Professional Research and Communication (ITECH 5500) Assignment 1

This assignment solution for ITECH 5500 covers several key concepts in research and data analysis. It begins with an analysis of a survey, discussing the use of the Interquartile Range (IQR) to validate data and address uncertainties. The solution then delves into stratified sampling, outlining its pros and cons within a specific case study. The assignment also explores different data types, including interval and nominal data, providing examples for each. Finally, it examines various research methodologies such as descriptive non-experimental, experimental, and quasi-experimental studies, highlighting their characteristics and applications. The provided bibliography lists the sources used to support the analysis and findings.

Name
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Contents
Question 1:.................................................................................................................................3
The IQR technique can act as a tool to check the impact of the variable..............................3
Question 2:.................................................................................................................................3
Introduction of the case.........................................................................................................3
The Pros of Stratified Sampling in the current case...............................................................4
The cons of Stratified sampling in the current case...............................................................4
Conclusion..............................................................................................................................5
Question 3..................................................................................................................................5
Question 4..................................................................................................................................6
Descriptive Non-Experimental Study.....................................................................................6
Experimental Study................................................................................................................6
Quasi-Experimental Research................................................................................................7
Bibliography...............................................................................................................................7
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Question 1:
In the current equation, we can see the calculations based on the averaging of the Likert Data.
Considering the fact that 17 percent of the respondents are unsure. This median of 3.19
cannot be justified because of it bents the equation toward the solution that supports the
bracket of “strong agreement “for the statement. We cannot undermine the fact that the
opinion of the 17 percent of people (not sure) should be treated as a very strong variable in
this case.
The IQR technique can act as a tool to check the impact of the variable
In order to nullify this unwarranted variable, we can use the technique of finding an Inter-
quartile range (IQR). IQR range can act as a confirmatory value on the validity of 3.19. IQR
is the difference between 75th and 25th percentiles between the upper and the lower quartiles
(Kumar, 2015). If the value of IQR is one or two then the current value is closer to the
interpretation that it is a strong opinion. If the IQR is in the range of three and four then it
shows that the current finding is not giving us the right picture or the right value.
Question 2:
Introduction of the case
In the above-mentioned case, the polling company is adopting a quantitative method to
research. Further, it can be termed as the stratified sampling where geographical region and
time bands are functioning as the agent to minimize the variables. In the current case, the
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corner of the street is the spatial strata or the strata of the geography and time band of 9-12 is
another variable to keep a check on the excessive randomization of the survey result. Many
experts also term it as a stratified cluster. However, the theories connected to the cluster are
not applicable here because we don't have a dedicated list or a list closer to a dedicated list to
fix a sampling frame (Thompson, 2012).
The Pros of Stratified Sampling in the current case
1. A stratified sample based on the geographical area presents a clear subdivision of the
population of the city. The method of Random sampling further brings down the
biasness of the result by ensuring a maximum representation of all the possible
subgroups based on gender, caste, and creed.
2. This type of data collection also allows calculating the mean of each subgroup
differently. Sometimes these individual values can play an important role when we
deal with one-sided results. For instance, the trends coming after calculating the mean
of the larger picture gives us an indication of the victory for a particular party. The
mean of the subgroups where the winning party is not performing well can give us an
indication about the margin of the victory when we treat the data separately.
3. The disproportionate allocation of the sampling frame will introduce a low standard
deviation when we will calculate the mean for a large amount of data (Lavy, 2013).
This is an advantage that will help us in bringing down the biases ratio. In the
common parlance, we can also say that disproportionate allocation will ensure the
representation of a maximum number of subgroups by default because we are not
setting any boundaries.
The cons of Stratified sampling in the current case
1. If the prima facie findings of the processing of the research data show mix results
then it can also give rise to Simpson's paradox, where the impact of the variance
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may go on the higher side and give us two contrasting pictures (S, 2015). There
are two ways to process; first, we can find a sum of all the results obtained from
the subgroups. It will give us a result connected to the predictions, in the second
method we can randomly mix the data of the subgroups in a wider picture and
apply the same set of calculations that we did with the subgroups. In the second
case sometimes the findings may vary and show exactly the opposite results
(Gupta, 2012).
