Hello, 

I need 20 pages Research Report for Jose’s Southwestern Café. 

This research report is based on the questionnaire and SPSS analysis from Joe’s Southwestern Grill. I’ve attached 2 sample along with SPSS output, background information, and questionnaire. Important is a table of contents (no cover pages) base on the bellow. 

Table of Contents:   

 

1. Background

2. Problem Statement

3. Operational Definitions

4. Research Objectives

5. Research Procedures

6. Hypotheses

7. Research Procedure

8. Limitations

9. Recommendations

Refer back to the questionnaire to determine the research objectives and hypotheses.

This research project utilized the survey method, not focus groups or secondary research. Discuss the different types of survey methods and the advantages and disadvantages of each.  This survey was completed as a mall intercept.

Address sections in the sample Research Report by reading carefully the questions in the survey.

The questions in the survey reveal what they were trying to understand.

Analyzed the data so now based on what you discovered, what should the restaurant executives do?

Train employees to be friendly? Change the menu? Redesign the look of the restaurant?

Those are just a couple of things to think about. 

Every survey question got a response and told us something.   

So for each of the questions you answered for the SPSS project, what do you recommend?

1.What is the mean for each (not combined)

A) X7 – 4.70 which means that self-confidence in their future is not 100%, not close to it’s “strongly agree” choice.

B) X12 – 3.81 which means that customers of Mexican restaurant don’t think 100% that employees are friendly, however the numbers are close to an agreement choice than not.

C) X17 – 4.64 which represents customer’s opinion as a disagreement of an attractive interior design.

D) X22 – 5.33 is very satisfied answer of many customers on the scale.

2. Compare and contrast the means of two groups. Are males OR females less likely to buy a new product? Explain fully your conclusion. Don’t guess. Support your answer by providing the mean that was computed.

Females are less likely to buy a new product. The mean of 5.49 in a female group is lower in buying a new product than in male’s group with mean which was 5.91. Hence man a little more likely to purchase a new product.

3. Correlation: Explain fully the concept of correlation between variables. Based on the questionnaire implemented and the SPSS outputs, does the Pearson Correlation reveal that there is a high or low correlation between the level of satisfaction and the likelihood to return to a favorite Mexican restaurant? What was the Pearson Correlation computed to be? For example .4, .6, .73, .85, or 1.0? Don’t guess. Explain fully.

Correlation measures a relationship between variables which measured in a numeric representation of relation. Pearson’s data is at 0.584 of likely to return with 0.584 satisfaction which means there is a very strong relation with the satisfaction and likelihood to return to the restaurant, the variables are correlated.

4. What does the multiple regression reveal about the ability of fun, size, taste, and service to predict customer satisfaction? Don’t guess. What are the beta coefficients for each? Explain fully.

In multiple regression it shows that there is an increase of customer’s satisfaction if there is satisfaction with fun, size, taste and service in the restaurants.

The beta confidence has shown the fun place to eat is at confidence at 0.1

18

which means that per every 1 increase of customers there is an increase of 0.118 confidence satisfaction.

The beta confidence in a large size portion has shown a confidence of 0.139 which means that per every 1 increase satisfied customer there is an increase of 0.139 satisfaction.

The beta confidence in an excellent food taste has shown a confidence of 0.234 which means that per every 1 increase satisfied customer there is an increase of 0.234 satisfaction.

The beta confidence in a speed of service has shown a confidence of 0.188 which means that per every 1 increase satisfied customer there is an increase of 0.188 satisfaction.

SPSS outputs

FREQUENCIES VARIABLES=x22

/ORDER=ANALYSIS.

Frequencies

Statistics

X22 — Satisfaction

N

Valid 325

Missing 1

X22 — Satisfaction

Frequency Percent Valid Percent Cumulative Percent

Valid 3 22 6.7 6.8 6.8

4 93 28.5 28.6 35.4

5 58 17.8 17.8 53.2

6 60 18.4 18.5 71.7

7 = Highly Satisfied 92 28.2 28.3 100.0

Total 325 99.7 100.0

Missing System 1 .3

Total 326 100.0

FREQUENCIES VARIABLES=x22

/STATISTICS=MEAN MEDIAN MODE

/ORDER=ANALYSIS.
Frequencies
Statistics

X22 — Satisfaction

N Valid 325
Missing 1

Mean 5.33

Median 5.00

Mode 4

X22 — Satisfaction
Frequency Percent Valid Percent Cumulative Percent
Valid 3 22 6.7 6.8 6.8
4 93 28.5 28.6 35.4
5 58 17.8 17.8 53.2
6 60 18.4 18.5 71.7
7 = Highly Satisfied 92 28.2 28.3 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0
FREQUENCIES VARIABLES=x22
/STATISTICS=MEAN MEDIAN MODE

/BARCHART FREQ

/ORDER=ANALYSIS.
Frequencies
Statistics
X22 — Satisfaction
N Valid 325
Missing 1
Mean 5.33
Median 5.00
Mode 4
X22 — Satisfaction
Frequency Percent Valid Percent Cumulative Percent
Valid 3 22 6.7 6.8 6.8
4 93 28.5 28.6 35.4
5 58 17.8 17.8 53.2
6 60 18.4 18.5 71.7
7 = Highly Satisfied 92 28.2 28.3 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0

FREQUENCIES VARIABLES=x7

/STATISTICS=MEAN MEDIAN MODE
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X7 — Self-Confident

N Valid 325
Missing 1

Mean 4.70

Median 5.00

Mode 5

X7 — Self-Confident
Frequency Percent Valid Percent Cumulative Percent

Valid 2 14 4.3 4.3 4.3

3 43 13.2 13.2 17.5

4 68 20.9 20.9 38.5

5 113 34.7 34.8 73.2

6 74 22.7 22.8 96.0

Strongly Agree 13 4.0 4.0 100.0

Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0

FREQUENCIES VARIABLES=x12

/STATISTICS=MEAN MEDIAN MODE
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X12 — Friendly Employees

N Valid 325
Missing 1

Mean 3.81

Median 4.00

Mode 5
X12 — Friendly Employees
Frequency Percent Valid Percent Cumulative Percent

Valid Strongly Disagree 5 1.5 1.5 1.5

2 70 21.5 21.5 23.1

3 35 10.7 10.8 33.8

4 87 26.7 26.8 60.6

5 128 39.3 39.4 100.0

Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0

FREQUENCIES VARIABLES=x17

/STATISTICS=MEAN MEDIAN MODE
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X17 — Attractive Interior

N Valid 325
Missing 1

Mean 4.64

Median 5.00
Mode 5
X17 — Attractive Interior
Frequency Percent Valid Percent Cumulative Percent

Valid 2 6 1.8 1.8 1.8

3 45 13.8 13.8 15.7

4 77 23.6 23.7 39.4

5 134 41.1 41.2 80.6

6 59 18.1 18.2 98.8

Strongly Agree 4 1.2 1.2 100.0

Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0
FREQUENCIES VARIABLES=x22

/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM

/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics
X22 — Satisfaction
N Valid 325
Missing 1

Std. Deviation 1.331

Variance 1.771

Range 4

Minimum 3

Maximum 7

X22 — Satisfaction
Frequency Percent Valid Percent Cumulative Percent
Valid 3 22 6.7 6.8 6.8
4 93 28.5 28.6 35.4
5 58 17.8 17.8 53.2
6 60 18.4 18.5 71.7
7 = Highly Satisfied 92 28.2 28.3 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0

FREQUENCIES VARIABLES=x7
/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X7 — Self-Confident

N Valid 325
Missing 1

Std. Deviation 1.196

Variance 1.431

Range 5

Minimum 2

Maximum 7
X7 — Self-Confident
Frequency Percent Valid Percent Cumulative Percent
Valid 2 14 4.3 4.3 4.3
3 43 13.2 13.2 17.5
4 68 20.9 20.9 38.5
5 113 34.7 34.8 73.2
6 74 22.7 22.8 96.0
Strongly Agree 13 4.0 4.0 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0
FREQUENCIES VARIABLES=x12
/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X12 — Friendly Employees

N Valid 325
Missing 1

Std. Deviation 1.212

Variance 1.470

Range 4

Minimum 1

Maximum 5

X12 — Friendly Employees
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 5 1.5 1.5 1.5
2 70 21.5 21.5 23.1
3 35 10.7 10.8 33.8
4 87 26.7 26.8 60.6
5 128 39.3 39.4 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0
FREQUENCIES VARIABLES=x17
/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM
/BARCHART FREQ
/ORDER=ANALYSIS.
Frequencies
Statistics

X17 — Attractive Interior

N Valid 325
Missing 1

Std. Deviation 1.029

Variance 1.059

Range 5
Minimum 2
Maximum 7

X17 — Attractive Interior
Frequency Percent Valid Percent Cumulative Percent
Valid 2 6 1.8 1.8 1.8
3 45 13.8 13.8 15.7
4 77 23.6 23.7 39.4
5 134 41.1 41.2 80.6
6 59 18.1 18.2 98.8
Strongly Agree 4 1.2 1.2 100.0
Total 325 99.7 100.0
Missing System 1 .3
Total 326 100.0

CROSSTABS

/TABLES=x2 BY x32

/FORMAT=AVALUE TABLES

/CELLS=COUNT

/COUNT ROUND CELL.

Crosstabs

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

X2 — Party Person * X32 — Gender 325 99.7% 1 0.3% 326 100.0%

X2 — Party Person * X32 — Gender Crosstabulation

Count

X32 — Gender Total

Male Female

X2 — Party Person 2 6 3 9

3 34 18 52

4 87 41 128

5 64 33 97

6 11 16 27

Strongly Agree 6 6 12

Total 208 117 325

CROSSTABS
/TABLES=x2 BY x32
/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ

/CELLS=COUNT EXPECTED COLUMN

/COUNT ROUND CELL.
Crosstabs
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
X2 — Party Person * X32 — Gender 325 99.7% 1 0.3% 326 100.0%

X2 — Party Person * X32 — Gender Crosstabulation
X32 — Gender Total
Male Female

X2 — Party Person 2 Count 6 3 9

Expected Count 5.8 3.2 9.0

% within X32 — Gender 2.9% 2.6% 2.8%

3 Count 34 18 52

Expected Count 33.3 18.7 52.0

% within X32 — Gender 16.3% 15.4% 16.0%

4 Count 87 41 128

Expected Count 81.9 46.1 128.0

% within X32 — Gender 41.8% 35.0% 39.4%

5 Count 64 33 97

Expected Count 62.1 34.9 97.0

% within X32 — Gender 30.8% 28.2% 29.8%

6 Count 11 16 27

Expected Count 17.3 9.7 27.0

% within X32 — Gender 5.3% 13.7% 8.3%

Strongly Agree Count 6 6 12

Expected Count 7.7 4.3 12.0

% within X32 — Gender 2.9% 5.1% 3.7%

Total Count 208 117 325

Expected Count 208.0 117.0 325.0

% within X32 — Gender 100.0% 100.0% 100.0%

Chi-Square Tests

Value df Asymptotic Significance (2-sided)

Pearson Chi-Square 8.472a 5 .132

Likelihood Ratio 8.121 5 .150

Linear-by-Linear Association 3.435 1 .064

N of Valid Cases 325

a. 2 cells (16.7%) have expected count less than 5. The minimum expected count is 3.24.

