Formulation of the Research Problem, Part V Michael Colapietro, Robert Contreras, Donald Dennis, Ericka Gutierrez, Russell Girdler, Diana Velez QNT/561 » Applied Business Research and Statistics February 28, 2015 Heidi Carty Formulation of the Research Problem Customer satisfaction is very important in the new home building trade. Builders compete to have the highest customer satisfaction rating which translates into sales. Builders are judged by independent survey service providers (JD Powers, 2015). Intermountain West Builders needs to have strong customer satisfaction scores to be competitive in the marketplace. The company is conducting a research study regarding price and home quality based on customer satisfaction scores. The research broken down into two variables that need to be studied, a research question is formed, and finally a hypothesis is reached (McClave, Benson, & Sincich, 2011). The Variables Intermountain West Builders goal is to stay competitive therefore need to increase sales and have a competitive advantage over their competitors. They have focused their attention on the following two variables: price and age. A dependent variable responds to the independent variable. The independent variable is home quality and the dependent variable is age. Age of the buyer will have an effect on the price of the home. Young people still do not have family and this result to less household when it comes to house equipment’s and occupants. This will result in buying a small and less expensive house. Likewise, if the customer is of older age (35+) he or she will buy a more expensive and larger house because of a larger family occupant. Background and Business Problem Intermountain West Builders is a company with a history of quality built homes. This strong background helps propel them as a premier builder of quality homes. One aspect of this company is its personable approach to each customer. No customer is excluded or discounted despite the size of this company. However, recent trends suggest customer satisfaction is not as high as the perception suggests. The problem would take value from the company and further alienate the customer’s perception of both satisfaction and quality homes built. The company business model of customer satisfaction should be reviewed based on feedback generated from surveys. Research Question The two variables in question are home age of the customer and the price. If the customer is older, they are more likely to pay a higher price for a bigger home. This principal applies to many other applications, be it purchase of house, any household item, grocery item, or any other service. Moreover, a house is a large investment, where it is diffic.

1. Formulation of the Research Problem, Part V
Michael Colapietro, Robert Contreras, Donald Dennis,
Ericka Gutierrez, Russell Girdler, Diana Velez
QNT/561 » Applied Business Research and Statistics
February 28, 2015
Heidi Carty
Formulation of the Research Problem
Customer satisfaction is very important in the new
home building trade. Builders compete to have the highest
customer satisfaction rating which translates into sales. Builders
are judged by independent survey service providers (JD Powers,
2015). Intermountain West Builders needs to have strong
customer satisfaction scores to be competitive in the
marketplace. The company is conducting a research study
regarding price and home quality based on customer satisfaction
scores. The research broken down into two variables that need
to be studied, a research question is formed, and finally a
hypothesis is reached (McClave, Benson, & Sincich, 2011).
The Variables
Intermountain West Builders goal is to stay competitive
therefore need to increase sales and have a competitive
advantage over their competitors. They have focused their
attention on the following two variables: price and age. A
dependent variable responds to the independent variable. The
independent variable is home quality and the dependent variable
is age. Age of the buyer will have an effect on the price of the
home. Young people still do not have family and this result to
less household when it comes to house equipment’s and
occupants. This will result in buying a small and less expensive
house. Likewise, if the customer is of older age (35+) he or she
will buy a more expensive and larger house because of a larger

2. family occupant.
Background and Business Problem
Intermountain West Builders is a company with a
history of quality built homes. This strong background helps
propel them as a premier builder of quality homes. One aspect
of this company is its personable approach to each customer. No
customer is excluded or discounted despite the size of this
company. However, recent trends suggest customer satisfaction
is not as high as the perception suggests. The problem would
take value from the company and further alienate the customer’s
perception of both satisfaction and quality homes built. The
company business model of customer satisfaction should be
reviewed based on feedback generated from surveys.
Research Question
The two variables in question are home age of the
customer and the price. If the customer is older, they are more
likely to pay a higher price for a bigger home. This principal
applies to many other applications, be it purchase of house, any
household item, grocery item, or any other service. Moreover,
a house is a large investment, where it is difficult for a company
to convince the customer to purchase and close the deal.
Intermountain West Builders needs to focus on providing best
quality at affordable prices. A company can always procure
materials at the lowest prices, by bargaining with different
suppliers. In addition, the business can offer a product of high
quality at competitive prices.
The Hypotheses
The research question discussed was based on price and
age. The question inquired was, “Is there a correlation between
prices (DV) based on age of the customer?” Based on the
research question, Intermountain West Builders have formed
two hypotheses:
1. Fair home prices and good customer satisfaction will lead to

