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Arc 323 human studies in architecture fall 2018 lecture 6-data collection
This document discusses procedures for collecting quantitative data for research in architectural engineering. It covers obtaining permissions from institutions and participants, selecting participants through probability and non-probability sampling methods, identifying variable types and data collection methods, and developing instruments and procedures to reliably obtain and record data. Key aspects include securing multi-level approvals, choosing a sufficiently sized random or stratified sample, operationalizing variables, and locating or creating valid and reliable instruments to collect data.
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Arc 323 human studies in architecture fall 2018 lecture 6-data collection
- 1. Faculty of Engineeringand Technology Department of Architectural Engineering ARC 323 : Human Studies in Architecture Fall 2018 Dr. Yasser Mahgoub Lecture 6 - Data Collection
- 2. Key Ideas 1. Obtainingpermissions for data collection 2. Selecting participants for data collection 3. Identifying data options 4. Recording and administering data collection
- 3. Procedures for CollectingQuantitative Data 1. Obtain permissions – identify the unit of analysis –secure permissions –obtain informed consent from participants
- 4. Obtaining Permissions • Institutionalor organizational (e.g. school district) • Site-specific (e.g. secondary school) • Individual participants or parents • Campus approval (e.g. university or college)
- 5. Procedures for CollectingQuantitative Data 2. Select participants –specify a population and sample –use probability and non-probability sampling –choose a sample size
- 6. Select Participants Choose aSample Size • Select a sample size as large as possible from the individuals available • Select a sufficient number of participants for the statistical tests you will use (e.g. 15 per group for experiments)
- 7. Procedures for CollectingQuantitative Data 3. Identify data options –specify variables – operationalize variables – select scales of measurement – choose types of data measures
- 8. Procedures for CollectingQuantitative Data 4. Record and administer data collection –locate or develop an instrument –obtain reliable and valid data –develop administrative procedures for data collection
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- 10. Select Participants Specify aPopulation and Sample • A population is a group of individuals that comprise the same characteristics
- 11. Select Participants Specify aPopulation and Sample • A sample is a sub-group of the target population that the researcher plans to study for the purpose about making generalizations about the target population.
- 12. Sampling • Aim ofsampling is to equate unknown characteristics that may influence variation and to preserve the representativeness of the sample
- 13. Populations and Samples Sample Target Population Sample Population -Allteachers in high schools in one city -College students in all community colleges -Adult educators n all schools of education Sample -All high school biology teachers -Students in one community college -Adult educators in 5 schools of education in the Midwest
- 14. Two Classes ofSampling Techniques: 1. Non-probability Sampling 2. Probability Sampling
- 15. Select Participants: UseProbability and Non- Probability Sampling • Probability sampling is the selection of individuals from the population so that they are representative of the population • Non-probability sampling is the selection of participants because they are available, convent, or represent some characteristic the investigator wants to study.
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- 17. Probability Sampling • Commonfeature is that each unit in the population has a known, nonzero probability of being included in the sample
- 18. Advantages of ProbabilitySampling • Objective standards remove possibility of unknown confounds • Intent to remove bias in selection process
- 19. Types of ProbabilitySamples • Simple Random: selecting a sample from the population so all in the population have an equal chance of being selected • Systematic: choosing every “nth” individual or site in the population until the desired sample size is achieved
- 20. Types of ProbabilitySamples • Stratified sampling: stratifying the population on a characteristic (e.g. gender) than sampling from each stratum. • Multi-Stage Cluster Sampling: a sample chosen in one or two stages because the population is not easily identified or is large
- 21. Proportional Stratification Sampling Approach Boys N=6000 Girls N=3000 Population (N=9000) .66of pop. 200 .33 of pop 100 Sample = 300
- 22. A.Simple Random Sample Eachmember of the study population has an equal probability of being selected
- 23. B. Stratified RandomSample Each member of a population is assigned to a group or stratum, then random sample is drawn from each stratum (ensures levels represented)
- 24. C. Proportional RandomSample Each member of a population is assigned to a sub-group, then representative sample is drawn from each group proportional to population
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- 26. Non-probability Sampling • Commonfeature is that subjective judgments are used to determine the population that are contained in the sample.
- 27. Advantages of Non-probabilitysampling • Fast, low effortcost methods • Useful in exploratory research
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- 29. A. Convenience sampling Selectcases based on their willingness and available to be studied
- 30. B. Judgmental sampling Selectcases based on some purpose (Most similardissimilar, Typical or Critical cases)
- 31. C. Systematic Sampling Every4th Select cases based on some predefined criteria (Interval sampling)
- 32. D. Snowball Sampling Theresearcher asks participants to identify other participants to become members of the sample.
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