ONEWAY x9 BY x32

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS

.

Oneway

Descriptives

X9 — Buy New Products

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum

Maximum

Lower Bound Upper Bound

Male 208 5.91 1.495 .104 5.71 6.12 1 7

Female 117 5.49 1.627 .150 5.19 5.79 2 7

Total 325 5.76 1.555 .086 5.59 5.93 1 7

ANOVA

X9 — Buy New Products

Sum of Squares df Mean Square F Sig.

Between Groups 13.607 1 13.607 5.710 .017

Within Groups 769.673 323 2.383

Total 7

83.280 324

ONEWAY x24 BY x33

/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS

/POSTHOC=SCHEFFE ALPHA(0.05).

Oneway
Descriptives

X24 — Likely to Recommend

N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum
Lower Bound Upper Bound

No Children at Home 148 4.87

1.535 .126 4.62 5.12 1

1-2 Children at Home 95 4.72

1.478 .152 4.41 5.02 1

More Than 2 Children at Home 82 5.13

1.538 .170 4.80 5.47 1

Total 325 4.89 1.523 .084 4.73 5.06 1

Descriptives
X24 — Likely to Recommend
Maximum

No Children at Home 7

1-2 Children at Home 7

More Than 2 Children at Home 7

Total 7
ANOVA
X24 — Likely to Recommend
Sum of Squares df Mean Square F Sig.

Between Groups 7.819 2 3.910 1.693 .186

Within Groups 743.412 322 2.309

Total 751.231 324

Post Hoc Tests

Multiple Comparisons

Dependent Variable: X24 — Likely to Recommend

Scheffe

(I) X33 — Number of Children at Home (J) X33 — Number of Children at Home Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval

Lower Bound Upper Bound

No Children at Home 1-2 Children at Home .156 .200 .738 -.34 .65

More Than 2 Children at Home -.263 .209 .456 -.78 .25

1-2 Children at Home No Children at Home -.156 .200 .738 -.65 .34

More Than 2 Children at Home -.418 .229 .190 -.98 .14

More Than 2 Children at Home No Children at Home .263 .209 .456 -.25 .78

1-2 Children at Home .418 .229 .190 -.14 .98

Homogeneous Subsets

X24 — Likely to Recommend

Scheffea,b

X33 — Number of Children at Home N Subset for alpha = 0.05

1

1-2 Children at Home 95 4.72
No Children at Home 148 4.87
More Than 2 Children at Home 82 5.13

Sig. .147

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 101.770.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

CORRELATIONS

/VARIABLES=x22 x23

/PRINT=TWOTAIL NOSIG

/STATISTICS DESCRIPTIVES

/MISSING=PAIRWISE.

Correlations

Descriptive Statistics

Mean Std. Deviation N

X22 — Satisfaction 5.33 1.331 325

X23 — Likely to Return 4.53 1.148 325

Correlations

X22 — Satisfaction X23 — Likely to Return

X22 — Satisfaction Pearson Correlation 1 .584**

Sig. (2-tailed) .000

N 325 325

X23 — Likely to Return Pearson Correlation .584** 1

Sig. (2-tailed) .000

N 325 325

**. Correlation is significant at the 0.01 level (2-tailed).

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT x22

/METHOD=ENTER x13 x14 x18 x21.

Regression

Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 X21 — Speed of Service, X14 — Large Size Portions, X18 — Excellent Food Taste, X13 — Fun Place to Eatb . Enter

a. Dependent Variable: X22 — Satisfaction

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .422a .178 .168 1.214

a. Predictors: (Constant), X21 — Speed of Service, X14 — Large Size Portions, X18 — Excellent Food Taste, X13 — Fun Place to Eat

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 102.239 4 25.560 17.346 .000b

Residual 471.534 320 1.474

Total 573.772 324

a. Dependent Variable: X22 — Satisfaction

b. Predictors: (Constant), X21 — Speed of Service, X14 — Large Size Portions, X18 — Excellent Food Taste, X13 — Fun Place to Eat

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 1.898 .498 3.808 .000

X13 — Fun Place to Eat .118 .079 .082 1.490 .137

X14 — Large Size Portions .139 .057 .132 2.417 .016

X18 — Excellent Food Taste .234 .064 .191 3.673 .000

X21 — Speed of Service .188 .034 .292 5.604 .000

a. Dependent Variable: X22 — Satisfaction

18

Background

Jose’s Southwestern Cafe

Celebrating 20 years of business in 2019,

Joe’s Southwestern Cafe

owner Juan Morales is committed to quality foods and customer service and is conducting marketing research to better understand customers.

Morales was born and raised in the Mesilla Valley and knows what local residents want. After nurturing the former Joe’s Southwestern Cafe restaurant on Foster Road, which opened in 1989, Morales opened the first Joe’s Southwestern Cafe in 1999. One year later, Morales was running three grills. Today, the corporation operates 11 grills throughout southern New Mexico, including Las Cruces and Hatch, NM. When it comes to quality Southwestern cuisine that’s fresh, hot and fast, Joe’s Southwestern Cafe is the answer.

The menu choices are plenty and appeal to any diet. Joe’s Southwestern Cafe offers low-carb options, such as shrimp and steak items as well as salads. The grills also offer traditional Mexican shrimp cocktail, philly cheese steaks and hot ham and cheese sandwiches to curb your hunger.

Joe’s Southwestern Cafe creates a new contemporary interpretation of Southwest-style bar & grill cuisine, infusing indigenous foods such as squash, corn, pintos & chiles with fresh local spices and condiments The restaurant’s striking atmosphere and reasonable pricing have quickly established it as a popular spot with locals and visitors alike.

An open kitchen reveals a long bank of grills and open flame broilers, bustling our tradition-inspired creations of grilled steaks, meats & fresh seafood. A gas-fired rotisserie roasts seasoned whole chickens. Homemade red & green chile sauces spice traditional enchiladas while serrano, poblano, & jalapeno chilies, along with cilantro, garlic and onions bring to life fish & steak tacos, pastas, soups and sandwiches. The Prime-aged “baseball cut” sirloin comes served with poblano chile rajas and red chile onion rings.

Interior design incorporates the hand-tooled Bar and authentic furniture by Esteban Chapital of Puebla, Mexico. Antique copper and ceramic pots are displayed above vibrant still life paintings created byartist, Ricardo Montes Salcido of Guadalajara. Click here for a virtual tour.

The bar at Joe’s Southwestern Cafe seats fifteen, while bancos and small tables tuck guests into surrounding corners to meet with friends. The back bar, constructed from old mesquite doors and covered with a stamped tin ceiling, provides an authentic backdrop to enjoy a margarita. Eight draft beers are available on tap featuring local & regional microbrews. Sixteen wines are available by the glass. Our tequila and Margarita list offer the classic Mexican & Santa Fe cocktails, featuring a broad selection of Mexico.

Joe’s Southwestern Cafe

CCoolllleeggee SSttuuddeennttss && BBrreeaakkffaasstt

Research Report

prepared for

Rise & Shine Corp.

December 2012

By George Bush, President

Bush Consulting Group

Background

On February 14, 1998, David Michael Anthony set out on a quest that would

change not only his life, but also the lives of millions of hungry people around the world.

In an attempt to raise money for World Hunger Year, this thirty-three year old engineer

cycled on his bike over 26,560 miles across 44 states pulling a trailer weighing 1,200

pounds. Anthony raised a total of 2.5 million dollars for world hunger by agreeing to

attach a sponsor sign on his tailor for anyone who donates $50,000 to a hunger

organization. His contribution towards world hunger was phenomenal.

Have you ever wondered what motivates people to take action like Anthony to

help great causes such as world hunger? Even the most passionate individuals will

sometimes not initiate a potential resolution. This is because an individual’s attitude

does not necessarily coincide with his/her behavior. Factors such as accessibility or

ease of donating, awareness, and financial status all effect donation behaviors.

Similarly gender and class sometimes have differing donation behavior and

attitudes.

Studying these constituents, can be an important aid for changing the current trends on

donation.

Organizations such as the American Red Cross and World Vision preach, “Even

a small monetary donation from an individual could greatly impact the life of a starving

person.” Unfortunately, many people are simply unaware of the extent of world hunger.

Secondary data proves that world hunger is an issue that must sometime be seriously

addressed. For example, it has been proven that every 3.6 seconds someone dies of

hunger (think quest). In addition, the U.S. does not rank high when it comes to

providing assistance to the hungry people in other lands. We have a “stingy mixed

record on poverty-focused foreign aid” (Foreign aid and world hunger). In 1997, Jeff

Ambers of Yorkville Common Pantry quotes, “It used to be families coming (to pantries)

once or twice a month. Now over half of the families are coming four times a month”

(Ridgeway 40). The problem is by no means getting better and needs to be addressed.

However, how can an individual take action without jumping on a bicycle and peddling

cross country, and how can the next generation such as college students, take a more

active role at eliminating world hunger? In an attempt to answer some of these

questions a team of highly qualified experts at Lake Forest College conducted a

research project on donations for world hunger.

Problem Statement

In a research project “the problem must ask about the relationship between two

or more variables” (Wunsch 1). In addition it clearly identifies the purpose of the project.

The problem statement for this research project is stated below.

The problem of the study is to compare the donation attitudes and

behaviors between classes and gender with regard to world hunger

among college students at Lake Forest College.

