Primary data techniques.
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This document discusses different types of primary research methods including quantitative and qualitative research. Quantitative research uses numerical data and statistical analysis to generalize results to broader populations, while qualitative research provides in-depth insights through non-numerical methods like interviews and focus groups. Specific quantitative sampling methods covered include random sampling, stratified sampling, and quota sampling, which differ in their selection processes and representativeness. Common qualitative techniques mentioned are personal interviews, focus groups, and observation.
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Researchers usually work within a paradigm that is consistent with their world view, and that gives rise to the types of question that excite their curiosity.
The maturity of the concept of interest also may lead to one or the other paradigm: when little is known about a topic, a qualitative approach is often more fruitful than a quantitative one
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This document discusses data collection methods, including secondary and primary data. Secondary data refers to data that has already been collected for other purposes, and can help identify problems, define research questions, and interpret primary data. However, secondary data may not be directly relevant or accurate for the current research problem. Primary data is originally collected by the researcher specifically for the research problem and can be qualitative or quantitative. Common primary data collection methods include surveys, interviews, and observations. Surveys are a widely used quantitative method that involve distributing questionnaires via mail, email, telephone or online. They have advantages of being low-cost and allowing large samples, but suffer from low response rates.
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Research instruments
After the formulation of research questions and sample selection, the next step in research chain is developing data collection instruments or research instruments.
They are measurement tools (i.e., tests, questionnaires or interviews)
They can be designed by the researcher or can be previously-developed by other researchers.
Data Collection
This document discusses various methods for collecting primary and secondary data for research. It explains that primary data is collected fresh for a study, while secondary data comes from existing sources like books, papers, and reports. Some primary data collection methods covered include surveys, experiments, interviews, questionnaires, schedules, case studies, and observation. Factors to consider when choosing a method include the research objectives, budget, timeframe, and the researcher's knowledge. Collecting quality data is essential for answering the research problem.
Importance of research
Quantitative research involves structured surveys and questionnaires to collect statistics and facts from a large sample of people. It is used to understand market demand and identify customer demographics. Qualitative research analyzes unstructured data like interviews and feedback to understand the reasons behind people's opinions and behaviors. Secondary research uses existing published sources like reports and archives to determine what is already known on a topic. Primary research collects new information directly from subjects through methods like interviews, surveys, and observations.
Research instruments
Research instruments are devices used to collect data and include questionnaires, interviews, and observation. Questionnaires involve a series of questions to elicit information and have advantages like being simple and allowing wide data collection, but responses may lack depth. Interviews can be structured or unstructured and allow clarification and flexibility. Observation directly perceives behavior but accurate prediction is difficult. Validity and reliability are important criteria for measuring research instruments. Validity refers to what an instrument measures, and is tested through face validity, content validity, and construct validity. Reliability is the consistency of measurement.
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The document provides guidance on designing effective questionnaires. It emphasizes that questionnaires must have well-defined objectives in order to ask relevant questions and draw meaningful conclusions from the responses. Questions should follow logically from clear objectives. It also stresses that both open-ended and closed-format questions each have advantages, and the type of questions used should depend on the specific information needed. Demographic questions can help analyze response patterns among different groups. Overall, carefully considering objectives, question types, and question wording is essential for creating a questionnaire that efficiently gathers high-quality data.
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This document discusses various methods and considerations for collecting and analyzing data. It describes primary and secondary data sources and qualitative and quantitative methods. It also discusses different research tools like questionnaires, interviews, and mechanical devices. Guidelines are provided for developing questionnaires and conducting interviews. Different types of questions, interview formats, and potential issues are outlined. Overall, the document provides an overview of data collection methods and best practices for tools like questionnaires and interviews.
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Here are some data collection methods that can be used if you don't have direct access to the population of interest:
- Remote key informant interviews: Conduct interviews over the phone or online platforms with people who have knowledge about the population/area but are not directly part of it, such as local leaders, aid workers, journalists etc.
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FCS 681 Lecture 5
Sampling
What is sampling and Why sample?Selecting part of a larger groupPurpose is to make generalizations about the population from the smaller group.Why not study everyone?