2. In the current system, we cannot see any means to keep a check on the
overlapping of the feedbacks. In spite of an access to a large number of data,
sometimes we can find it difficult to find similar fields that can help us in
corroborating our findings.
Conclusion
In the current data collection model, means to prevent this overlapping are missing. It
is also a thumb rule that stratification of the data often needs a secondary layer of the
research of action research to come up with exact findings. In the absence of the
means to keep a check on the overlapping of the data, the results may vary and show a
wrong picture of the likely outcome.
Question 3
a) Cars passing through a signal in a given hour (whole numbers) is an interval data. An
interval data can be compared with the help of numbers. In this case, we can compare
the data internally as well as externally. Internally we can find out that which five
minutes of the hour was the busiest. We can also differentiate between various time
intervals. If we have two samples with us then again we can compare them as well. It
means that the change from 1 to 2 is equal to the change from 7 to 8.
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b) Kelvin Thermometers represents a Ratio data. This we can state because of the
presence of the absolute zero in the scale. While dealing with a Kelvin scale we can
also use the finding for descriptive and inferential statics. Any Ratio data based scale
allows us to add, subtract, divide and multiply the ratios. This property is also present
on the Kelvin scale.
c) Fahrenheit Thermometers represent interval based scales because we can deal with
the direct values present on the scale.
d) The type of the mobile phone that a person is having is a nominal data. Here each
mobile phone represents a different label.
e) A person's height is a nominal data when we are taking care of a single subject. It is
just another entry like the color of the eyes or the color of the hair. However, when we
are collecting a database corresponding to the height of a person then it becomes an
interval data because we can compare the values (black, 2009).
Question 4
Descriptive Non-Experimental Study
First I will conduct an exam for a lesson that they learned through recording. Later on, I will
compare its result with a lesson where they attended a human lecture. The non-experimental
study defines that the subjects should be studied in a natural environment. The ratio of the
variables may be high, however, a descriptive finding can be accumulated (Turner, 2014).
Experimental Study
Under the experimental study, I will divide the class into two random groups; one of them
will learn the lesson with the help of video class. Whereas the other group will attend the
regular class, the comparison of the results will give me an idea that which one is better.
Experimental research is the research where we can manipulate one variable while keeping
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rest of them stationary. Here we need to design some groups and treat every group as an
evidence of the fact that we are trying to corroborate or find (Mertler, 2018).
Quasi-Experimental Research
Quasi-experimental Research is the next level of the experimental research. In this type of
research, we need to check the baseline properties of the groups that we have made. In the
current case, we can add some variables like high scorers, low scorers etc. While making
groups, after the formation of the group we can subject them to similar chapter under similar
conditions (Suter, 2011). The results obtained with the help of this method will be more
accurate because baselines are the same and the variables are playing a minimalistic role in
the process.
Bibliography
black, k. (2009). Business Statistics: Contemporary Decision Making. New Jersey: Wiley.
Gupta, A. K. (2012). Theory of Sample Surveys. Singapore: World Scientific.
kumar, D. C. (2015). Construction of Interquartile range (IQR) control chart using process. ,
American International Journal of Research in Science, Technology, Engineering &
Mathematics,
https://pdfs.semanticscholar.org/f326/6af8020111de4d9ffe1bb6cbe509c7fe0fde.pdf
.
Lavy, P. S. and Lemeshow.S (2013). A sampling of Populations: Methods and Applications 4th
Edition . New Jersey: Wiley.
Mertler, C. A. (2018). Introduction to Educational Research. California: Sage Publications.
S, K. and Banerjee. M (2015). Logic and Its Applications: 6th Indian Conference, ICLA 2015,
Mumbai, India. ICIA 2015 (pp. 58-59). Germany Springer.
Suter, W. N. (2011). introduction to Educational Research: A Critical Thinking Approach.
California: Sage Publications.
Thompson, S. K. (2012). Sampling. New Jersey: Wiley.
Turner, J. L. (2014). Using Statistics in Small-Scale Language Education Research: London:
Routledge.
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