Operational Definitions

Prior to the experiment two operational definitions were defined to make the

research more precise. The first definition was “donation.” Setting a monetary value on

the description of donation is necessary for classifying donors in the survey and focus

group. Therefore, the operational definition is given below

Donation – a gift given by the donator in the form of food or money with a

monetary value above $5.00.

The second word that required clarification is “awareness.” In this research project

awareness is used with regard to an individuals’ knowledge of contribution distribution.

As a result, awareness was defined as follows:

Awareness – knowledge of an individuals’ nationality, country, race, or condition,

which their donation will assist.

Research Objectives

Although the problem statement defines the purpose of the project, Wunsch also

admits “a single research project can be designed to answer more than one question”

(1). These questions are called objectives. The objectives for this research project are

stated below.

O1: Do people believe that their donation to world hunger will help the

problem?

O2: Do males or females tend to donate more to world

hunger?

O3: Do freshmen or seniors tend

to donate more to

world hunger?

O4: Does awareness affect an individual’s attitude or behavior toward world

hunger?

O5: Does financial status affect donation habits?

O6: What motivates people to donate?

O7: How can donating become easier for

college students?

O8: What messages and media should be used to encourage donating among

college students?

Hypotheses

A hypothesis is “a conjectural statement about a relationship between two or

more variables that can be tested with empirical data” (McDanials and Gates 28). This

research project is designed to answer the following hypotheses.

H1: No significant difference exists between males and females with regard to

donation

attitudes.

H2: No significant difference exists between freshmen and seniors with regard

to donation attitudes.

H3: No significant difference exists between males and females with regard to

donation habits.

H4: No significant difference exists between freshmen and seniors with regard

to donation habits.

Research Procedures

Secondary Data

One important advantage to secondary data is that it “may provide primary data

research method alternatives” (McDanials and Gates 84). For example, before the

study was conducted at Lake Forest College, the researchers were able to examine

other studies that might offer a better method for testing the variable. Examining a

study in which produces inconsistent or inadequate results is a warning sign for the

researchers telling them to possibly use an alternative testing method. Another major

advantage of secondary data is that it may help clarify the problem (McDanials and

Gates 84). A team of professors at Brown University conducted a study in 1996 to

address the long-term problems of world hunger. These professors compared the

number of hungry people counted between the years of 1992 and 1994 to the number

of hungry people counted between 1994 and 1996. Their results proved that in three of

the five countries there was an increase in the number of starving people and an overall

increase in hungry people worldwide. The results discovered by Brown University

supports Jeff Ambers’ analysis of hunger based on the increased number of hungry

families. When viewed together, this data redefines the problem and gives plausibility

to the project at hand.

Interviews

Interviews are can be a major factor in a research project for two main reasons.

Interviews permit open-ended questions, which allow the interviewee to give an in-depth

response. Interviews are also particularly useful at the beginning of a research project

when exploratory research is conducted to find out more information on the topic. The

research project conducted at Lake Forest College implemented a total of three

interviews. One interview was given by e-mail to Karen Ryerson, an employee of the

American Red Cross. (See Appendix) The research team hoped to gain specific facts

and trends about donating from the perspective of an expert. The second interview was

administered to Les Dlabay, a frequent donator to world hunger. (See Appendix) His

specific knowledge concerning the best method of donating to world hunger could help

the research team design valid survey questions.

Finally, the last interview was completed with Karen Hermann. (See Appendix)

Hermann is the advisor of Athletic Council, an organization, which promotes student

involvement in athletic events at Lake Forest College. As a community service project,

Athletic Council sponsors a food drive to benefit Libertyville Township Food Pantry. As

an organization on campus the research team was interested in finding the ways in

which the Lake Forest College community is involved in donating to world hunger.

When asked how Athletic Council gets students to participate, Hermann quotes, “We

hang signs in the cafeteria and around commons and we also hand out flyers at the

football game at the game prior to the collection.” However, the response to the drive

by the students is not always successful. Karen says, “Most of our donations come

from the parents at the games.” Responses such as these once again helped the

research group formulate survey and focus group questions in which the students

themselves could provide information on ways to increase involvement.

Focus Group

Besides interviews, another way to obtain qualitative information on a subject is

to conduct a focus group. According to McDanials and Gates, “A focus group consists

of 8 to 12 participants who are led by a moderator in an in-depth discussion on one

particular topic or concept” (111). The world hunger research team held a focus group

with the intent of discovering students’ attitudes and behaviors towards donating to

world hunger. Two members of the group acted as moderators while the other member

recorded the data.

The focus group was took place in a suite in Deerpath, in an informal

atmosphere. Verifying that each participant is comfortable can be an essential

component in order to obtain involuntary information. The ten members of the focus

group represented different ages, sex, and donation behaviors. For example, two of the

ten participants were senior males while one member was a freshman female. Since

the focus of the research project is to essentially study the donation attitudes and

behaviors between males and females and freshman and seniors, it is helpful that these

categories of people are included in the discussion. Similarly, at least three members of

our focus group identified themselves as contributors to world hunger by stating that

they have participated in or helped organize food drives.

Several bits of information were obtained from the focus group, which helped the

world hunger team create a conclusive survey in which to obtain quantitative data. (See

Appendix) Most importantly, of the ten people in the focus group only one could identify

an organization on campus where they knew they could go to donate for world hunger.

This proves the awareness at Lake Forest College is low.

The research group also obtained creative ideas from students concerning new

ways Lake Forest College could increase student involvement, which Hermann said

was lacking. When asked, “How can donating become easier for you?” a senior male

responded, have “One day where you donate your dinner money to the hungry. No

eating in the café one day.” Another great idea mentioned at the focus group was

donating extra flex dollars at the end of the semester to feed the hungry. In response to

these answers, the research team further investigated these ideas on their research

instrument.

Survey

By compiling different ideas and opinions from the interviews and focus group,

the research group was able to devise a survey, which focused on students’ attitudes

and behaviors about world hunger. (See Appendix) Kellerman and Thoms advise,

“Determine what information needs to be gathered: select the appropriate question type

to elicit the desired information; and choose a format that is easy to read” (38). The

world hunger group formatted the questions in order to meet the research objectives

established by the group. Also, prior to distribution, the survey was pre-tested. In order

to test the validity of the survey instrument, the instrument was read to a team of

experts in Professor Dlabay’s class. Validity “addresses the issue of whether what we

tried to measure was actually measured” (McDanials and Gates 258). Responses and

reactions were noted from the experts to aid the research team in improving its survey.

In order to obtain information about the entire population, a sample size must be

defined prior to the project. The population in the sample for world hunger consisted of

freshman and senior, male and female students at Lake Forest College since the

hypotheses aim to compare donation attitudes and behaviors among these groups.

McDanials and Gates point out that ideally, the population sample from which

information is obtained “should be a representative cross section of the total population”

(328).

The surveys were administered to a total of forty students. This included ten

male and ten female freshman and ten male and ten female seniors. These students

were randomly selected around campus through personal contacts and distribution in

highly populated areas such as the cafeteria and resident halls. Because participants of

the survey were selected randomly in a convenient fashion, a sample frame was not

necessary. Such a frame would include a full list of all senior and freshman students of

Lake Forest College. Instead, the research group chose to conduct a nonprobability

sample, or specifically, a convenient sample. This sampling technique is helpful

because it is “easy to collect” (McDanials and Gates 247). One hundred percent of the

surveys administered were returned, making for an efficient data collection method.

Findings and Conclusions

The results of this project are applicable to the Lake Forest College community.

Clubs and organizations interested in food drives or world hunger such as Athletic

Council, will obtain first class ideas, which have been presented by actual Lake Forest

College students. The conclusions will also be beneficial to larger nation wide hunger

organizations such as World Hunger and the American Red Cross, whose continual

mission is to increase donations. The findings can alert such organizations of current

trends in donation among college students and possibly assist in new campaigns.

Maybe these companies should allocate more expenditure on ad campaigns specifically

geared towards college students. This study could help settle debates on these issues.

Also, although all world hunger organizations have the same goal, it is important to

remember that they are still in competition with one another. The information obtained

during this research project, especially through focus group discussions, in-depth

interviews, as well as open-ended questions on the survey instrument, might provide a

new creative idea that will result in a boost of business above a competitor.

Volunteering is a service that can be compared to donating to world hunger.

Both activities are services provided by individuals wishing to improve the lives of other

less fortunate people. Michael Gerson reports “20 percent of volunteers say they have

cut back because they weren’t sure there work helped” (30). Such is the case with

donating because if people feel their donations are not making a difference, they will not

donate.

Objective 1 ~ Do people believe that

their donation to world hunger will help

the problem?

Hypothesis 1 ~ No significant

difference exists between males and

females with regard to donation

attitudes.

Hypothesis 2 ~ No significant

difference exists between freshman

and seniors with regard to donation

attitudes.

Objective one addresses the individuals’ attitudes towards donations for world

hunger. The first step to increasing the total donations received is to convince people

their donation will impact society. The research group surveyed the sample and

recorded response to the statement “one person can make a difference in our society.”

The opinion on this issue among males and females were as follows:

Survey item #7 N SA (5) A (4) N (3) D (2)

SD

(1) X

Males 20 4 12 4 0 0 4.0

Females 20 6 13 1 0 0 4.25

Among seniors and freshmen, the opinions were:

Survey item #7 N SA (5) A (4) N (3) D (2)
SD
(1) X

Seniors 20 7 12 1 0 0 4.3

Freshmen 20 3 13 4 0 0 3.95

A mean score of 4.0 indicates that people tend to agree with the statement. This was

the case in all four categories, but the senior and females ranked the highest in

approval. Therefore, this data does not indicate that the problem of world hunger is

linked with the attitude that a small donation will not make a difference.

A survey by the New York City Coalition Against Hunger reported that due to lack

of food, in one summer, “73,000 people were turned away from emergency food

programs” (Ridgeway 40). In order to improve the current donation trend, it is important

to grasp the donation behavior of the individuals of interest: male and female, freshman

and seniors at Lake Forest College. Objective two attempts to answer the research

groups’ third hypothesis.

Objective 2 ~ Do males or females tend

to donate more to world hunger?

Hypothesis 3 ~ No significant

difference exists between males and

females with regard to donation habits.

The donation behavior of males and females was measured by survey question three.

The number (n) is out of the twenty surveyed and represents the number who

responded “yes.”

Survey item #3 Females (n) Females (%) Males (n) Males (%)

Have you ever donated $5 or

more to an organization that 8 40% 6 30%

provides assistance for

World Hunger?

