1. Warm up – Unit 4 Test – Financial Analysis
Monday, March 5th
Warm up – Unit 4 Test – Financial Analysis
Unit 5 – Marketing Information Management
Obj – Interpreting Data
Slide Show/Notes

2. Marketing Information Management.
Unit 5
Marketing Information Management.

3. Marketing information Management
What is it?
Why do we do it?

4. Objective 3.05
Interpret marketing information to test hypotheses and/or to resolve issues.

5. Interpret and Check Data
LOTS OF DATA

6. paper-based marketing data Must be Physically Inspected
If errors are caught early, they can be corrected.
Look for standardizations and continuity
Missing values/nonuniform data entry which means changing the value or formula of the response codes.
Make sure information exists as required
Data may not be presented in the form needed for further analysis ie
incomplete responses (skips questions)
data entry error (wrong entry/answer; recorded improperly –choice is B but entered as C)
Inconsistencies in responses

7. Questions to ask when screening initial data
Where did the data come from?
Who did research?
Who is source?

8. Questions to ask when screening initial data
Has the data been peer reviewed?
Professionals have looked at the study before it was published and concluded that the study’s authors pretty much followed the rules of good scientific research

9. Questions to ask when screening initial data
How was the data collected?
If survey, were the respondents selected at random?
No cherry picking (selecting an area you know will give you the answers you hope for)

10. Questions to ask when screening initial data
Be skeptical when dealing with comparisons
A correlation (relationship) DOES NOT mean causation
Be aware of numbers taken out of context
Numbers picked to look interesting may mean something entirely difference once it is placed in a different context

11. Actions that can be taken with incomplete records
Cleanse the data which includes dropping problematic data.
If caught early and still in field, can correct at once
Recontact/reinterview respondent
Check responses for each question as interview progresses
Drop incomplete data from survey (in part or in full)

12. Failure of data entry technology Information is not recorded properly
Reasons for data entry errors that limit the use of marketing research data
Wrong entry
Failure of data entry technology
Information is not recorded properly

13. Inconsistencies in responses
Reasons that questionable entries are found in raw marketing research data
Honesty
Inconsistencies in responses

14. Role of coding in processing marketing data
Procedure converts the edited raw data into numbers or symbols
Code book is created
Identifies all of the variable names and numbers associated with each possible response to each question

15. Generally accepted coding principles
Specify categories/classes
ie Male = M or 1 Female = F or 2
Locate only one character in each column
Use only numeric codes
Use as many columns for fields assigned to variables
Use standard coded for no information
ie No answers = 9
Code in respondent ID on each record
to tie coded data to questionnaire

16. Importance of editing coded information
Must have systematic procedure for assessing the questionnaires developed by the research analyst
Cleary defined decision rules should be in place and followed
MUST not allow subjectivity to enter into editing process
Data editors should be intelligent, experienced, and objective

17. Data-entry options On-line direct data entry Optical scanning
Computer Assisted Telephone Interviewing (CATI)
interviewer is sitting behind a terminal and asks the questions that appear on the screen. The respondent's answer is then typed into computer by the interviewer.
Optical scanning
For highly structured questionnaires
Keyboarding
Data entry via a computer keyboard; requires verification

18. Computer based software saves time and money.
Paper-based marketing information vs. computer-based marketing information
Computer based software saves time and money.
The programs have error checking built in to prevent common errors.
The availability of the item bank makes it easier to build questionnaires.

19. Shows each question in the questionnaire
Importance of using a codebook to describe data and to indicate where and how they can be accessed
Shows each question in the questionnaire
Shows the variable name and label
Shows the variable type, values and value labels.

20. Data Analysis
After data is collected it must be analyzed so that conclusions can be made.
Data summarization: is the process of describing a data set by computing a small number of measures that explain the data set

21. Purpose of Data Summarization
Summarizes the data
Make the data understandable
Communicates patterns
Shows how the sample findings relate to the overall population

22. What is descriptive statistics?
Descriptive statistics are used simply to describe the sample you are concerned with.
Descriptive statistics use measures of central tendency to explain data.

23. What is central tendency?
A measure of central tendency is a measure that tells us where the middle of a bunch of data lies. It explains where data falls.
The three most common measures of central tendency are the mean, the median, and the mode.

24. Mean The responses are as follows: 2, 3, 1, 0, 9, 2, 3, 2, 4, 2.
It is simply the sum of the numbers divided by the number of numbers in a set of data.
This is also known as average.
Example –
A researcher is interested in the effect of family size on self-esteem. To begin this study, 10 students are each asked how many brothers and sisters they have.
The responses are as follows: 2, 3, 1, 0, 9, 2, 3, 2, 4, 2.
What is the mean number of brothers and sisters?
The answer is 2.8

25. Median
The median is the middle score in a distribution of scores that have been ranked in numerical order.
Example –
What is the median number of brothers and sisters?
The responses are as follows: 2, 3, 1, 0, 9, 2, 3, 2, 4, 2.
Answer - 2
 A disadvantage of the median is if there is a large set of numbers, it would be time consuming to place each in order of size

26. Mode
The mode is the most frequently occurring score in a set of scores.
Example:
The responses are as follows: 2, 3, 1, 0, 9, 2, 3, 2, 4, 2. What is the mode for this set of data? 2

27. When to use the mean, median, and mode
The mean can be used to find an average/ typical number.
Use the mean when numbers are closely related.
If however, the numbers are very widely spread, are very unevenly distributed, or contain extreme values, e.g. 9, 10, 13, 17, 23, 30, 45; or a hundred values of 10 and one value of 50 then the mean can be misleading,.
Use the median if looking for the central point.
Mode – When you want to determine the most common response

28. When do you use measures of variability?
Measure of variability are used when the researcher wants to know the extent to which the data set differ from one another.
Range, and Standard Deviation are computations for measuring variability.

29. Range
The range is the difference between the highest and lowest scores in a distribution.
A social researcher would like to know how many digits people in different age categories can recall with only one presentation of a list. She creates random lists of digits and presents them to participants. The number of digits recalled by the first 10 participants is as follows: 5, 9, 6, 10, 9, 7, 8, 7, 9, 12. What is the range of this data set?
Answer – 7 (12-5)

30. Standard Deviation
The standard deviation is a measure of how spread out your data are. How far away is the data from the average.