1. Describe two (2) research scenarios where cluster analysis may be used as the data analysis strategy.
A company wants to launch a new product, and it first needs to identify its target market. By conducting a cluster analysis on its customer data (considering variables such as age, income, past purchasing behavior, geographical location, etc.), the company can identify distinct groups of customers who may respond differently to the new product. For instance, a segment may consist of high-income young adults who are early technology adopters, and they can be targeted with specific marketing strategies.
Educational institutions can use cluster analysis to group students based on their performance, learning styles, interests, etc. This can help in personalizing teaching methods, identifying students who may need additional support, and creating effective academic programs.
2. Describe two (2) research scenarios where one way manova may be used as the data analysis strategy.
You could use a one-way MANOVA to understand whether there were differences in students’ short-term and long-term recall of facts based on three different lengths of lecture (i.e., the two dependent variables are “short-term memory recall” and “long-term memory recall”, whilst the independent variable is “lecture duration”, which has four independent groups: “30 minutes”, “60 minutes”, “90 minutes” and “120 minutes”).
A researcher randomly assigns 33 subjects to one of three groups. The first group receives technical dietary information interactively from an on-line website. Group 2 receives the same information from a nurse practitioner, while group 3 receives the information from a video tape made by the same nurse practitioner. The researcher looks at three different ratings of the presentation, difficulty, usefulness and importance, to determine if there is a difference in the modes of presentation. In particular, the researcher is interested in whether the interactive website is superior because that is the most cost-effective way of delivering the information.
3. Describe two (2) research scenarios where linear discriminant analysis may be used as the data analysis strategy.
In the medical field, LDA has a great application in classifying the patient disease on the basis of various parameters of patient health and the medical treatment which is going on. On such parameters, it classifies disease as mild, moderate, or severe. This classification helps the doctors in either increasing or decreasing the pace of the treatment.
In customer identification, LDA is currently being applied. It means with the help of LDA; we can easily identify and select the features that can specify the group of customers who are likely to purchase a specific product in a shopping mall. This can be helpful when we want to identify a group of customers who mostly purchase a product in a shopping mall.
4. Describe two (2) research scenarios where factor analysis may be used as the data analysis strategy.
Factor analysis is used to identify “factors” that explain a variety of results on different tests. For example, intelligence research found that people who get a high score on a test of verbal ability are also good on other tests that require verbal abilities. Researchers explained this by using factor analysis to isolate one factor, often called verbal intelligence, which represents the degree to which someone is able to solve problems involving verbal skills.
Factor analysis is used in fields such as finance, biology, psychology, marketing, operational research, etc. For example, during inquiries about consumer satisfaction with a product, people may respond similarly to questions about that product’s utility, price, and durability.
5. Mr. Smith wants to determine the dimensions, if any, along which certain attitudinal variables may be related to certain health characteristics. The attitudinal variables are attitude toward the role of women (attrole), toward locus of control (control), toward current marital status (attmar), and toward self (esteem). Larger number indicate increasingly conservative attitudes about the proper role of women, increasing feelings of powerlessness to control one’ fate (external as opposed to internal locus of control), increasing dissatisfaction with current marital status, and increasingly poor self-esteem). The health variables are mental health (menheal), physical health (phyheal), number of visits to health professionals (timedrs), attitude toward the use of medication (attdrug), and frequency-duration measure of the use of psychotropic drugs (druguse). Large numbers reflect poorer mental and physical health, more visits to doctors, greater willingness to use drugs, and more use of them. Use the data ‘canondat.sav’ for this problem. Screen the data for normality and multivariate outliers. Perform necessary transformation of the variables, if necessary. Conduct a canonical correlation analysis and determine what dimension(s) of attitude and health variables may be related. Report your results in 2-3 paragraphs, including summary tables.
For question 6, refer to the attached spss data.
6. From the valuegenesis data (valuegendata), determine what might explain the personal religiosity indicators (SET1 of youths (spiritual maturity, commitment to Jesus, and personal devotion). Choose 5-7 variables (SET 2) that may reasonably explain personal religiosity. The goal is to determine the dimensions of SET2 variables that may be related to dimensions of SET1 variables.