These data tend to indicate that females donate more than males. Only thirty percent of

the males surveyed have donated compared to forty percent of the females survey. In

fact, Ryerson of the American Red Cross says, “Females make up the majority of

donors to Red Cross, although the average gift is higher among males.” Since this

study did not measure the average gift by males and females, a comparison cannot be

made about the average size of the gift. However, the research results do agree with

Ryerson’s claim that more women than men are likely to donate.

This research project is also interested in discovering the donation behavior

between freshman and seniors as expressed in objective three.

Objective 3 ~ Do freshman or seniors

tend to donate more to world hunger?

Hypothesis 4 ~ No significant

difference exists between freshman
and seniors with regard to donation

habits.

Although Hermann says “it is hard to guess as to whether freshman or seniors donate

more” at Lake Forest College, the survey results for this study revealed that a larger

majority of seniors donate when compared to freshman. In fact, almost half of the total

seniors surveyed admitted they have donated to world hunger. These results are

shown below:

Survey item #3 Freshman (n) Freshman (%) Seniors (n) Seniors (%)

Have you ever donated $5 or

more to an organization that 5 25% 9 45%

provides assistance for
World Hunger?

Prior to the research project, awareness was defined to be “knowledge of an individuals’

nationality, country, race, or condition, which their donation will assist.” Many people

are skeptical of the size of the proportion of their donation that is going directly to feed

the hungry. As a result, objective four remained on the research teams list.

Objective 4 ~ Does awareness affect an individual’s attitude or behavior toward

world hunger?

Les Dlabay, a frequent donator to world hunger, has been donating $22 a month

to World Vision. He sponsors one child in El Salvador and another in Gana. Despite

some negative criticism by the Tribune, he believes that World Vision is a “good strong

organization and accountable.” However, Ryerson did not offer numbers when asked,

“Do people know when they are donating, where their money is going and if it will be

sent to the right person or place?” Instead, she claims that most of donated money is

“used for disasters that are getting less media attention, but just as critical for those

involved.”

A recent study found that the amount of money given to charity to feed the

hungry has dropped. Schwartzberg quotes, “While the poor get poorer not because

they’re unemployed but because they can’t survive on what their jobs pay them – fewer

American households are giving anything to charity, and those that do are wiring

smaller checks” (36). Objective five addresses this issue.

Objective 5 ~ Does financial status affect donation habits?

In the focus group conducted for this study, on person commented, “If a person feels it

is important enough to donate, then they will. There are many wealthy people who do

not donate at all.” However, Ryerson of the American Red Cross says, “People with

greater resources give larger gifts, and people with less wealth give a greater

percentage of their income.”

In an effort to eliminate financial stability as a factor in discussing donations to

world hunger “The Hunger Site” has agreed to donate a days worth of rice or maize to

the “United Nations World Food Programme” for every visitor to their website. This

enables those financially unstable individuals the ability to “give without giving” (36).

Objective 6 ~ What motivates people to donate?

In the focus group conducted for this study, several members of the group

expressed that the reason they donate to world hunger was for self-satisfaction. One

member said, “I got to help those people that were in need and less fortunate than me.”

However, this is not the only reason that causes people to donate. Survey results from

this study reveal that most people don’t feel that money raised for world hunger should

be used to assist only U.S. allies, as shown in the following tables:

Survey item #8 N SA (5) A (4) N (3) D (2) SD (1) X

Money raised for world

hunger should be used 40 5 3 16 10 6 2.775

only to assist US allies.

Because the mean score is below three, people tend to slightly disagree with the

statement. People sometimes are motivated to donate based on where and to whom

their donation is assisting. These results indicate that people will tend to donate less if

their money were to assist only U.S. allies.

Objective 7 ~ How can donating become easier for college students?

Survey results from this study also reveal that ease of donating is also an

important issue at Lake Forest College. Males especially believe that donating to world

hunger is not very convenient. The results are shown below:

Survey item #5 –

males N SA (5) A (4) N (3) D (2)

SD

(1) X

Making donations at

Lake

Forest College for

World 40 2 6 5 7 0 3.15

Hunger is

convenient.

Women on the other hand, tended to have more agreement with the statement, and

therefore don’t find donating at Lake Forest College as inconvenient as the men. The

results for the females are shown below:

Survey item #5 –

females N SA (5) A (4) N (3) D (2)

SD
(1) X
Making donations at
Lake
Forest College for

World 40 1 7 3 8 1 2.95

Hunger is
convenient.

It is important to note that the mean score for both men and women are very close to

neutral. This data indicates that there is definite room for improvement when it comes

to accommodating students.

In the focus group conducted for this study, a participant offered a creative idea,

which could possibly increase total donations given by Lake Forest College students.

This response also specifically addressed the teams objective seven. At the beginning

of each semester, students have a predetermined amount of “flex dollars” on their

student identification, which can be used to purchase food at one of the three coffee

houses located around campus. One member of the focus group quoted, “Extra flex

dollars can be donated towards world hunger.” This idea sparked an interest in the

group, which not only caused further discussion about the topic but also convinced the

research group to highlight this suggestion when developing their survey.

Survey item #4

Freshman

(n)

Freshman

(%) Seniors (n) Seniors (%)

Would you be willing to

donate

your extra flex dollars left

over at 20 100% 19 95%

the end of the semester

towards

World Hunger if under

$5.00?

Of the total sample surveyed, 97.5% stated that they would be willing to donate

their extra flex dollars to help eliminate world hunger. In addition, the research group

also discovered that 68 % (25 of 37) of students would rather donate to world hunger

via campus organizations. This data shows that students wish to donate at Lake Forest

College as opposed to a world hunger organization. One student said that she would

rather donate to a campus organization because, “It would promote community

involvement.” However, convenience is still an issue. Another student stated that she

would donate “on campus if it is convenient.”

Objective 8 ~ What messages and media should be used to encourage donating

among college students?

Athletic Council was not the only organization at Lake Forest College, which

sponsored food drives. During National Hunger and Homeless Awareness Week, the

Community Service Coalition and Kappa Alpha Psi Fraternity, Inc. presented an annual

food drive to help those who are less fortunate during the upcoming holiday season.”

Like Athletic Council this drive targeted participation by the students and used signs and

flyers in order to increase awareness. (See Appendix) However, evidence from the

focus group concludes that their technique to gain awareness is not effective.

By reviewing all the data collected during the project, the research team was able

to design some better promotional suggestions applicable to Lake Forest College

community, as well as to world hunger organizations. One major issue discovered

during the project was the inconvenience of donating. Organizations or clubs at Lake

Forest College looking to increase the total donations from the students should address

this issue first. The study revealed that not only do the female and senior students think

that donating on campus is more convenient in comparison to the male and freshman,

but also they tended to donate more. As a result, the research team believes that a

campaign aimed to accommodate the freshman and male students would be a great

idea. Populate the all male and all freshman dorms with signs about donating.

Including donation sites not only in the male and freshman dorms but also in every dorm

on campus, which would increase the convenience of donating and accommodate more

people. One student felt that “dorm competitions” would be a good way to promote

donating for world hunger. Another student suggested that an organization could “hold a

contest between dorms and give a prize to the winner.” A competition between

freshman and seniors or males and females could possibly increase the donations by

the freshman and males also. (See Appendix for a possible flyers)

Karen Ryerson of the American Red Cross comments that “several campaigns

over the last several years have specifically targeted this group (students). One was

called Generation X.” World hunger organizations such as the American Red Cross

might consider another campaign specifically targeting males, since only 30% of the

males surveyed had ever donated $5. This approach would attempt to increase

donations by convincing the males they should donate as often as the females.

Limitations

Several weaknesses of the research project should also be noted. First, as

Wunch points out in a survey “a representative sample has the same characteristics of

the population, and the data that the sample provides is the same data that the

population would have provided had the total population been surveyed” (31).

However, during this research project for world hunger, this was not the case. Every

participant in the study was a student at Lake Forest College. It is also hard to conclude

that Lake Forest is a good representative college, since the average tuition for the

school is higher and the average student body population for the school is lower than

other schools.

Another flaw in the project was that due to a time constraint the team chose to

use a small sample size. One way to validate a survey by increasing the confidence

level, decrease the allowable error, or create a diverse study, is to increase the total

number in the sample. The research group also used a convenient sample when

choosing their participants. A convenient sample is “a non-probability sample used

primarily because data are easy to collect.” Surveys were administered at random to

people that were willing to participate. This creates a weakness in the study because

the team is taking the chance that the sample randomly chosen will represent the

population.

Some flaws in research projects are hard to avoid and impossible to eliminate.

For example, one weakness is that respondents to the survey are limited by responses

based on the wording of the instrument. If one person has an opinion that is not offered

as a selection on the survey, he/she must compromise and select a different option.

For example, if one student preferred donating to a local food drive rather than online, a

world hunger organization, or a campus organization, he/she would have not choice but

to select one of the alternative choices. Therefore, the survey instrument is not truly

measuring this person’s opinion on donating. Another weakness that is hard to

eliminate is the validity of the respondents’ remarks. In the world hunger case, the team

must take for granted that Les Dlabay (a frequent donator to world hunger) is an expert

in this field. The information that he provides is considered the correct and valid

information, even though this might not be true.

One final flaw in this project is that although the team properly set up the

procedures that were needed for drawing conclusions, they did not statistically analyze

their data. Given more time and better resources the team would have also been able

to calculate the results and make better comparisons.

Recommendations

1. To increase the total donations given by students at Lake Forest College

students should be offered the option of donating their “flex dollars” to world

hunger at the end of the semester.

2. To encourage students to donate on the Lake Forest College campus action

should be taken to organize an inter-dorm, “battle of the sexes”, or “battle of the

classes” competition.

3. To increase the total donations world hunger organizations should receive they

should target males and freshmen in college.

4. In future studies, researchers should consider a survey to compare the average

gift among males and females to see if Karen Ryerson’s claim that the average

gift is higher among males.

5. Conduct a similar project but by using a stratified random sample instead of a

convenient sample in order to obtain more accurate results.

6. Conduct a similar project but by choosing a sample which could be generalized

to the whole population.

