1. Secondary data collection

2. Business Intelligence
Collecting Data
Primary Data
Secondary Data
Business Intelligence

3. Secondary data
are data that have already been collected (by somebody) for purposes other than the problem at hand. At face value this definition seems straightforward(basit). However, many researchers confuse the term, or quite rightly see some overlap with business intelligence.

4. Secondary data, are data collected by someone other than the user
Secondary data, are data collected by someone other than the user. Common sources of secondary data for social science include censuses, organisational records and data collected through qualitative methodologies or qualitative research. Primary data, by contrast, are collected by the investigator conducting the research.

5. Primary data
are originated by a researcher for the specific purpose of addressing the problem at hand. They are individually tailored for the decision-makers of organisations that pay for well-focused and exclusive support.
Compared with readily available data from a variety of sources, this tailoring means higher costs and a longer time frame in collecting and analysing the data.

6. Primary Data consists of a collection of original primary data
Primary Data consists of a collection of original primary data. It is often undertaken after the researcher has gained some insight into the issue by reviewing secondary research or by analyzing previously collected primary data. It can be accomplished through various methods, including questionnaires and telephone interviews in market research, or experiments and direct observations in the physical sciences, amongst others.

7. Advantages of secondary data
Secondary data are easily accessible
relatively inexpensive
quickly obtained

8. Disadvantages of secondary data
Because secondary data have been collected for purposes other than the problem at hand, their usefulness to the current problem may be limited in several important ways, including relevance and accuracy. The objectives, nature and methods used to collect the secondary data may not be appropriate to the present situation.
Also, secondary data may be lacking in accuracy or may not be completely current or dependable. Before using secondary data, it is important to evaluate them according to a series of factors.

9. Published external secondary sources-1

10. Non-government statistical data
Published statistical data are of great interest to researchers. Graphic and statistical analyses can be performed on these data to draw important insights. Examples of non governmental statistical data include trade associations such as the Swedish Tourism Trade Association (

12. Government sources
Turkish government and the EU also produce large amounts of secondary data. Each European country has its own statistical office which produces lists of the publications available (and the costs involved).

14. Census data
Most world countries produce either catalogues or newsletters that describe the array(seri)of census publications available and the plans for any forthcoming census.

17. Statistical data are periodicals published by government or non-governments, also can be published nationally or internationally.

18. What is Statistics?
1. Numerical data—the unemployment rate last month, total government expenditure last year, the number of impaired drivers charged during the recent holiday season, the crime rates of cities, and so forth.
2. We will view statistics the way professional statisticians view it—as a methodology for collecting, classifying, summarizing, organizing, presenting, analyzing and interpreting numerical information.

19. Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied.
Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal." Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments

20. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).[5] 

21. Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other.
Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.

22. Statistics as a science
Statistics as data
Statistics as a science

23. Theoretical Statistics
Applied Statistics

24. Mathematıcal Statistics Analytical Statistics

25. Descriptive Statistics Inferential Statistics
Istintaci, sonuç çıkarımlı
Statistics
Descriptive Statistics
Inferential Statistics

26. in Economics and Other Social Sciences
The Use of Statistics
in Economics and Other Social Sciences

27. Businesses use statistical methodology and thinking to make decisions about which products to produce, how much to spend advertising them, how to evaluate their employees, how often to service their machinery and equipment, how large their inventories should be, and nearly every aspect of running their operations.
The motivation for using statistics in the study of economics and other social sciences is somewhat different. The object of the social sciences and of economics in particular is to understand how the social and economic system functions.

28. Views and understandings of how things work are called theories
Views and understandings of how things work are called theories. Economic theories are descriptions and interpretations of how the economic system functions.
They are composed of two parts—a logical structure which is tautological (gereksiz tekrar) and a set of parameters in that logical structure which gives the theory empirical content.

29. If the facts turn out (tersine çevirmek) to be consistent (tutarlı) with the testable implications of the theory, then we accept the theory as true until new evidence inconsistent with it is uncovered.
A theory is valuable if it is logically consistent both within itself and with other theories established as “true” and is capable of being rejected by but nevertheless consistent with available evidence.

30. THE USE OF STATISTICS

31. “The rich are getting richer and the poor poorer
“The rich are getting richer and the poor poorer.” This is clearly an empirically testable proposition (önerme) for reasonable definitions of what we mean by “rich” and “poor”. It is really an interesting proposition (oran), however, only in conjunction with some theory of how the economic system functions in generating income and distributing it among people.
Such a theory would usually carry with it some implications (karışıklık) as to how the institutions within the economic system could be changed to prevent (önlemek) income inequalities from increasing. And thinking about these implications forces us to analyse the consequences of reducing income inequality and to form an opinion as to whether or not it should be reduced.

32. Statistics is the methodology that we use to confront (karşı koymak) theories like the theory of demand and other testable propositions with the facts.
It is the set of procedures and intellectual processes by which we decide whether or not to accept a theory as true—the process by which we decide what and what not to believe. In this sense, statistics is at the root of all human knowledge.

