Understanding Business Research
Research at least three quantitative data collection instruments and sampling methods available to
researchers using the text and additional resources from the University Library.
Identify two articles in the University Library: one in which the business problem is researched using a
descriptive statistical method and another using an inferential method.
Summarize each of the data collection instruments, sampling methods, and the statistical methods.
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Write a 1,050- to 1,400-word paper in which you compare and contrast each of the approaches:
�What are the strengths and weaknesses of each sampling approach?
�What are the specific situations in which you would choose to use each of the instruments and
designs?
�What are the strengths and weaknesses of each statistical approach?
�How can they be used most effectively in a combined approach?
�Which methods are more appropriate for research in your own business of youth ministry?
When conducting research, it is important to determine an appropriate research sample
that will bring out the desired results. For any researcher, it is important that they know the
observations and cases from the findings do contribute useful information.
The simple random sample is a very basic and ideal sampling method. The method is
easy to understand which makes it appropriate where not much information about population is
needed. Its use eliminates subject classification errors while making it possible to analyze and
interpret results with relative ease. Despite its great value, simple random sampling is bedeviled
by two main issues. When analyzing a large population, the sampling error reported was higher
than when using stratified sampling in addition to having has to number each population
element. Simple random sampling is best suited for target populations where members have a
higher degree of similarities on the important variables (Singha & Jaman, 2012).
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The proportional stratified sampling approach derives its strength for its ability to use
easy subgroup comparisons while capturing a more representative sample than random and
systematic. In this approach, the relationship between dependent variable is considered carefully
and when the incidence is high, fewer subjects will be used and the results from this approach
have emerged as a good population representation without weighting.
For its much strength, the proportional stratified has major hiccups. Its requirement that
each population subgroup be identified and proportion identified makes a costly and difficult
model to use since the population elements must be listed. This sampling approach is has
emerged as most suited for heterogeneous populations containing quite a number of different
groups exhibiting high relations and interconnection to the study topic.
The Cluster sampling method has emerged as the most suited of sampling approaches to
address itself to population that largely consists of units as opposed to individuals. Given its
approach, it is a very cheap method has proved time and again to be very efficient when
employed in large populations. Its ability to enable for individual cluster analysis is extremely
helpful. Unfortunately, it has emerged to be the least accurate of the sampling methods given the
high than normal challenge of trying to get objective data from a cluster.
Considering further that each population element will have to be assigned to only one
cluster, this further undermines the approach. There is need to consider the uniqueness of each
member of a unit, which makes the approach less effective. Despites its shortcoming, it has
emerged as the best suited approach to when considering population as units as opposed to
individuals.
When considering a sampling technique to apply to a particular population, the error of
the sample should be at the forefront. It should be the sole aim of each approach to reduce the
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sampling error as much as possible within the confines of reality. It should be representative
enough to cover the whole population without having a sample so big that it makes sampling
uneconomical. On the overall, it remains the rule that the larger the sample, the smaller the
sampling error and the better the job one can do.
When the sample has been identified, an appropriate quantitative data collection
instrument must be identified for the task. From the journals analyzed, interviews and focus
groups are the most used instruments.
Interviews usage is based on the assumption that the respondents’ view of the question is
meaningful, affects the outcome of the research in a significant way and can be explicitly
expressed. To achieve this, one chooses to either use structured – questionnaire based, or in-
depth – respondents are encouraged to freely express themselves with use of open responses.
Both data collection instruments are ideally suited for evaluation research. The choice of
instrument to use will depend on the data the researcher is looking for.
When the information needed is precise and easily defined, then a questionnaire will be
best suited – precisely worded questions are asked with specific parameters set where the
responses must fit, while in-depth interviews are designed to have the respondents open up and
freely give information. The latter instrument may be more time consuming but is better suited
when the researcher is looking to measure opinion and would like to know the basis of the
opinion.
Interviews have emerged to be the most ideal tools when issues to be addressed include
questions like;
To the stakeholders and participants, what does the issue under research feel and look to
them?
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What do both parties know about the research issue?
For the participants and stakeholders with knowledge about the research issue, what are
their views concerning the various components of the issue?
What are their expectations?
With regards to the issue under research, what features are most salient to the
stakeholders and participants?
Descriptive statistical methods are designed to paint a better picture of the data where
there are a lot of results. As opposed to listing results, descriptive statistical methods avoid the
boredom that would by synonymous with this and offer a tool that easily simplifies large
amounts of data, highlighting the key factors while presenting the information in an easily
understandable manner with clear focus on the parameters under scrutiny (Grim, 2012).
This method will thus sort and group, illustrate and develop summary statistics that
elucidate of the research question. When sorting and grouping data, one has a choice between
using range of values or frequency. The latter is the best method when sorting huge amounts of
data and is able to show in a simple way number by type present – qualitative or discrete
quantitative data.
When faced with data requiring size comparison of different data during interpretation,
bar charts have emerged as the most widely used of approaches – qualitative data only. For
quantitative discrete data, a frequency diagram is the best suited – emphasis on discreteness will
be best elucidated by the use of strips. When the data to be exhibited shows comparison between
information side by side, a composite bar chart will be idea.
When in need to showing how a total amount is divided into constituent parts, then a pie-
chart is best suited. A pie chart will normally require the data to be captured in percentages for
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better value allocation. Finally, there are instances when research is a bit complicated and
requires two or move variable to be related. When seeking a link between the two is sort, a
scatter diagram will best capture this results and show the relationship.
The Inferential statistical method is best when inferences are required from a large
population based on a sample. Since the measure of comparison is characteristic of the
population, a sample cannot accurately capture the true picture of the population. Thus the
sample will have to overcome errors (Lantz, 2013). When the final analysis is made, then a
qualified inference is arrived at based on a particular percentage of confidence that it is
representative. In inferential analysis, one can use Bivariate – to analyze two variable, or
Multivariate – to analyze more than two variables (Lantz, 2013). The choice of inferential
statistic will depend on the type of variable to be analyzed – Nominal, Ordinary, Interval/Ratio.
For the business of youth ministry, it is important to use research tools that will allow for
capturing of all aspects and views of the participants. The choice of tool will be especially
important given the dynamics of the youth ministry. By their very nature, the youth are very
vibrant and highly mobile. The choice of method will be one that can capture these two critical
aspects. The use of group or in-depth discussion will capture all aspects that are important to the
sample population. By allowing the youth to ventilate and speak, they get a feeling that their
views are value and thus are more likely than not to be responsive and be more generous with
their views.
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Reference:
Grim, B. J (2012) Is Religious Freedom Good for Business. A Conceptual and Empirical
Analysis, Interdisciplinary Journal of Research on Religion, Vol. 10, No. 4, pp. 25-72.
Lantz, B (2013) Equidistance of Likert-type Scales and Validation of Inferential Methods Using
Experiments and Simulations, Electronic Journal of Business Research Methods, Vol. 11,
No. 4, pp. 16-28.
Singha, K & Jaman, M. S (2012) Nexus between Population and Economic growth in India: A
Co-integration analysis, Scientific Journal of Pure and Applied Sciences, Vol. 3, No. 3,
pp. 1-7.