Conclusion

This research project was hopefully able to open the eyes of some members of

the Lake Forest community who are too busy to take time to help a worthy cause. The

project also aimed to provide world hunger organizations as well as campus

organizations with information regarding the opinions and donation trends of students,

which is useful when organizing a campaign to increase total donations. The longer

people wait to take action, the worse the world hunger problem will become. This

research project proved that donating to help such an important cause could be more

convenient, but it is by no means difficult. It only takes a fraction of David Anthony’s

ambition to make a difference in the life of a starving person.

REFERENCES

“Act of bike love.” Bicycling v41. June 2000: 30.

“Foreign aid and world hunger.” America 3 185, no. 4. August 2001: 3.

Gerson, Micheal J. “Do Do-Googers Do Much Good?” U.S. News & World Report.

April 1997: 26-37.

Kellerman, Debra K. and Karen J. Thoms. “But It’s Only a Questionnaire.” Business

Education Forum. (1996): 36-38.

McDanials, Carl and Rodger Gates. Marketing Research Essentials. University of

Texas

@ Arlington, 2001

Ridgeway, James. “Feeding Desperate People.” Source Village Choice v42n50.

December 1997: 40.

Schwartzberg, Jason. “Virtually Selfless.” Village Voice. December 1999: 36.

Think Quest. Home page. Retrieved October 6, 2001 from the World Wide Web

Wunsch, Daniel R. “How to evaluate research as a research consumer.” Instructional

Strategies – An applied Research Series. (1991): 1-5.

http://www.thinkquest.org/

TA

B

LEO

F

CO

N

TENTS

BACKG

R

OUND

PROBLEM STATEMENT

OPERATIONAL DEFINITIONS

RESEARCH OBJECTIVES

HYPOTHESES

RESEARCH PROCEDURES

LIMITATIONS

SECONDARY RESEARCH FINDINGS

PRIMARY RESEARCH FINDINGS

RECOMMENDATIONS

CONCLUSION

APPENDIX SPSS OUTPUT

BACKGROUND

In early

2

0

0

4

,

t

wo recent college business graduates (one majored in finance and the other in management) came together with a new restaurant concept for a Southwestern casual dining experience that focused on a Mexican theme with a variety of good food items and a friendly family-oriented atmosphere

.

After six months of planning and creating detailed business and marketing plans, the two entrepreneurs were able to get the necessary capital to build and open their restaurant- calling it Santa Fe Grill Mexican Restaurant.

After the initial six months of success, the noticed that revenues, traffic flow, and sales were declining and realized that they knew only the basics about their patrons. Neither of the owners had taken any marketing courses beyond basic marketing in college, so they turned to a friend who worked in marketing for some advice. Initially they were advised to hire a marketing research firm to collect some primary data about people’s dining out habits and patterns. Looking into marketing research consulting firms, they quickly found out these firms wanted too much money to conduct the research. So they went to Barnes & Noble bookstore and purchased a practitioner’s book on how to do marketing research studies. Using their understanding of how to do research and design questionnaires, the owners decided to use an experience intercept research design (randomly stopping customers as they were leaving Santa Fe Grill), with trained interviewers to qualify the respondents using a set of three screening questions, and a

3

5

question, self-administered survey to actually collect the data.

The report mainly focuses on analyzing the data from Santa Fe Grill Restaurant with the means of quantitative analysis to identify Santa Fe Grill’s competitive advantages.

Mean

while, via analyzing the psychographic/demographic profile of Santa Fe Grill’s customer, it aims to assess the customer’s willingness to return to the restaurant in the future. Through comparative studying on Santa Fe Grill, to determine the characteristics customers use to describe the Santa Fe Grill restaurant and then further to find out the Santa Fe Grill’s address areas for improvement. And then, provide reasonable recommendations to improve Santa Fe Grill’s business performance.

PROBLEM STATEMENT

In a research project, “the problem must ask about the relationship between two or more variables” (Wunsch

1

). In addition, it clearly identifies the purpose of the project. The problem statement for this research project is stated below:

The problem of this study is to determine the level of satisfaction of the customers with their favorite Mexican restaurant (Santa Fe Grill restaurant). Also the factors that influenced their level of satisfaction

OPERATIONAL DEFINITIONS

MEAN: A mean is the simple mathematical average of a set of two or more numbers. The mean for a given set of numbers can be computed in more than one way, including the ARITHMETIC MEAN method, which uses the sum of the numbers in the series, and the GEOMETRIC MEAN method.

MEDIAN: A median is the middle number in a sorted list of numbers. To determine the median value in a sequence of numbers, the numbers must first be arranged in value order from lowest to highest. If there is an odd amount of numbers, the median value is the number that is in the middle, with the same amount of numbers below and above.

MODE:

A statistical term that refers to the most frequently occurring number found in a set of numbers. The mode is found by collecting and organizing the data in order to count the frequency of each result. The result with the highest occurrences is the mode of the set

STANDARD DEVIATION: Standard deviation is a measure of the dispersion of a set of data from its mean. If the data points are further from the mean, there is higher deviation within the data set. Standard deviation is calculated as the square root of variance by determining the variation between each data point relative to the mean.

VARIANCE:

Variance

is a measurement of the spread between numbers in a data set. The variance measures how far each number in the set is from the mean.

SATISFACTION: fulfillment of one’s wishes, expectations, or needs, or the pleasure derived from this.

RESEARCH OBJECTIVES

Although the problem statement defines the purpose of the project, Wunsch also admits “a single research project can be designed to answer more than one question”. These questions are called objectives. The objectives for this research project are stated below.

• To identify the factors people deem important in making casual dining restaurant choice decisions.

• To develop a psychographic/demographic profile of Santa Fe Grill’s customer base.

• To determine the patronage and positive word of mouth advertising patterns toward the Santa Fe Grill Mexican Restaurant.

• To assess the degree to which the customer is satisfied with their Santa Fe Grill restaurant experience.

• To assess the likelihood of the customer’s willingness to return to the Santa Fe Grill in the future.

• To determine the characteristics that customers use to describe the Santa Fe Grill Mexican Restaurant.

HYPOTHESIS

Hypothesis is when a proposition is formulated for empirical testing. As a declarative statement about the relationship between two or more variables, a hypothesis is of a tentative and conjectural nature. Hypothesis have also been described as statements in which we assign variables to cases.

HYPOTHESIS 1:

There is a relationship between Fresh foods and the level of customer satisfaction

HYPOTHESIS 2:

There is a relationship between friendly employees and the level of customer satisfaction.

HYPOTHESIS 3:

There is a relationship between number of children in a household of customers and the level of recommendation to others.

RESEARCH PROCEDURES

SECONDARY DATA

These are data that have had at least one level of interpretation inserted between the event and its recording. One important advantage to secondary data is that it ‘may provide primary data research method alternatives’ (McDanials and Gates 84). For example, for this study, I was able to examine other studies that might offer a better method for testing the variable. Examining a study in which produces inconsistent or inadequate results is a warning sign for the researchers telling them to possibly use an alternative testing method. Another major advantage of secondary data is that it may be useful in clarifying the problem.

FOCUS GROUPS

Focus groups became widely used in research during the 1980s and are used for increasingly diverse research applications today. 11 A focus group is a group of people (typically

6

to 10 participants), led by a trained moderator, who meet for 90 minutes to 2 hours. The facilitator or moderator uses group dynamics principles to focus or guide the group in an exchange of ideas, feelings, and experiences on a specific topic.

A study room in the Brooklyn college Library café was used for the focus group. Verifying that each participant is comfortable can be an essential component in order to obtain involuntary information. The ten members of the focus group represented different ages, sex, food preferences and racial backgrounds. Their various experiences at the Santa Fe Grill restaurant was the topic of discussion.

SELF ADMINISTERED SURVEYS

Nowhere has the computer revolution been felt more strongly than in the area of the self-administered survey. Computer-delivered self –administered questionnaires use organizational intranets, the internet, or online services to reach their participants. Intercept surveys at malls, conventions, state fairs vacation destinations, even busy city street corners- may use a traditional paper-and-pencil questionnaire or a computer-delivered survey via a kiosk. The respondent participates without interviewer assistance, usually in a predetermined environment such as a room in a shopping mall.

In order to obtain information about the entire population, a sample size must be defined prior to the project. The surveys were administered to a total of four hundred and five customers. For this research, it was decided to use an experience intercept research design (randomly stopping customers as they were leaving Santa Fe Grill), with trained interviewers to qualify the respondents using a set of three screening question. One hundred percent of the surveys administered were returned, making for an efficient data collection method.

LIMITATIONS

Some limitations were encountered during the completion of this research. Firstly, some of the customers that came into the restaurant were unwilling to fill the survey on their way out because they were in a hurry and some of them were running late to other places.

Secondly, there was a limited sample size as not all the customers of Santa Fe Grill restaurant took part in the survey. At least 2000 customers come into Santa Fe grill restaurant monthly but our sample size was

405

. For this reason, it is difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution and to be considered representative of groups of people to whom results will be generalized or transferred.

Another limitation to the study is self-reported data. This study is limited by the fact that the data rarely can be independently verified. I had to take what the respondents and focus group participants said at face value. Self-reported data can contain several potential sources of bias.

Fourthly, time constraint was a limitation faced during this project. The time given for this project was small and a vastly comprehensive research with more facts couldn’t be carried out.

Lastly, the limited history of the project is another limitation that was encountered during the research. Not a vast amount of research has previously been carried out on this topic.

SECONDARY RESEARCH FINDINGS

One of the secondary research findings is that the competitive advantages of Santa Fe Grill mainly lies in its product quality providing new and different foods, relaxed environment involving a fun place to eat and large size portions as well as knowledgeable employees. Moreover, the most important factors influencing people’s dining decision mainly include price, food quality and service.

Secondly, it was found out that the customers had a positive judgment about the restaurant’s operations. They painted the good image of the customers

PRIMARY RESEARCH FINDINGS

• A mean of

  3.24

  means that the average respondent chose that they occasionally eat at their favorite Mexican restaurant with a little amount leaning towards eating somewhat infrequently at their favorite Mexican restaurant

• A median of 3 means that after arranging the responses gotten from participants in either ascending or descending other, the middle value is 3 which means that the average respondent chose that they occasionally eat at their favorite Mexican restaurant.

• A mode of 3 means that most of the respondents reported that they occasionally ate at their favorite Mexican restaurant.

• A standard deviation of

  1.118

  shows that there has been a large deviation or distance from the mean of 3.24. The data points far away from the mean, on average. This means the values in the data set are farther away from the response of the respondents occasionally eating at their favorite Mexican restaurant.

