Santa fe grill mexican restaurant case study

Research Report For Jose's Southwestern Cafe

TABLE OF CONTENTS

BACKGROUND

PROBLEM STATEMENT

OPERATIONAL DEFINITIONS

RESEARCH OBJECTIVES

HYPOTHESES

RESEARCH PROCEDURES

LIMITATIONS

SECONDARY RESEARCH FINDINGS

PRIMARY RESEARCH FINDINGS

RECOMMENDATIONS

CONCLUSION

APPENDIX SPSS OUTPUT

BACKGROUND

In early 2004, two 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 35 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. Meanwhile, 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

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

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

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.

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

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.

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

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

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

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

FREQUENCIES VARIABLES=x25

/ORDER=ANALYSIS.

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

[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

27-OCT-2016 20:08:19

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

FREQUENCIES VARIABLES=x25

/STATISTICS=MEAN MEDIAN MODE

/ORDER=ANALYSIS.

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

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

27-OCT-2016 20:09:48

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

FREQUENCIES VARIABLES=x22

/STATISTICS=STDDEV VARIANCE RANGE MINIMUM MAXIMUM

/ORDER=ANALYSIS.

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

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

27-OCT-2016 20:13:57

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

CROSSTABS

/TABLES=x31 BY x32

/FORMAT=AVALUE TABLES

/CELLS=COUNT

/COUNT ROUND CELL.

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.00

Dimensions Requested

2

Cells Available

524245

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

Count

X32 -- Gender

Total

Male

Female

X31 -- Ad Recall

Do Not Recall Ads

188

82

270

Recall Ads

76

59

135

Total

264

141

405

CROSSTABS

/TABLES=x31 BY x32

/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ

/CELLS=COUNT EXPECTED COLUMN

/COUNT ROUND CELL.

Crosstabs

Notes

Output Created

27-OCT-2016 20:15:37

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

CROSSTABS

/TABLES=x31 BY x32

/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ

/CELLS=COUNT EXPECTED COLUMN

/COUNT ROUND CELL.

Resources

Processor Time

00:00:00.00

Elapsed Time

00:00:00.03

Dimensions Requested

2

Cells Available

524245

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

Count

188

82

270

Expected Count

176.0

94.0

270.0

% within X32 -- Gender

71.2%

58.2%

66.7%

Recall Ads

Count

76

59

135

Expected Count

88.0

47.0

135.0

% within X32 -- Gender

28.8%

41.8%

33.3%

Total

Count

264

141

405

Expected Count

264.0

141.0

405.0

% within X32 -- Gender

100.0%

100.0%

100.0%

Chi-Square Tests

Value

df

Asymptotic Significance (2-sided)

Exact Sig. (2-sided)

Exact Sig. (1-sided)

Pearson Chi-Square

7.050a

1

.008

Continuity Correctionb

6.475

1

.011

Likelihood Ratio

6.950

1

.008

Fisher's Exact Test

.011

.006

Linear-by-Linear Association

7.033

1

.008

N of Valid Cases

405

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 47.00.

b. Computed only for a 2x2 table

ONEWAY x24 BY x32

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS.

Oneway

Notes

Output Created

27-OCT-2016 20:17:15

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

ONEWAY x24 BY x32

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS.

Resources

Processor Time

00:00:00.02

Elapsed Time

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

3.60

1.005

.062

3.48

3.72

2

7

Female

141

4.13

1.435

.121

3.90

4.37

2

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

26.432

1

26.432

19.232

.000

Within Groups

553.879

403

1.374

Total

580.311

404