--Cost: US Decennial Census, 1990, cost $1.5 billion, 2010, $14 billion
--Quality: When data are collected on a large scale, quality declines
--Timeliness
Advantages of samplingSelecting less than the entire population has advantages:
Less expensive
Less time
Better quality control
Key Sampling Concepts
Copyright ©2002, William M.K. Trochim, All Rights Reserved
Key sampling conceptsDistinction between the theoretical population and the actual study populationGeneralizations can be made only to the actual population
Theoretical Population
The Study or Accessible Population
Theoretical
Study
Sampling Frame
The list or procedure defining the POPULATION (From which the sample will be drawn.)
Examples:
Telephone book
Voters list
Mailing list
Distinguish sampling frame from sample.
Theoretical
Study
Sampling Frame
Selected Sample
Those selected to participate in the study
Not all will necessarily participate
--Non deliverable surveys
--Incomplete surveys
Theoretical
Study
Sampling Frame
Selected Sample
Actual
sample
Subjects who complete the study and data is used in data analysis
Ratio of actual to selected is called response rate
Key Concepts in samplingRandom sampling error
--the difference between the sample results and the results of a census conducted using identical procedure.Systematic sampling error
--due to sampling methods or imperfection in execution. Sampling bias
--biased results due to improper sampling (e.g., a sample of a population in which some members of the population are less likely to be included than others)
Sampling bias: an example1936 Presidential election. Literary Digest poll through telephone incorrectly predicted Alf Landon the winnerProblems?
--a biased sample
--response bias: respondents were more likely to express their preference; non-respondents had a different opinion
Rule:If you wish to make inferences to the population from which the sample was drawn, a Probability sample must be used.
--each element of the population has a known non-zero probability of selection.
--Randomness is the bases for sample selection
--Tend to give approximate representation of the population
--Allow for prediction of size of error in estimation
Key concepts: sample sizeThe larger the better?The main key is not size, but representativenessRule of thumb for Quantitative research
--A minimum of 30 per group
--A minimum of 10 per variable for multiple regressionIn Qualitative research, it doesn't matter as much
-- Not trying to infer generalizability
Types of Samples
Probability
Non-Probability
Convenience
Purposive
Simple Random
Systematic Random
Multi-stage Random
Stratified Random
Cluster Random
Quota
Simple Random SamplingEach element in the po
Chapter5.ppt
This document discusses various sampling methods used in research. It defines key terms like population, element, parameter, and statistic. It also describes different sampling techniques including probability methods like random sampling, stratified sampling, and cluster sampling as well as non-probability methods like convenience sampling, judgment sampling, and snowball sampling. The document highlights important considerations for sample size and achieving high response rates.
Chapter5
This document discusses various sampling methods used in research. It defines key terms like population, element, parameter, and statistic. It also describes different sampling techniques including probability methods like random sampling, stratified sampling, and cluster sampling as well as non-probability methods like convenience sampling, judgment sampling, and snowball sampling. The document highlights important considerations for sample size and achieving high response rates.
Chapter5.ppt
This document discusses various sampling methods used in research. It defines key terms like population, element, parameter, and statistic. It also describes different sampling techniques including probability methods like random sampling, stratified sampling, and cluster sampling as well as non-probability methods like convenience sampling, judgment sampling, and snowball sampling. The document highlights important considerations for sample size and achieving high response rates.
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Primary data techniques.
- 1. Primary Research Categories Quantitative Research Numerical Statistically reliable Projectable to a broader population
- 2. Sampling Methods: Random Samples – equal chance of anyone being picked May select those not in the target group – indiscriminate Sample sizes may need to be Large to be representative Can be very expensive Quantitative Research Categories
- 3. Stratified or Segment Random Sampling Samples on the basis of a representative strata or segment Still random but more focussed May give more relevant information May be more cost effective Quantitative Research Categories
- 4. Quota Sampling Again – by segment Not randomly selected Specific number on each segment are interviewed, etc. May not be fully representative Cheaper method Quantitative Research Categories
- 5. Qualitative Research In-depth, insight generating Non-numerical ‘Directional’ Common Techniques Personal interviews (depth, one-on-one) Focus groups (8-12) and mini-groups (3- 6) Qualitative Research Categories
- 6. METHODS OBSERVATION METHOD Through personal observation PERSONAL INTERVIEW Through Questionnaire TELEPHONE INTERVIEW Through Call outcomes, Call timings MAIL SURVEY Through Mailed Questionnaire