• Variance of 1.25 tells us that the level of the values spread out of the mean is 1.25

Statistics

X25 — Frequency of Eating at . . . ??

N

Valid

405

Missing

0

Mean

3.24

Median

3.00

Mode

3

Valid

12.8

12.8

17.3

24.9

22.5

91

22.5

22.5

405

100.0

100.0

		X25 — Frequency of Eating at . . . ??
				Frequency											Percent					Valid Percent					Cumulative Percent
		Very Infrequently			52									12.8
		Somewhat Infrequently			7 0						17.3			30.1
		Occasionally			101						24.9			55.1
		Somewhat Frequently						91												22.5			77.5
		Very Frequently															100.0
																Total

Statistics

N

Valid

405

Missing

0

3

	X22 — Satisfaction
					Std. Deviation			1.118
	Variance		1.251
	Range					4
				Minimum
				Maximum													7

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

3

9.4

9.4

4

36.5

23.5

23.0

7.7

100.0

Total

405

100.0

100.0

				X22 — Satisfaction
	38						9.4
	148				36.5		45.9
	5		95				23.5		69.4
	6		93				23.0		92.3
	7 = Highly Satisfied		31				7.7

In conclusion, we found out that most of the Families occasionally eat at the Santa Fe Restaurant. The mean, mode and median are all close to 3, the standard deviation of 1.18 and the variance of 1.25 hence we can conclude that most of the values are close to the mean, the values doesn’t spread out too much.

Another finding during the course of this research were the two most important factors that led to the increase in customer satisfaction;

Friendly employees: the multiple regression analysis reveals that for every increase in Friendly employees, there will be a 0

.281

increase in satisfaction.

Fresh food: the multiple regression analysis reveals that for every increase in fresh food, there will be a 0

.390

increase in satisfaction.

.000

.000

.178

.000

	Coefficientsa
				Model		Unstandardized Coefficients		Standardized Coefficients		t							Sig.
	B						Std. Error		Beta
												1		(Constant)		-.128		.298		-.429		.668
	X12 — Friendly Employees		.281		.037		.304		7. 59 6																.000
	X15 — Fresh Food		.390		.038		.417		10.346
	X16 — Reasonable Prices				.17 8		.035		.197		5.041
	X17 — Attractive Interior		.195		.042		4.617
			a. Dependent Variable: X22 — Satisfaction

Lastly, according to our analysis, it shows that the mean of households with 2 or more children is

4.45

, the household with 1-2 children at home has a mean of

4.28

and also, the household with no children has a mean of

3.13

.

In conclusion, the household with 2 or more children are the most likely to recommend their favorite Mexican restaurant to their friends because they have the highest mean amongst the three groups.

N

Mean

Std. Deviation

Std. Error

Minimum

Maximum

7

2

7

3

7

Total

405

2

7

		Descriptives
				X24 — Likely to Recommend
		95% Confidence Interval for Mean
			Lower Bound				Upper Bound
					No Children at Home			190			3.13		1.052		.0 76		2.98		3.28													2
					1-2 Children at Home			107			4.28		1.204		.116		4.05		4.51
					More Than 2 Children at Home			108			4.45		.754		.073		4.31		4.60
			3.79				1.199			.060			3.67				3.90

RECOMMENDATION

Providing options for portion sizes should be considered and added to the menu. Different portions for lunch and dinner items may include; salads, sandwiches, and soups. A menu item can be selected based on a full size or half size portion. A lower price can be attributed to the smaller portion sizes in order to reflect the difference. This will appease those customers who are looking to eat something light and give more options. Second recommendation is that the advertisement should be kept to a minimal amount. Using alternative methods of advertisement to lower the cost would be beneficial to the restaurants bottom line. Word of mouth and free or lower cost alternatives will work the best in spreading the word of the quality and specials offered. Lastly, a healthy selection guide on the menu will appeal to the customers that are health conscious. Having a small label or star next to healthy items as well as a nutrition breakdown would be beneficial and appealing to the customers that are health conscious.

Overall, the Santa Fe Grill is operating a sound business that can use an added boost to capture and maintain a newer customer base. Taking in consideration the conclusion and recommendations of the research data and implementing them into their service would greatly benefit the restaurant and the customers.

The areas that the owners of Santa Fe Grill should focus on are; competitive analysis, new product planning, and integrated marketing communications. Competitive analysis will give the owners insight on their competitors; it will clue them in on what is happening and what they are competing against. New product planning will explore the possibilities of new menu items and feedback on the positive and negatives of the items. Integrated marketing communication will help get the Santa Fe Grill’s name and business out to the public and help generate new customers.

CONCLUSION

In conclusion to the survey and data collected there are several different aspects the owners of the Santa Fe Grill should take into consideration. The portion sizes of the meals are very important to the customers of the restaurant, customers of the Santa Fe Grill do not patronage based on advertisement, and customers are careful on what they select to eat off the menu based on their age.

REFERENCES

Baidu. Retrieved November 05, 2016 from the World Wide Web

http://wenku.baidu.com/view/72111b6cf5335a8102d2204d.html

Bush Consulting Group “College Students & Breakfast: Research report prepared for Rise & Shine Corp.”, (2012)

Cox, Ashley. “Executive Briefing Marketing Research”, (2012)

Graeff, Timothy. “Marketing Research for Managerial Decision Making”, (2006)

Libguides. Retrieved from the World Wide Web November 06, 2016

McDanials, Carl and Rodger Gates. Marketing Research Essentials. University of Texas

@ Arlington, 2001

Wunsch, Daniel R. “How to evaluate research as a research consumer.” Instructional

Strategies – An applied Research Series. (1991): 1-5.

APPENDIX A-RESEARCH METHODS

INTERNAL VALIDITY: internal validity factors cause confusion about whether the experimental treatment (X) or extraneous factors are the source of observation differences. Do the conclusions we draw about a demonstrated experimental relationship truly imply cause?

EXTERNAL VALIDITY: This is concerned with the interaction of the experimental treatment with other factors and the resulting impact on the ability to generalize to (and across) times, settings, or persons. Does an observed causal relationship generalize across persons, settings and times?

• Random sampling:  is the basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample.

• Randomization: does not guarantee that if a pretest of the groups was conducted before the treatment condition, the groups would be pronounced identical; but it is an assurance that those differences remaining are randomly distributed.

• Matching: employs a nonprobability quota sampling approach the object of matching is to have each experimental and control subject matched on every characteristic used in the research

Experiments are studies involving intervention by the researcher beyond that required for measurement. The usual intervention is to manipulate some variable in a setting and observe how it affects the subjects being studied

ADVANTAGES

• The researcher’s ability to manipulate the independent variable

• Contamination from extraneous variables can be controlled more effectively than in other designs.

• The convenience and cost of experimentation are superior to other methods

• Repeating an experiment with different subject groups and conditions leads to the discovery of an average effect of the independent variable across people, situations and times

• The researchers can use naturally occurring events and to some extent, field experiments to reduce subjects’ perceptions of the researcher as a source of intervention or deviation in their everyday lives

Nowhere has the computer revolution been felt more strongly than in the area of the self-administered survey. Computer-delivered self –administered questionnaires use organizational intranets, the internet, or online services to reach their participants. Intercept surveys at malls, conventions, state fairs vacation destinations, even busy city street corners- may use a traditional paper-and-pencil questionnaire or a computer-delivered survey via a kiosk. The respondent participates without interviewer assistance, usually in a predetermined environment such as a room in a shopping mall.
ADVANTAGES

• Costs: self-administered surveys of all types typically cost less than surveys via personal interviews. This is true of mail surveys, as well as of both computer-delivered and intercept surveys.

• Sample accessibility: one asset to using mail self-administered surveys is that researchers can contact participants who might otherwise be inaccessible.

DISADVANTAGES

• Time constraint: although intercept studies still pressure participants for a relatively quick response, in a mail survey, the participant can take more time to collect facts, talk with others or consider replies at length than is possible in a survey employing the telephone or in a personal interview

• Topic coverage: a major limitation of self-administered surveys concerns the type and amount of information that can be secured. Researchers normally do not expect to obtain large amounts of information and cannot probe deeply into topics.

The telephone survey is still the workhorse of survey research. With the high level of telephone service penetration in the United States and the European Union, access to participants through low cost, efficient means has made telephone interviewing a very attractive alternative for researchers. Pollsters working with political candidates use telephone surveys to assess the power of a speech or a debate during a hotly contested campaign

ADNAVANTAGES OF TELEPHONE RESEARCH

• Moderate cost: one study reports that sampling and data collection costs for telephone surveys can run from 45 to 64 percent lower than costs for comparable personal interviews. Much of the savings comes from cuts in travel costs and administrative savings from training and supervision.

• Faster completion of study: when compared to either personal interviews or mail self-administered surveys, the use of telephones brings a faster completion of a study, sometimes taking only a day or so for the fieldwork.

• Reduction of Bias: when compared to personal interviewing, it is also likely that interviewer bias, especially bias caused by the physical appearance, body language and actions of the interviewer, is reduced by using telephones.

• Behavioral norms: also, behavioral norms work to the advantage of telephone interviewing. If someone is present, a ringing phone is usually answered, and it is the caller who decides the purpose, length and termination of the call.

DISADVANTAGES OF TELEPHONE RESEARCH

• Inaccessible households: telephones may be considered as one of the prime methodology for communication studies. However, several factors reduce such an enthusiastic embrace of the methodology. Rural households and households with incomes below the poverty line remain underrepresented in telephone studies.

• Limitation on interview length: a limit of interview length is another disadvantage of the telephone survey, but the degree of this limitation depends on the participant’s interest in the topic. Ten minutes has generally been thought of as ideal, but interviews of 20 minutes or more are not uncommon.

• Ease of interview termination/; some studies suggest that the response rate in telephone studies is lower than that for comparable face to face interviews. One reason is that participants find it easier to terminate a phone interview.

• Less participant involvement: telephone surveys can result in less thorough responses and persons interviewed by phone find the experience to be less rewarding than a personal interview.

• Inaccurate or non-functioning numbers: one source says the highest incidence of unlisted numbers is in the west, in large metropolitan areas, among nonwhites, and for persons between 18 and 34 years of age. Several methods have been developed to overcome the deficiencies of directories; among them are techniques for choosing phone numbers by using random dialing or combination of directories and random dialing.