3. higher customer service scores.
2. There is a correlation between home price and customer age.
a. The better quality home, the higher the price will become.
Introduction values on home purchases are not expected to
equal the cost nor the price of such purchase. Cost is for labor
and materials, whereas price is the amount you pay for goods or
services. Price can affect value, but will never determine the
value (Folger, n.d.). Quality can be defined in many ways,
based on neighborhood characteristics, structural attributes, as
well as features within (Joseph, 2014) the home. According to
Goodman, 2013, most people will compensate builders more for
better quality, because price is strongly correlated with
encountered problems and the expectations of the client (Kerr,
2015).
Correlation between price and age
A stream of marketing research indicates that price often have
effect on the age bracket of the customer purchasing a home.
Most elderly people view their home as an important asset of
investment. They view it as the asset their children will inherit
and hope it will increase in value. This leads to high number of
older people buying more homes than the younger generation.
They will buy more homes and much expensive home than the
younger people do (Kerr, 2015).
Price and how it affects customer service
Establishing is paramount to the selling price a new home. The
price charged to clients will have a direct effect on the success
of any construction business. The basic rules of pricing are:
· All prices must cover costs and profits.
· The most effective way to lower prices is to lower costs.
· Review prices frequently to assure that they reflect the
dynamics of cost, market demand, response to the competition,

4. and profit objectives.
· Prices must be established to assure sales
Before setting a price, construction businesses must know the
costs of running the business. If the price for the home does not
cover costs the company will exhaust precious financial
resources, and the business will ultimately fail. After setting a
proper price, a product can be floated in market for selling.
Research price and how it affects customer service
The price/quality relationship refers to the perception by most
consumers that a relatively high price is a sign of good quality.
Older people understand the value of the home better than the
young people thus they will spend more on the quality rather
than quality. The belief in this relationship is most important
with complex products that are hard to test, and experiential
products that cannot be tested until used. The greater the
uncertainty surrounding a product, the more consumers depend
on the price/quality hypothesis and the greater premium the
consumers are prepared to pay.
The relationship between customer satisfaction and customer
service
The relationship between customer satisfaction and
customer serves is pronounced. High customer service equals
higher customer satisfaction. A quote from Demand Media
states “one of the most important business traits is learning how
to please your customers” sums the relationship between
customers service and customer satisfaction (Holt, 2015). With
high customer serves comes high customer loyalty as a study
fromDr. Emel Kursunluoglu shows an increase of 43% in
customer loyalty (Kursunluoglu, 2011). With high customer
service comes high customer satisfaction relating to high
customer loyalty and an increase in business with the strongest
form of advertising, word of mouth increasing (Kerr, 2015).
Quality and long lasting effects on customer service scores
Quality is a continued investment that is part of a continuous

5. improvement plan. Quality built into the construction of a new
home will impact the end buyer if each and every defect is
addressed along the way in order to minimize their frequency.
The construction business finds the importance tracking and
maintaining data related to defects, defect frequency of
occurrence, defect origins, defect severity, and defect repair
costs (Rosenfield 109). As a result, the quality built home will
help to improve customer satisfaction as the new owner finds
minimal or zero repairs due to a defect from the construction
process. The only repairs might come from routine care of the
home to ensure prolonged satisfaction from a well-built home.
A quality built home is a long lived in home!
Effects of poor overall service
The effects of poor overall service directly affect the customers’
perceptions in relation to pricing and quality. Akkemans and
Voss (2013) demonstrated this principal in correlating the
effects of the customer satisfaction levels relative to delays and
rework in the supply chain. The negative effects are furthered
bolstered by Ahmed and Kangari, whose research leads to the
conclusion that construction businesses lag behind other
industries that have implemented total quality management
(TQM). Given that customer satisfaction is a key component of
TQM, the client’s perspective of receiving a fair price with an
acceptable level of quality are wanting within the construction
industry as a whole. Finally, Eriksson (2010) tackles the issue
of poor service and the effects by taking a detailed look at the
construction industry by striving to improve the industry from
the client-side versus the more traditional supply-side approach.
All these articles lend credence to the effects of poor overall
service in the construction industry.
Sampling Element
Intermountain Builders determined that the best sampling
element is the survey method. The primary reasoning behind the
decision was budgetary constraints and the time needed to
gather the necessary data. The survey is a cost effective solution

