TYPES OF DATA
IS STATISTICS 100% CORRECT? 2
DATA SOURSES Primary Data Collection Secondary Data Compilation Print or Electronic Observation 3 Survey Experimentation 3
TYPES OF DATA Data Categorical Numerical Examples: Marital Status Political Party Eye Color (Defined categories) Discrete Examples: Number of Children Defects per hour (Counted items) Continuous Examples: Weight Voltage (Measured characteristics) 4
DEFINITIONS Quantitative Data (Numerical) consists of numbers representing counts or measurements. Qualitative Data (Categorical) can be separated into different categories that are distinguished by some nonnumeric characteristic. 5
DEFINITIONS Discrete Data result when the number of possible values is either a finite number or a countable number. Continuous Data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps. 6
WHAT IS A VARIABLE? A variable - a characteristic of a population or a sample, e.g. Examination marks Stock price The waiting time for medical services Data - Observed values of variables 7
EXAMPLE Data - Observed values of variables 46 49 46 48 45 49 46 45 47 43 45 46 44 47 44 45 49 46 42 47 46 44 42 45 46 46 42 45 41 47 48 43 43 49 40 44 46 43 45 44 41 47 43 47 48 42 44 48 48 45 Scores on a Test 8
TYPES OF VARIABLES A. Qualitative or Attribute variable - the characteristic being studied is nonnumeric. EXAMPLES: Gender, religious affiliation, type of automobile owned, state of birth, eye color are examples. B. Quantitative variable - information is reported numerically. EXAMPLES: balance in your checking account, minutes remaining in class, or number of children in a family. 9
QUANTITAIVE VARIABLES Classifications Quantitative variables can be classified as either discrete or continuous. A. Discrete variables: can only assume certain values and there are usually gaps between values. EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,,etc). B. Continuous variable can assume any value within a specified range. EXAMPLE: The pressure in a tire, the weight of a pork chop, or the height of students in a class. 10
SUMMARY: TYPES OF VARIABLES 11
SCALES OF MEASUREMENT Scales of Measurement 1. Nominal Scale Categorical/qualitative observations Use number to represent the categories. Example: Single=1, Married=2 2. Ordinal Scale Ordered categorical observations Value are in order Example: Poor-1 Fair-2 Good-3 3. Interval Scale Numerical/quantitative observations Numerical bring the meaning of value. Example: marks, temperature, IQ 4. Ratio Scale Numerical/quantitative observations Have absolute zero value Example: weight, height, income 12
SCALES OF MEASUREMENT Nominal level data that is classified into categories and cannot be arranged in any particular order. EXAMPLES: eye color, gender, religious affiliation. Ordinal level involves data arranged in some order, but the differences between data values cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Sevenup number 3, and Orange Crush number 4. Interval level similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point. EXAMPLE: Temperature on the Fahrenheit scale. Ratio level the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer s representatives per month. 13
DEFINITIONS Nominal Scale is characterized by data that consists of names, labels, or categories only. Ordinal Scale data can be arranged in some order, but differences between data values either cannot be determined or are meaningless. 14
DEFINITIONS Interval Scale is like the ordinal scale, with additional property that the difference between any two data values is meaningful. However, data at this level do not have a natural zero starting point. Ratio Scale is similar to the interval scale with additional property that there is an absolute zero (where zero indicates that none of the quantity is present). In this scale ratios are meaningful. 15
SUMMARY: SCALES OF MEASUREMENT 16
EXAMPLES Nominal Person Marital status Ahmad Siva Ah Keong.... Computer married single single Brand 1 IBM 2 Dell 3 IBM.... Ratio/Interval data Age - income 55 75000 42 68000... Weight. gain +10 +5. 17
EXAMPLES Nominal With nominal data, all we can do is, calculate the proportion of data that falls into each category. IBM Dell Compaq Other Total 25 11 8 6 50 50% 22% 16% 12% Ratio/Interval data Age - income 55 75000 42 68000.... Weight gain +10 +5. 18
TYPES of DATA TYPES of ANALYSIS Knowing the type of data is necessary to properly select the suitable technique to be used when analyzing data. Type of analysis allowed for each type of data Ratio/Interval data arithmetic calculations/average 67,74,71,83,93,55,48,82,68,62 Average=70.3 Nominal data counting the number of observation/ frequency in each category Single:1,Married:2 Divorced:3, Widowed:4 Data record: 1,2,2,2,4,1,2,2,1,3 Average=2.0; Does this mean average person is married???? 19
TYPES of DATA TYPES of ANALYSIS Solution of Nominal data Category Code Frequency Single 1 3 Married 2 5 Divorced 3 2 Widowed 4 4 Ordinal data - computations based on an ordering process 20
HIERARCHY OF DATA Ratio/Interval* Values are real numbers All calculations are valid Data may be treated as ordinal or nominal Example : Examination Marks Ordinal Value must represent the ranked order of the data Calculation based on an ordering process are valid Data may be treated as nominal but not as interval Nominal Value are the arbitrary numbers that represent categories. Only calculation based on the frequencies of occurrence are valid. Data may not be treated as ordinal or interval *Higher-level data type may be treated as lower-level ones. 21
PUBLISHED DATA For example: This is often a preferred source of data due to low cost and convenience. For example: Published data is found as printed material, tapes, disks, and on the Internet. Bureau of Census. Data published by the organization that has collected it is called PRIMARY DATA The Statistical abstracts of the United States, compiles data from primary sources Compustat, sells variety of financial data tapes compiled from primary sources Data published by the US Data published by an organization different than the organization that has collected it is called SECONDARY DATA. 22
OBSERVATIONAL or EXPERIMENTAL When published data is unavailable, one needs to conduct a study to generate the data. Observational study is one in which measurements representing a variable of interest are observed and recorded, without controlling any factor that might influence their values. Experimental study is one in which measurements representing a variable of interest are observed and recorded, while controlling factors that might influence their values. 23
STATISTICAL STUDIES Statistical Studies Past Observational Retrospective study When observations are made? At one point Do you make observations only, or do you modify the subjects? Future Prospective study Experiment Design: 1. Control effects of variables 2. Use replication 3. Use randomization Cross-sectional study 24
IS STATISTICS 100% CORRECT? 25
DEFINITIONS Voluntary Response Sample (or selfselected sample) is one in which the respondents themselves decide whether to be included in the sample. Voluntary response sample might not be representative of the intended population. 26
SURVEYS Surveys solicit information from people. Surveys can be made by means of personal interview telephone interview self-administered questionnaire 27
QUESTIONNAIRE A good questionnaire must be well designed: Keep the questionnaire as short as possible. Ask short,simple, and clearly worded questions. Start with demographic questions to help respondents get started comfortably. Use dichotomous and multiple choice questions. Use open-ended questions cautiously. Avoid using leading-questions. Pretest a questionnaire on a small number of people. Think about the way you intend to use the collected data when preparing the questionnaire. 28
IS STATISTICS 100% CORRECT? 29