Participant. With the poor eyesight of an interviewer and the problems of question clarity, a personal interview, rather than the intercept/self-administered questionnaire, is the preferable method for communication.

ADVANTAGES

• Depth of information/; the greatest value lies in the depth of information and detail that can be secured. It far exceeds the information secured from telephone and self-administered studies via mail or computer.

• Quality of information: the interviewer can also do more things to improve the quality of the information received than is possible with another method.

• Control: human interviewers also have more control than other kinds of communication studies. They can prescreen to ensure the correct participant is replying and they can set up and control interviewing conditions

DISADVANTAGES

• Cost: a survey via personal interview may cost anywhere from a few dollars to several hundred dollars for an interview with a hard-to-reach person. Costs are particularly high if the study covers a wide geographic area or has stringent sampling requirements

• Changes in social climate: many people today are reluctant to talk with strangers or to permit strangers to visit in their homes. Interviewers are reluctant to visit unfamiliar neighborhoods alone, especially for evening interviewing.

• Bias; results of surveys via personal interviews can be affected adversely by interviewers who alter the questions asked or in other ways bias the results.

• Nominal scale: in business research, nominal data are widely used. With nominal scales, you are collecting information on a variable that naturally or by design can be grouped into two or more categories that are mutually exclusive and collectively exhaustive. This can be used for determination of quality for example Gender (male.

  Female

  )

• Ordinal scale: include the characteristics of the nominal scale plus an indication of order. Ordinal data require conformity to a logical postulate, which states: if a is greater than b and b is greater than c then a is greater than c. the use of ordinal scale implies a statement of “greater than” or “less than” without saying how much greater or less. This can be used for determination of greater or lesser value. For example, Category of Professors

• Interval scale: have the power of nominal and ordinal data plus one additional strength: they incorporate the concept of equality of interval (the scaled distance between 1 and 2 equals the distance between 2 and 3) calendar time is such a scale. This can be used for determination of equality of intervals or differences. For example, temperature in degrees

• Ratio scale: incorporate all of the powers of the previous scales plus the provision for absolute zero or origin. Ratio data represent the actual amounts of a variable. This is used for the determination of equality of ratios. For example, age In years

• THE RESPONDENT: opinion differences that affect measurement come from relatively stable characteristics of the respondent. Typical of these are employee status, ethnic group membership, social class and nearness to manufacturing facilities. The skilled researcher will anticipate many of these dimensions, adjusting the design to eliminate, neutralise or otherwise deal with them. Respondents may be reluctant to express strong positive (or negative)feelings, may purposefully express attitudes that they perceive as different from those of others, or may have little knowledge about KBC but be reluctant to admit ignorance. Respondents may also suffer from temporary factors like fatigue, boredom, anxiety, hunger, impatience; these limit the ability to respond accurately or fully

• SITUATIONAL FACTORS: Any condition that places a strain on the interview or measurement session can have serious effects on the interviewer-respondent rapport. If another person is present, that person can distort responses by joining in, by distracting, or by merely being there.

• THE MEASURER: the interviewer can distort responses by rewording, paraphrasing, or reordering questions. Stereotypes in appearance and action introduce bias. Inflections of voice and conscious or unconscious prompting with smiles, nods and so forth, may encourage or discourage certain replies.

• THE INSTRUMENT: A defective instrument can cause distortion in two major ways. First it can be too confusing and ambiguous. Secondly, there can be poor selection from the universe of content items.

VALIDITY: is the extent to which a test measures what we actually wish to measure.

• Content validity: the content validity of a measuring instrument is the extent to which it provides adequate coverage of the investigative questions guiding the study

• Criterion-related validity: this reflects the success of measures used for prediction or estimation. You may want to predict an outcome or estimate the existence of a current behavior or time perspective

• Construct Validity: in attempting to evaluate construct validity, we consider both the theory and the measuring instrument being used. If we were interested in measuring the effect of trust in cross-functional teams, the way in which ‘trust’ was operationally defined would have to correspond to an empirically grounded theory

RELIABILITY: has to do with the accuracy and precision of a measurement procedure. A measure is reliable to the degree that it supplies consistent results.

• Stability: a measure is said to possess stability if you can secure consistent results with repeated measurements of the same person with the same instrument.

• Equivalence: this is concerned with variations at one point in time among observers and samples of items

• Internal consistency: this uses only one administration of an instrument or test to assess the homogeneity among the items

PRACTICALITY: is concerned with a wide range of factors of economy, convenience and interpretability

• Economy: some trade-off usually occurs between the ideal research project and the budget.

  Data

  are not free and instrument length is one area where economic pressures dominate.

• Convenience: a measuring device passes the convenience test if its easy to administer.

• Interpretability: this aspect of practicability is relevant when persons other than the test designers must interpret the results.

• LIKERT SCALE: consists of a series of statements, and the participant is asked to agree or disagree with each statement. Summation is possible with this scale although not necessary and in some instances undesirable

• SEMANTIC DIFFERENTIAL SCLAE: measures the psychological meanings of an attitude object. Researcher use this scale for studies of brand and institutional image.

• STAPEL SCALE: IS USED AS AN ALTERNATIVE TO THE SEMANTIC DIFFERENTIAL, ESPECIALLY WHEN IT IS DIFFCULT TO FIND BIPOLAR ADJECTIVES THAT MATCH THE INVESTIGATIVE QUESTION.

• NUMERICAL SCALES: have equal intervals that separate their numeric scale points. Verbal anchors serve as the labels for the extreme points. Numerical scales are often 5-point scales but may have 7 or 10points.

• MULTIPLE RATING SCALE: is similar to the numerical scale but accepts a circled response from the rater, and the layout allows visualization of the results.

• CONSTANT-SUM SCALE: This scale helps the researcher discover proportions. The participant distributes 100 points among up to 10 categories

• GRAPHIC RATING SCALE: was originally created to enable researchers to discern fine differences. Raters check their response at any point along a continuum.

• SIMPLE CATEGORY SCALE: offers two mutually exclusive response choices

• MULTIPLE-CHOICE, SINGLE RESPONSE SCLAE: offers the rater several options, including ‘other’

• MULTIPLE-CHOICE, MULTIPLE-RESPONSE SCALE: allows the rater to select one or several alternatives, thereby providing a cumulative feature.

MEAN: A mean is the simple mathematical average of a set of two or more numbers. The mean for a given set of numbers can be computed in more than one way, including the ARITHMETIC MEAN method, which uses the sum of the numbers in the series, and the GEOMETRIC MEAN method.

MEDIAN: A median is the middle number in a sorted list of numbers. To determine the median value in a sequence of numbers, the numbers must first be arranged in value order from lowest to highest. If there is an odd amount of numbers, the median value is the number that is in the middle, with the same amount of numbers below and above.
MODE: What is a ‘Mode’

A statistical term that refers to the most frequently occurring number found in a set of numbers. The mode is found by collecting and organizing the data in order to count the frequency of each result. The result with the highest occurrences is the mode of the set

STANDARD DEVIATION: Standard deviation is a measure of the dispersion of a set of data from its mean. If the data points are further from the mean, there is higher deviation within the data set. Standard deviation is calculated as the square root of variance by determining the variation between each data point relative to the mean.

VARIANCE: Variance is a measurement of the spread between numbers in a data set. The variance measures how far each number in the set is from the mean.
We got the following results after analyzing our data:

MEAN: 3.24

MEDIAN: 3.00

MODE: 3.00

STANDARD DEVIATION: 1.118

The question used in analyzing these was QUESTION 25: How often do you eat at your favorite Mexican restaurant with a response scale of 1=Very infrequently, 2= Somewhat infrequently, 3=Occasionally, 4=Somewhat infrequently, 5=Very frequently.

A mean of 3.24 means that the average respondent chose that they occasionally eat at their favorite Mexican restaurant with a little amount leaning towards eating somewhat infrequently at their favorite Mexican restaurant

A median of 3 means that after arranging the responses gotten from participants in either ascending or descending other, the middle value is 3 which means that the average respondent chose that they occasionally eat at their favorite Mexican restaurant.

A mode of 3 means that most of the respondents reported that they occasionally ate at their favorite Mexican restaurant.

A standard deviation of 1.118 shows that there has been a large deviation or distance from the mean of 3.24. The data points far away from the mean, on average. This means the values in the data set are farther away from the response of the respondents occasionally eating at their favorite Mexican restaurant.

Variance of 1.25 tells us that the level of the values spread out of the mean is 1.25

In conclusion, we found out that most of the Families occasionally eat at the Santa Fe Restaurant. The mean, mode and median are all close to 3, the standard deviation of 1.18 and the variance of 1.25 hence we can conclude that most of the values are close to the mean, the values doesn’t spread out too much.

According to our analysis, it shows that the mean of households with 2 or more children is 4.45, the household with 1-2 children at home has a mean of 4.28 and also, the household with no children has a mean of 3.13.

In conclusion, the household with 2 or more children are the most likely to recommend their favorite Mexican restaurant to their friends because they have the highest mean amongst the three groups.

The Pearson product-moment correlation coefficient is a measure of the linear dependence between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.

The Pearson correlation from our analysis is 0.802, this means that there is a strong relationship between X22=How satisfied are you with your favorite Mexican restaurant and X24= How likely are you to recommend your favorite Mexican restaurant to a friend.

Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends

Regression

analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’).

For X12=Friendly employees: the multiple regression analysis reveals that for every increase in Friendly employees, there will be a 0.281 increase in satisfaction.

For X15= Fresh food: the multiple regression analysis reveals that for every increase in fresh food, there will be a 0.390 increase in satisfaction.

• The beta coefficients are:

X12, friendly employees= 0.281

X15, fresh food= 0.390

APPENDIX B – SPSS OUTPUT

GET

FILE=’

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

‘.

DATASET NAME

DataSet1

WINDOW=FRONT.

FREQUENCIES VARIABLES=x25

/ORDER=ANALYSIS.

Frequencies

405

00:00:00.00

Notes

Output Created

27-OCT-2016 20:06:21

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on all cases with valid data.

Syntax

FREQUENCIES VARIABLES=x25 /ORDER=ANALYSIS.

Resources

Processor Time

00:00:00.00

Elapsed Time

[DataSet1] E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Statistics

X25 — Frequency of Eating at . . . ??

N

Valid

405

Missing

0

X25 — Frequency of Eating at . . . ??