6. requiring little outlay of capital, especially if an electronic
survey service is utilized (i.e. Survey Monkey). Basic services
from electronic surveys are often free and Intermountain
Builders can apply existing client contact information from in-
house data stores, gather public records contact information,
and purchase realtor contact information quickly to round out
the survey targeted participants.
Stratified Sampling Method
The final method of sampling used by the company is
a survey plan. This survey is a questioner based on a number of
questions that rate quality, customer satisfaction and price of
the customer’s home. This survey is a rating system based on
stratified sample. Stratified sample is a sampling technique in
which the entire target population is divided into different
subgroups and then randomly selects the final subjects
proportionally from the different strata (About education,2015).
By subgrouping the population specific areas of interest can be
identified in the research.
Descriptive Statistics
Amount Spent to Buy the Home (in Thousands)
We need some additional analysis here as to the conclusion of
the descriptive statistics. The distribution is normally
distributed.
Central Tendency:
Mean =133.15 ($ in thousands)
Dispersion:
Standard deviation = 3.4462 ($ in thousands)
Count:
100
Min/Max:
Min 75; Max 174 ($ in thousands)
Inferential Statistics

7. The researcher is trying to find the correlation between two
research variables, the amount spent on the house purchase and
the number of rooms in the purchased home. In this study, the
sum paid to buy the house is an independent variable whereas
the number of rooms in the home is the dependent variable. As
mentioned in the week 4’s research paper the researcher
randomly selected 500-population samples to avoid any data
collection bias or sampling errors to reach a decisive outcome.
Samples are measured in the units or numbers. Dependent and
independent variables were determined that are unrelated to the
number of rooms the buyer purchased for the homes. Therefore,
the researcher decided the amount spent on the acquisition of
the home is an independent variable and number of rooms in the
home is a dependent variable because of the customer’s needs;
customers bought the home but there was not a dependency on
the number of rooms in the home.
Therefore, the researcher performs the regression slope analysis
to test the following hypothesis and to reach a definitive
decision that shows that there is a relationship between the
selected variables or not. (There is not a significant linear
relationship between the amount spent on the house purchase
and the age of the purchaser)
(There is an important linear relationship between amount
spent on the house purchase and age of the purchaser in the
purchased home). Below is the regression analysis:
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.079999084
R Square

8. 0.006399853
Adjusted R Square
0.004404672
Standard Error
3.656416932
Observations
500
ANOVA
df
SS
MS
F
Significance F
Regression
1
42.88438082
42.88438082
3.207655515
0.073901618
Residual
498
6657.953619
13.36938478

9. Total
499
6700.838
Coefficients
Standard Error
t Stat
P-value
Intercept
6.108605675
0.368080592
16.59583747
1.47971E-49
X Variable 1
-0.138121065
0.077119829
-1.790992885
0.073901618
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
5.385423388
6.831787962
5.385423388
6.831787962
-0.2896414
0.013399269
-0.2896414
0.013399269
Based on the calculation for the regression, the analysis

10. indicates that there is very weak positive correlation between
the two research variables and correlation coefficient value
0.080. Thus strengthening researcher findings that there is very
weak positive correlation between two research variables.
Furthermore, when the researcher evaluates the data and
identifies that coefficient of determination of 0.004404 proves
that approximate 99.99% of the data cannot be explained by the
collected data. One step further, the researcher tries to
strengthen the findings that there is no relationship between
research variables, regression hypothesis test was performed,
and the results are mentioned in the previous calculations.
Analysis of the slop of the dependent variable for significance
shows that t-stat for the hypothesis is – 1.79099, and p-value is
0..73902. Therefore, there is significant evidence that for the
confidence interval of 99%, 95% or 90%, or for the significance
level of 0.01, 0.05, and 0.10 the null hypothesis can be rejected.
Conclusion
Answers to the research questions
Donald: Per Appendix C, the scatter graph shows no correlation
between the prices of home and the ages of those home buyers.
Older people are not to be assumed for purchasing a more
expensive home. Younger people are also not to be assumed to
buy less expensive homes. Those home buyers between the ages
of 25 and 45 show significant evidence that each age group is
well represented in each home price group; furthering our
conclusion rejecting the null hypothesis.
Ericka Summary of the results of testing the null hypothesis:
Ninety-nine percent of the data collected could not be explained
therefore the null hypothesis was rejected. The regression slope
analysis was used to test the null hypothesis which
demonstrated a weak positive correlation between the two
variables. In addition, the multiple coefficient of determination
.079999 also concluded there to be a weak relationship between
the amount spent on the house and the number of rooms in the
purchased home.

