HYPOTHESES TESTING (10 Marks) Review the definitions of Type I and Type II errors, and answer the following questions. J

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HYPOTHESES TESTING (10 Marks) Review the definitions of Type I
and Type II errors, and answer the following questions. Jason works
for a company that develops and tests new drugs that the company
hopes will treat or cure medical problems. The company is working
on a new drug that company leaders believe will be effective
against leukemia: people’s lives will be saved, and the company
will make money. Which type of error, Type I or Type II, should the
company researchers most vigilantly try to avoid when testing the
effectiveness of the drug? At the same time, the company
researchers should also try to avoid the other type of error.
Explain why. This scenario clearly shows the importance of
balancing the possibility of Type I errors against the possibility
of Type II errors. In this scenario, what are the two best
strategies for achieving this balance? Explain why. Is there a
difference in enrollment in AP classes depending on whether they
are taught in the morning versus the afternoon? A professor wanted
to compare the enrollments in specialized AP seminars (12) taught
by 10 colleagues: 6 who offered their seminars in the morning and
6who offered their seminars in the afternoon. Is there a
difference? Morning enrollments: 12, 9, 9, 10, 8, 8 Afternoon
enrollments: 10, 4, 7, 8, 7, 6 Directions: Calculate the following
manually: Calculate the independent T ratio and calculate the
hypothesis test. Calculate the effect size. Calculate the 0.95 CI.
Write a small interpretive summary of your findings. ANOVA (10
Marks) The data in the Table 1 below (hypothetical) is
self-reported blood sugar levels of workers who are paid in three
different ways: by salary, by hourly, and by commission. Is there a
difference in blood sugar readings among the different payment
methods? Conduct all appropriate tests with the data. Be sure to
discuss whether the assumptions for this procedure were met. Write
a short interpretive summary of your findings. Table 1. Blood
Sugar: Pay Method Data (Self-Reported Blood Sugar Level ) Salary
Hourly Commission 78 99 88 99 108 92 87 111 91 75 114 87 66 106 91
82 120 92 101 135 87 79 100 88 73 97 96 81 95 80 CORRELATION (10
Marks) Data Source https://goo.gl/oaWKgt These data include 18
items asking about satisfaction with various aspects of the hotel
(cleanliness, dining experience, staff, satisfaction with elite
status perks, and so forth), each on a 7 point rating scale. In
addition to the survey responses, the data include each
respondent’s corresponding number of nights stayed at the hotel,
the distance traveled, reason for visiting, their elite membership
level, and the average amounts spent per night on the room, dining,
and WiFi. 1. Visualize the distributions of the variables in the
hotelsatisfaction data. Which of these variables are normally
distributed? Are there variables that might be understood better if
they are transformed? Which variables and what transforms would you
apply? (Suggestion: for efficiency, it may help to divide the data
set into smaller sets of similar variables.) 2. What are the
patterns of correlations in the data? Briefly summarize any
patterns you observe, in 2–4 sentences. 3. Consider just the three
items for cleanliness (satCleanRoom, satCleanBath, and
satCleanCommon). What are the correlation coefficients among those
items? Is there a better measure than Pearson’s r for those
coefficients, and why? Does it make a difference in these data? 4.
Management wants to know whether satisfaction with elite membership
perks (satPerks) predicts overall satisfaction (satOverall). Assume
that satPerks is a predictor and we want to know how satOverall is
associated with changes in it. How do you interpret the
relationship? 5. We might wish to control the previous satPerks
model for other influences, such as satisfaction with the Front
Staff (satFrontStaff) and with the city location (satCity). How do
you change the previous model to do this?Model and interpret the
result. Is the answer different than in the model with only Perks?
Why or why not? REGRESSION (10 Marks) Simple Linear Regression A
researcher is examining the relationship between stress levels and
performance on a test of cognitive performance. She hypothesizes
that stress levels lead to an increase in performance to a point,
and then increased stress decreases performance. She tests 10
participants, who have the following levels of stress: 10.94,
12.76, 7.62, 8.17, 7.83, 12.22, 9.23, 11.17, 11.88, and 8.18. When
she tests their levels of mental performance, she finds the
following cognitive performance scores (listed in the same
participant order as above): 5.24, 4.64, 4.68, 5.04, 4.17, 6.20,
4.54, 6.55, 5.79, and 3.17. Perform a linear regression to examine
the relationship between these variables. What do these results
mean? Report your results in APA style Multiple regression Answers
to selected exercises are downloadable at www.spss-step-by-step.net
Use the helping3.sav file for the exercises that follow
(downloadable at the address shown above). Conduct the following
THREE regression analyses: Criterion variables: 1. thelplnz: Time
spent helping 2. tqualitz: Quality of the help given 3. tothelp: A
composite help measure that includes both time and quality
Predictors (use the same predictors for each of the threedependent
variables): age: Ranges from 17 to 89 angert: Amount of anger felt
by the helper toward the needy friend effict: Helper’s feeling of
self-efficacy (competence) in relation to the friend’s problem
empathyt: Helper’s empathic tendency as rated by a personality test
gender: 1 = female, 2 = male hclose: Helper’s rating of how close
the relationship was hcontrot: Helper’s rating of how controllable
the cause of the problem was hcopet: Helper’s rating of how well
the friend was coping with his or her problem hseveret: Helper’s
rating of the severity of the problem obligat: The feeling of
obligation the helper felt toward the friend in need school: Coded
from 1 to 7 with 1 being the lowest education and 7 the highest
(>19 years) sympathi: The extent to which the helper felt
sympathy toward the friend worry: Amount the helper worried about
the friend in need • Use entry value of .06 and removal value of
.11. • Use stepwise method of entry. I do not care about the
assumptions—just give me the results! Please evaluate this
statement in the context of regression analysis. Do you agree? Are
the assumptions met for Multiple Linear Regression in this case?