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Very Infrequently

52

12.8

12.8

12.8

Somewhat Infrequently

70

17.3

17.3

30.1

Occasionally

101

24.9

24.9

55.1

Somewhat Frequently

91

22.5

22.5

77.5

Very Frequently

91

22.5

22.5

100.0

Total

405

100.0

100.0

FREQUENCIES VARIABLES=x25

/STATISTICS=MEAN MEDIAN MODE

/ORDER=ANALYSIS.

Frequencies

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on all cases with valid data.

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

27-OCT-2016 20:08:19

FREQUENCIES VARIABLES=x25 /STATISTICS=MEAN MEDIAN MODE /ORDER=ANALYSIS.

Statistics

X25 — Frequency of Eating at . . . ??

N

Valid

405

Missing

0

Mean

3.24

Median

3.00

Mode

3

X25 — Frequency of Eating at . . . ??

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Very Infrequently

52

12.8

12.8

12.8

Somewhat Infrequently

70

17.3

17.3

30.1

Occasionally

101

24.9

24.9

55.1

Somewhat Frequently

91

22.5

22.5

77.5

Very Frequently

91

22.5

22.5

100.0

Total

405

100.0

100.0

FREQUENCIES VARIABLES=x22

/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM

/ORDER=ANALYSIS.

Frequencies

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics are based on all cases with valid data.

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

27-OCT-2016 20:09:48

FREQUENCIES VARIABLES=x22 /STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM /ORDER=ANALYSIS.

Statistics

X22 — Satisfaction

N

Valid

405

Missing

0

Std. Deviation

1.118

Variance

1.251

Range

4

Minimum

3

Maximum

7

X22 — Satisfaction

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

3

38

9.4

9.4

9.4

4

148

36.5

36.5

45.9

5

95

23.5

23.5

69.4

6

93

23.0

23.0

92.3

7 = Highly Satisfied

31

7.7

7.7

100.0

Total

405

100.0

100.0

CROSSTABS

/TABLES=x31 BY x32

/FORMAT=AVALUE TABLES

/CELLS=COUNT

/COUNT ROUND CELL.

Crosstabs

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

2

27-OCT-2016 20:13:57
	Statistics for each table are based on all the cases with valid data in the specified range(s) for all variables in each table.
CROSSTABS /TABLES=x31 BY x32 /FORMAT=AVALUE TABLES /CELLS=COUNT /COUNT ROUND CELL.
	Dimensions Requested
	Cells Available		524245

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

405

0

405

100.0%

	Case Processing Summary
	Cases
	X31 — Ad Recall * X32 — Gender							10 0.0%		0.0%

Total

Total

405

	X31 — Ad Recall * X32 — Gender Crosstabulation
Count
	X32 — Gender
		Male			Female
	X31 — Ad Recall		Do Not Recall Ads		188		82		270
	Recall Ads		76		59		135
		264			141

CROSSTABS
/TABLES=x31 BY x32
/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ

/CELLS=COUNT EXPECTED COLUMN

/COUNT ROUND CELL.

Crosstabs

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics for each table are based on all the cases with valid data in the specified range(s) for all variables in each table.

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

Dimensions Requested

2

Cells Available

524245

27-OCT-2016 20:15:37

CROSSTABS /TABLES=x31 BY x32 /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT EXPECTED COLUMN /COUNT ROUND CELL.

00:00:00.03

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

X31 — Ad Recall * X32 — Gender

405

100.0%

0

0.0%

405

100.0%

X31 — Ad Recall * X32 — Gender Crosstabulation

X32 — Gender

Total

Male

Female

X31 — Ad Recall

Do Not Recall Ads

188

82

270

Recall Ads

Count

76

59

135

Expected Count

% within X32 — Gender

Total

Count

264

141

405

Expected Count

% within X32 — Gender

100.0%

100.0%

100.0%

		Count
		Expected Count	176.0	94.0	270.0
		% within X32 — Gender	71.2%	58.2%	66.7%
88.0	47.0	135.0
28.8%	41.8%	33.3%
264.0	141.0	405.0

1

1

1

.008

.011

1

.008

405

Chi-Square Tests
Value				df	Asymptotic Significance (2-sided)	Exact Sig. (2-sided)	Exact Sig. (1-sided)
Pearson Chi-Square	7.050a			.008
Continuity Correctionb	6.475		.011
Likelihood Ratio	6.950
Fisher’s Exact Test	.006
Linear-by-Linear Association	7.033
N of Valid Cases
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 47.00.
b. Computed only for a 2×2 table

ONEWAY x24 BY x32

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS

.

Oneway

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Syntax

Resources

Processor Time

Elapsed Time

00:00:00.02

27-OCT-2016 20:17:15
	Statistics for each analysis are based on cases with no missing data for any variable in the analysis.
ONEWAY x24 BY x32 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS.
			00:00:00.02

Descriptives

X24 — Likely to Recommend

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Minimum

Maximum

Lower Bound

Upper Bound

Male

264

2

7

Female

141

3.90

2

7

Total

405

3.79

1.199

.060

3.67

3.90

2

7

3.60	1.005	.062	3.48	3.72
4.13	1.435	.121	4.37

X24 — Likely to Recommend

df

Sig.

1

26.432

.000

Total

	ANOVA
		Sum of Squares			Mean Square			F
	Between Groups		26.432	19.232
	Within Groups	553.879	403	1.374
	580.311			404

ONEWAY x24 BY x33

/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS

/POSTHOC=SCHEFFE ALPHA(0.05).

Oneway

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics for each analysis are based on cases with no missing data for any variable in the analysis.

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

27-OCT-2016 20:19:09

ONEWAY x24 BY x33 /STATISTICS DESCRIPTIVES /MISSING ANALYSIS /POSTHOC=SCHEFFE ALPHA(0.05).

Descriptives

X24 — Likely to Recommend

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Minimum

Maximum

Lower Bound

Upper Bound

No Children at Home

190

3.13

1.052

.076

2.98

3.28

2

7

1-2 Children at Home

107

4.28

1.204

.116

4.05

4.51

2

7

More Than 2 Children at Home

108

4.45

.754

.073

4.31

4.60

3

7

Total

405

3.79

1.199

.060

3.67

3.90

2

7

ANOVA

X24 — Likely to Recommend

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

2

.000

Within Groups

Total

580.311

404

156.985	78.493	74.538
423.326	402	1.053

Post Hoc Tests

Std. Error

Sig.

Lower Bound

Upper Bound

No Children at Home

1-2 Children at Home

.000

More Than 2 Children at Home

.124

.000

1-2 Children at Home

No Children at Home

.124

.000

More Than 2 Children at Home

More Than 2 Children at Home

No Children at Home

.124

.000

1-2 Children at Home

.140

.465

Multiple Comparisons
Dependent Variable: X24 — Likely to Recommend
Scheffe
(I) X33 — Number of Children at Home	(J) X33 — Number of Children at Home	Mean Difference (I-J)	95% Confidence Interval
– 1.154*				.124	– 1.46	– .85
– 1.327*	– 1.63	– 1.02
1.154*	.85	1.46
– .173		.140		.465	– .52	.17
1.327*	1.02	1.63
.173	-.17	.52
*. The mean difference is significant at the 0.05 level.

Homogeneous Subsets

N

1

2

No Children at Home

190

3.13

1-2 Children at Home

107

4.28

More Than 2 Children at Home

108

4.45

Sig.

			X24 — Likely to Recommend
Scheffea,b
X33 — Number of Children at Home	Subset for alpha = 0.05
1.000	.409
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 125.690.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

CORRELATIONS

/VARIABLES=x22 x24

/PRINT=TWOTAIL NOSIG

/STATISTICS DESCRIPTIVES

/MISSING=PAIRWISE.

Correlations

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Syntax

Resources

Processor Time

00:00:00.02

Elapsed Time

00:00:00.02

27-OCT-2016 20:20:50

Statistics for each pair of variables are based on all the cases with valid data for that pair.

CORRELATIONS /VARIABLES=x22 x24 /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE.

Mean

Std. Deviation

N

X22 — Satisfaction

1.118

405

X24 — Likely to Recommend

3.79

1.199

405

Descriptive Statistics

4.83

X22 — Satisfaction

X24 — Likely to Recommend

X22 — Satisfaction

1

.000

N

405

405

X24 — Likely to Recommend

Pearson Correlation

.802**

1

Sig. (2-tailed)

.000

N

405

405

Correlations
	Pearson Correlation		.802**
	Sig. (2-tailed)
**. Correlation is significant at the 0.01 level (2-tailed).

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT x22

/METHOD=ENTER x12 x15 x16 x17.

Regression

Notes

Output Created

Comments

Input

Data

E:\BUSINESS RESEARCH SPSS\7279SantaFeGrillSPSSdataSet.sav

Active Dataset

DataSet1

Filter

Weight

Split File

N of Rows in Working Data File

405

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Syntax

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

27-OCT-2016 20:21:51
Statistics are based on cases with no missing values for any variable used.
REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT x22 /METHOD=ENTER x12 x15 x16 x17.
Memory Required	5792 bytes
Additional Memory Required for Residual Plots	0 bytes

Model

1

a. Dependent Variable: X22 — Satisfaction

Variables Enter ed /Removeda
Variables Entered	Variables Removed	Method
X17 — Attractive Interior, X15 — Fresh Food, X16 — Reasonable Prices, X12 — Friendly Employeesb	.	Enter
b. All requested variables entered.

Model

1

Model Summary
R	R Square	Adjusted R Square	Std. Error of the Estimate
.675a	.455	.450	.829
a. Predictors: (Constant), X17 — Attractive Interior, X15 — Fresh Food, X16 — Reasonable Prices, X12 — Friendly Employees

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

4

Total

404

a. Dependent Variable: X22 — Satisfaction

ANOVAa
230.019	57.505	83.575	.000b
Residual	275.225	400	.688
505.244
b. Predictors: (Constant), X17 — Attractive Interior, X15 — Fresh Food, X16 — Reasonable Prices, X12 — Friendly Employees

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-.128

.298

-.429

.668

X12 — Friendly Employees

.281

.037

.304

7.596

.000

X15 — Fresh Food

.390

.038

.417

10.346

.000

X16 — Reasonable Prices

.178

.035

.197

5.041

.000

X17 — Attractive Interior

.195

.042

.178

4.617

.000

a. Dependent Variable: X22 — Satisfaction

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