecurity: Measurement and Consequences for Well-
Being, Satisfaction, and Performance at Work). In van groep naar gemeenschap, ed. R. Bouwen.
Witte, K. D., Witte, H., & Taillieu, T. (1978). Manual of the General Health Questionnaire. Windsor, UK:
NFER-Nelson., 325-350.