What are the findings: omnibus test, omnibus effect size, and
individual predictor significance? Create a table showing for each
of the three analyses Multiple R, R2, then each of the variables
that significantly influence the dependent variables. Following the
R2, list the name of each variable and then (in parentheses) list
its, β value. Rank order them from the most influential to least
influential from left to right. Include only significant
predictors. STRUCTURAL EQUATION MODELING (10 Marks) How does
evaluation of construct validity differ between reflective and
formative measurement models? What advantages in specifying and
executing higher-order models does PLS-SEM have over CB-SEM? From
Table 2, which hypotheses are supported and which ones are not?
Write a short interpretive summary of the findings. Table 2 Results
of Hypothesis Testing Hypotheses Hypothesized Path Path Coefficient
(β) T-Statistics P-Values Results H1 INFQ → PU 0.299 6.359 0.000 H2
INFQ → TRUST 0.006 0.107 0.915 H3 SYSQ → PU 0.106 2.658 0.008 H4
SYSQ → TRUST -0.013 0.284 0.777 H5 SERQ → PU 0.309 6.673 0.000 H6
SERQ → TRUST 0.108 2.105 0.035 H7 PRISK → PU -0.249 6.908 0.000 H8
PRISK → TRUST -0.056 1.267 0.205 H9 PU → TRUST 0.256 4.462 0.000
H10 REP → TRUST 0.268 5.025 0.000 H11 SA → TRUST 0.152 3.093 0.002
H12 SN → TRUST 0.085 1.921 0.055 H13 SP → TRUST -0.016 0.391 0.696
H14 PSC → TRUST -0.043 0.98 0.327 H15 TRUST → SAT 0.345 6.845 0.000
H16 PU → SAT 0.345 6.483 0.000 H17 PU → CI 0.491 11.516 0.000 H18
PSC → CI -0.094 3.509 0.000 H19 TRUST → CI 0.257 5.802 0.000 H20
SAT → CI 0.192 4.866 0.000 Note: * significant at α=5% , **
significant at α = 1%, *** significant at α =0.1% PART B PRACTICAL
ASSESSMENT QUESTIONNAIRE DESIGN (20 Marks) Identify a (new) concept
in your area of specialization, download bibliographic from Scopus,
Web of Science or Pubmed and perform a bibliometric analysis of the
data. From the bibliometric analysis identify the major theories
that have been used in the development of research of the concept
you chose. Also identify some theoretical gaps in the area. You
could also derive these gaps from Special Issue Calls advertised by
Elsevier, Emerald, Springer, Taylor & Francis, Oxford
University Press, Cambridge University Press or Sage Publications
(In which case the Bibliometric analysis may not be necessary)
Identify at least five constructs from the review of theories in
your area of specialization and develop a research model linking
these constructs (keeping in mind the gaps identified). Make sure
at least one of these variables is acting as a mediator and another
acting as a moderator. Develop a questionnaire to measure the
various components and subcomponents of the identified constructs.
Using google docs survey form or any other web survey tool, solicit
responses from at least 300 respondents online. Outline the ex-ante
measure employed to minimize common method variance bias. Using an
appropriate statistical technique, assess whether common method
variance bias is present in the responses. Also, assess
non-response bias DESCRIPTIVE STATISTICS (10 Marks) Describe the
nature of the distribution of the data you have collected. (Perform
appropriate tests to see if the data follows a normal distribution)
Why is the requirement of multivariate normality not such a big
issue when employing PLS-SEM? Explore the correlation between the
variables you employed in your study Report the aggregate Mean and
Standard Deviation of the variables in your model STRUCTURAL
EQUATION MODELLING (20 Marks) (PARTIAL LEAST SQUARES- STRUCTURAL
EQUATION MODELLING) BASIC SEM Report on the reliability,
discriminant validity, and convergent validity of the measurement
model. If there is a concern for any of these, explore the data to
see if it can be fixed. Having assessed the adequacy of the
measurement model, perform the bootstrapping procedure to assess
the Structural Model. Report on the size, sign, and significance of
the path coefficients. Report on the predictive power of the model
using SRMR, Q2. R2, and PLS-Predict. Write a report that summarizes
the findings from both the measurement model assessment and
structural model assessment. ADVANCED SEM Using multi-group
analysis, explore the moderating effect of demographic variables
such as gender on the relationship between the constructs. Explore
the moderating effect of the moderator variable you identified from
above on the relationship between the other variables/constructs.
Assess the mediating role of the mediator you identified in your
model. Is there full mediation or partial mediation Perform
Importance-Performance Map Analysis to determine the most important
factors as well as their performance in predicting the target
variable. Perform Necessary Condition Analysis to identify the
necessary factor in determining the target variable. Present an
extensive write-up on the results from 1-5 under Advanced SEM