Data Collection, Sampling, Measurement Concept, Questionnaire Designing-Types
- 1. Unit 2 & 3 Data Collection Sampling Measurement Concept Questionnaire Designing-Types
- 2. Goals After completing this chapter, you should be able to: Describe key data collection methods Know key definitions: Population vs. Sample Primary vs. Secondary data types Qualitative vs. Qualitative data Time Series vs. Cross-Sectional data Explain the difference between descriptive and inferential statistics Describe different sampling methods & Experiments
- 3. Descriptive statistics Collecting, presenting, and describing data Inferential statistics Drawing conclusions and/or making decisions concerning a population based only on sample data Tools of Business Statistics
- 4. Descriptive Statistics Collect data e.g. Survey, Observation, Experiments Present data e.g. Charts and graphs Characterize data e.g. Sample mean = n xi
- 5. Data Sources Primary Data Collection Secondary Data Compilation Observation Experimentation Survey Print or Electronic
- 6. Data Sources Primary Data Collection Secondary Data Compilation Observation Experimentation Survey Print or Electronic
- 7. Survey Design Steps Define the issue what are the purpose and objectives of the survey? Define the population of interest Formulate survey questions make questions clear and unambiguous use universally-accepted definitions limit the number of questions
- 8. Survey Design Steps Pre-test the survey pilot test with a small group of participants assess clarity and length Determine the sample size and sampling method Select Sample and administer the survey (continued)
- 9. Types of Questions Closed-end Questions Select from a short list of defined choices Example: Major: __business __liberal arts __science __other Open-end Questions Respondents are free to respond with any value, words, or statement Example: What did you like best about this course? Demographic Questions Questions about the respondents’ personal characteristics Example: Gender: __Female __ Male
- 10. A Population is the set of all items or individuals of interest Examples: All likely voters in the next election All parts produced today All sales receipts for November A Sample is a subset of the population Examples: 1000 voters selected at random for interview A few parts selected for destructive testing Every 100th receipt selected for audit Populations and Samples
- 11. Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z Population Sample b c g i n o r u y
- 12. Why Sample? Less time consuming than a census Less costly to administer than a census It is possible to obtain statistical results of a sufficiently high precision based on samples.
- 14. Statistical Sampling Items of the sample are chosen based on known or calculable probabilities Probability Samples Simple Random SystematicStratified Cluster
- 15. Simple Random Samples Every individual or item from the population has an equal chance of being selected Selection may be with replacement or without replacement Samples can be obtained from a table of random numbers or computer random number generators
- 16. Stratified Samples Population divided into subgroups (called strata) according to some common characteristic Simple random sample selected from each subgroup Samples from subgroups are combined into one Population Divided into 4 strata Sample
- 17. Decide on sample size: n Divide frame of N individuals into groups of k individuals: k=N/n Randomly select one individual from the 1st group Select every kth individual thereafter Systematic Samples N = 64 n = 8 k = 8 First Group
- 18. Cluster Samples Population is divided into several “clusters,” each representative of the population A simple random sample of clusters is selected All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique Population divided into 16 clusters. Randomly selected clusters for sample
- 19. Data Collection Data Qualitative (Categorical) Quantitative (Numerical) Discrete Continuous Examples: Marital Status Political Party Eye Color (Defined categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics)
- 20. Data Types Data Qualitative (Categorical) Quantitative (Numerical) Discrete Continuous Examples: Marital Status Political Party Eye Color (Defined categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics)
- 21. Data Types Time Series Data Ordered data values observed over time Cross Section Data Data values observed at a fixed point in time
- 22. Data Types Sales (in $1000’s) 2003 2004 2005 2006 Atlanta 435 460 475 490 Boston 320 345 375 395 Cleveland 405 390 410 395 Denver 260 270 285 280 Time Series Data Cross Section Data
- 23. Data Measurement Levels Ratio/Interval Data Ordinal Data Nominal Data Highest Level Complete Analysis Higher Level Mid-level Analysis Lowest Level Basic Analysis Categorical Codes ID Numbers Category Names Rankings Ordered Categories Measurements
- 24. Randomization of Subjects Randomization: the use of chance to divide experimental units into groups
- 25. Experiment Vocabulary Experimental units Individuals on which the experiment is done Subjects Experimental units that are human Treatment Specific experimental condition applied to the units Factors Explanatory variables in an experiment Level Specific value of a factor
- 26. Example of an Experiment Does regularly taking aspirin help protect people against heart attacks? Subjects: 21,996 male physicians Factors Aspirin (2 levels: yes and no) Beta carotene (2 levels: yes and no) Treatments Combination of the 2 factor levels (4 total) Conclusion Aspirin does reduce heart attacks, but beta carotene has no effect.
- 28. Block designs Random assignment of units to treatments is carried out separately within each block (Group of experimental units or subjects that are known before the experiment to be similar in some way that is expected to affect the response to the treatments)
- 29. Making statements about a population by examining sample results Sample statistics Population parameters (known) Inference (unknown, but can be estimated from sample evidence) Sample Population Inferential Statistics
- 30. Key Definitions A population is the entire collection of things under consideration A parameter is a summary measure computed to describe a characteristic of the population A sample is a portion of the population selected for analysis A statistic is a summary measure computed to describe a characteristic of the sample
- 31. Statistical Inference Terms A parameter is a number that describes the population. Fixed number which we don’t know in practice A statistic is a number that describes a sample. Value is known when we have taken a sample It can change from sample to sample Often used to estimate an unknown parameter
- 32. Statistical Significance An observed effect (i.e., a statistic) so large that it would rarely occur by chance is called statistically significant. The difference in the responses (another statistic) is so large that it is unlikely to happen just because of chance variation.
- 33. Inferential Statistics Estimation e.g.: Estimate the population mean weight using the sample mean weight Hypothesis Testing e.g.: Use sample evidence to test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.
- 34. Sampling variability Sampling variability Value of a statistic varies in repeated random sampling If the variation when we take repeat samples from the same population is too great, we can’t trust the results of any one sample.
- 35. Chapter Summary Reviewed key data collection methods Introduced key definitions: Population vs. Sample Primary vs. Secondary data types Qualitative vs. Qualitative data Time Series vs. Cross-Sectional data Examined descriptive vs. inferential statistics Described different sampling techniques Reviewed data types and measurement levels