33. TYPES OF VARIABLES Mathematically continuous variable
Discrete variable
is one that can take on a range of values that correspond to some quantitative amount and can be diveded gradually. Age, income, weight kg, distance km etc.
is one that indicates membership in some group. Can not be diveded. gender, profession, education, living districts, cities, contries,names, etc. 1

34. TYPES OF VARIABLES Causality Independent variable Dependent variable
is one that affects other variables. A variable that is expected to influence the dependent variable in some way. Will explain later.
is one that be affected by other variables. A process outcome or a variable that is predicted and/or explained by other variables, will explain later. 1

35. Measurement Scales
Nonmetric
Metric
Nominal
Ordinal
Interval
Ratio

36. Nominal scale
A nominal scale is a figurative labelling scheme(plan)in which the numbers serve only as labels for identifying and classifying objects.
For example, the numbers assigned to the respondents in a study constitute(oluşturmak) a nominal scale, thus a female respondent may be assigned a number 1 and a male respondent 2.

37. When a nominal scale is used for the purpose of identification, there is a strict one-to-one correspondence between the numbers and the objects.
Each number is assigned to only one object, and each object has only one number assigned to it.

38. No matter which one is first or second

39. Common examples include student registration numbers at their college or university and numbers assigned to football players or jockeys in a horse race. In marketing research, nominal scales are used for identifying respondents, brands, attributes, banks and other objects.

40. Examples Telephone Nu……… Gender ( ) Female ( ) Male
Marital Status ( ) Married ( ) Single ( ) Divorced
Nationality ( ) Turk ( ) Arab ( ) Germen ( ) English
Telephone Nu………

41. Nationality Ferquency (%) Turkish 330 21 Germeny 610 38
- Mod and frequencies can be calculated
Table 1: The Nationality of the Tourists
Nationality Ferquency (%)
Turkish
Germeny
Japanies
Arab
Total

42. Ordinal scale
An ordinal scale is a ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic. An ordinal scale allows you to determine whether an object has more or less of a characteristic than some other object, but not how much more or less.

43. Thus, an ordinal scale indicates relative position, not the magnitude of the differences between the objects.
The object ranked first has more of the characteristic as compared with the object ranked second, but whether the object ranked second is a close second or a poor second is not known.

44. Common examples of ordinal scales include quality rankings, rankings of teams in a tournament and occupational status. In marketing research, ordinal scales are used to measure relative attitudes, opinions, perceptions and preferences.
Measurements of this type include ‘greater than’ or ‘less than’ judgments from the respondents.

46. Interval scale
In an interval scale, numerically equal distances on the scale represent equal values in the characteristic being measured because it is metric.
An interval scale contains all the information of an ordinal scale, but it also allows you to compare the differences between objects.
The difference between any two scale values is identical to the difference between any other two adjacent values of an interval scale.

47. There is a constant or equal interval between scale values
There is a constant or equal interval between scale values. The difference between 1 and 2 is the same as the difference between 2 and 3, which is the same as the difference between 5 and 6.
A common example in everyday life is a temperature scale. In marketing research, attitudinal data obtained from rating scales are often treated as interval data.

48. Turkish people are generous.(Likert Scale)
(5) Strongly agree
(4) Agree
(3) Neither agree nor disagree
(2) Disagree
(1) Strongly disagree
I am proud of my country.

49. Interval is a metric scale, so it is possible to calculate the arithmetical means of interval scale. Interval scale can be five, seven or nine-level scale.
Let us assume that 200 students have answered the question above as follow, calculate the arithmetical means

50. The weighted arithmetic means is?
Participation Degree
Frequency %
5 Strongly agree 75
37,5 4 64 32 3 38 19 2 18 9
1 Strongly disagree 5
2,5
Total
200
100
The weighted arithmetic means is?
(5x0,375)+(4x0,32)+(3x0,19)+(2x0,09)+(1x0,025)=3,93

51. Descriptive and Inferential Statistics
The application of statistical thinking involves two sets of processes. First, there is the description and presentation of data.
Second, there is the process of using the data to make some inference about features of the environment from which the data were selected or about the underlying mechanism that generated the data, such as the ongoing functioning of the economy or the accounting system or production line in a business firm.
The first is called descriptive statistics and the second inferential statistics.

53. Descriptive statistics utilizes numerical and graphical methods to find patterns in the data, to summarize the information it reveals and to present that information in a meaningful way.
Inferential statistics uses data to make estimates, decisions, predictions, or other generalizations about the environment from which the data were obtained.

55. Data Sets
There are three general kinds of data sets—cross-sectional, time-series and panel. And within data sets there are two kinds of data—quantitative and qualitative. Quantitative data can be recorded on a natural numerical scale. Examples are gross national product (measured in dollars) and the consumer price index (measured as a percentage of a base level).
Qualitative data cannot be measured on a naturally occurring numerical scale but can only be classified into one of a group of categories. An example is a series of records of whether or not the automobile accidents occurring over a given period resulted in criminal charges—the entries are simply yes or no.

56. Creating metric and non-metric variables by SPSS Package Program

57. Let’s assume that we have 20 costumers and we collected some basic information about their gender, education, age and opinion about our service. How can we design the database by using SPSS Package Program?

68. Homework
Create 4 different variables, nominal, ordinal interval and ratio then open an SPSS file.