Screen the data for violation of assumptions. Perform appropriate transformations, where appropriate. Be sure to explain the bases for your transformations.
Run canonical correlation analysis to determine the nature of the relationships between the two sets of variables.
ComJesus ImpReligion Gender SchType
Length:2315 Length:2315 Length:2315 Length:2315
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
Race Birth_ME Birth_Mother Birth_Dad
Length:2315 Length:2315 Length:2315 Length:2315
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
parstat FathSDA MothSDA GradeLvel
Length:2315 Length:2315 Length:2315 Length:2315
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
baptism howold spiritualmaturity PerDev
Length:2315 Length:2315 Min. :12.00 Min. : 4.00
Class :character Class :character 1st Qu.:35.00 1st Qu.:13.00
Mode :character Mode :character Median :41.00 Median :17.00
Mean :40.78 Mean :17.08
3rd Qu.:47.00 3rd Qu.:21.00
Max. :60.00 Max. :32.00
Grace Works CongClimate LV_Altruim LV_Adventism
Min. : 7.00 Min. : 3.00 Min. :11.0 Min. : 4.00 Min. :2.000
1st Qu.:17.00 1st Qu.: 7.00 1st Qu.:41.0 1st Qu.:11.00 1st Qu.:5.000
Median :22.00 Median :10.00 Median :49.0 Median :12.93 Median :6.000
Mean :21.79 Mean :10.12 Mean :47.8 Mean :12.35 Mean :5.971
3rd Qu.:27.00 3rd Qu.:14.00 3rd Qu.:56.0 3rd Qu.:14.00 3rd Qu.:7.000
Max. :35.00 Max. :15.00 Max. :66.0 Max. :16.00 Max. :8.000
LV_Materialism Den_Loyal AdventStd_Diss AtSchl
Min. :2.000 Min. : 5.00 Min. : 8.00 Min. : 4.00
1st Qu.:3.000 1st Qu.:16.00 1st Qu.:27.00 1st Qu.:15.00
Median :4.000 Median :19.00 Median :35.00 Median :18.00
Mean :4.508 Mean :18.21 Mean :34.47 Mean :16.94
3rd Qu.:6.000 3rd Qu.:21.00 3rd Qu.:42.00 3rd Qu.:20.00
Max. :8.000 Max. :23.00 Max. :64.00 Max. :24.00
FamClim SchClimate QualRelEd SpiritInfluence
Min. : 5.00 Min. : 9.00 Min. : 8.00 Min. : 27.00
1st Qu.:23.00 1st Qu.:22.00 1st Qu.:29.00 1st Qu.: 77.00
Median :26.00 Median :25.00 Median :34.00 Median : 89.00
Mean :25.03 Mean :24.52 Mean :33.71 Mean : 88.22
3rd Qu.:29.00 3rd Qu.:28.00 3rd Qu.:40.00 3rd Qu.:101.00
Max. :30.00 Max. :36.00 Max. :48.00 Max. :135.00
Rate_Church Rate_School IntRelig ExtRelig
Min. :10.00 Min. :10.00 Min. : 7.00 Min. : 7.00
1st Qu.:40.00 1st Qu.:40.00 1st Qu.:23.00 1st Qu.:15.00
Median :49.00 Median :45.00 Median :26.00 Median :18.00
Mean :49.04 Mean :45.51 Mean :25.78 Mean :18.29
3rd Qu.:57.00 3rd Qu.:53.38 3rd Qu.:29.00 3rd Qu.:21.00
Max. :70.00 Max. :70.00 Max. :35.00 Max. :35.00
FrndRel AdventOrtho MAH_1 ProbMah
Min. : 4.00 Min. : 25.0 Min. : 0.7353 Min. :0.000003
1st Qu.:10.00 1st Qu.:128.0 1st Qu.: 4.8931 1st Qu.:0.257340
Median :11.37 Median :138.0 Median : 7.1437 Median :0.521208
Mean :11.37 Mean :134.5 Mean : 7.9965 Mean :0.509567
3rd Qu.:13.00 3rd Qu.:144.0 3rd Qu.:10.1107 3rd Qu.:0.768939
Max. :16.00 Max. :150.0 Max. :40.3615 Max. :0.999432
NA's :16 NA's :16