11. Donald Research questions answers:
Ninety-nine percent of the data collected could not be explained
therefore the null hypothesis was rejected. The regression slope
analysis was used to test the null hypothesis which
demonstrated a weak positive correlation between the two
variables. In addition, the multiple coefficient of determination
.079999 also concluded there to be a weak relationship between
the amount spent on the house and the number of rooms in the
purchased home.
1. Donald: Answers to the research questions (this is critical).
2. Diana: Conclusions that were derived from this study
(Paragraph needed, who wants this one?)
1. An inconclusive result (i.e., no significant results were
found) is acceptable for this project. Should that be the case,
suggest potential future research efforts and a new research
questions that might provide more definitive results.
Robert: Recommendations:
The research data is very clear and direct in regards to the
research questions. However, because the relationship proves
to be weak, although positive, it would suggest the parameters
may need to be widened or the variables be revisited. The
research project is clear in what it is attempting to discoverer
and has sound objective. It could be that some other data points
to consider might be economic background, size of family,
number of years married, financial plans for retirement, or any
other variable that may help to discover the end result. The age
factor was an excellent place to compare what one demographic
might pay or perceive quality of home from that of another. An
older age bracket may suggest, as the paper notes, different
objectives and goals or the importance of investment. That
being said, the study has enough data to be tested by others or
for the research team to adjust variables for other test points.

12. 3. Michael: Observations (reflection) on the business problem
and its solution
Michael Observations:
The business problem Intermountain West Builders is
faced with reflects the research the company conducted. The
problem, poor customer satisfaction relates to lower price of
their home. Intermountain West Builders designed research to
confirm this thought building a hypothesis with research
questions around this thinking. The research broke the
hypothesis down into two variables that are the research
questions;
1. Fair home prices and good customer satisfaction will lead to
higher customer service scores.
2. There is a correlation between home price and customer age.
a. The better quality home, the higher the price will become.
The research question was “Is there a correlation between
prices (DV) based on age of the customer?” The solution raise
customer satisfaction and price of the home could be raised. If
customer satisfaction is low or remains unchanged prices of the
home cannot be raised
Russ: Research challenges and future proactive steps
WIP…
Appendix A
Raw data used in the analysis
Appendix B
Charts and Tables

13. Descriptive Statistics on Sales
Mean133.15
Standard Error0.907290338
Median130
Mode130
Standard Deviation17.34462
Sample Variance300.8358586
Kurtosis
14.51105606
SkewnessNaN
Range95
Minimum95
Maximum160
Sum13,315
Count100
Confidence Level(95.0%)8.27
Descriptive Statistics on Age
Mean33.91
Standard Error0.653675699
Median35
Mode25
Standard Deviation6.536756988
Sample Variance42.72919192
Kurtosis-1.41022686
Skewness0.095178927
Range20
Minimum25
Maximum45
Sum3391
Count100
Confidence Level(95.0%)1.297034402

14. Appendix C
Descriptive Statistics
Appendix D
Home Price vs. Ages
Regression Statistics
Multiple R0.052191723
R Square0.002723976
Adjusted R Square-0.00745231
Standard Error9.106647674
Observations100
ANOVA
dfSSMSFSignificance F
Regression122.1988784422.198880.2676787920.606058973
Residual988127.24112282.93103
Total998149.44
CoefficientsStandard Errort StatP-valueLower 95%Upper
95%Lower 95.0%Upper 95.0%
Intercept45.296480774.8344974419.3694292.82949E-
1535.7025779454.89038435.7025779454.8903836
X Variable 1-0.0724411910.1400163-0.517380.606058973-
0.3502989810.2054166-0.3502989810.2054166
$0$20$40$60$80$100$120$140$160$1800510152025Home
Price vs. Age
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