Sample design
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The document discusses sample design, which is a plan for selecting a sample from a population. It defines key aspects of sample design like sampling units, sampling frames, sample size, and sampling methods. The document contrasts random/probability sampling, which allows statistical inference, with non-random/non-probability sampling. It also outlines major types of random and non-random sampling techniques and discusses criteria for selecting a sampling procedure like controlling bias and applying results to the population.
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Sampling design
The document discusses various sampling techniques used in research including probability and non-probability sampling. It explains key concepts like population, sample, sampling frame, sampling error, systematic error. It describes different probability sampling designs such as simple random sampling, stratified sampling, cluster sampling and multistage sampling. It also discusses non-probability sampling techniques like convenience sampling and quota sampling. The document provides advantages and limitations of different sampling methods and guidelines for selecting an appropriate sampling design.
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This document discusses sampling methods for research. It defines key terms like population, sample, and sampling. It covers the main types of sampling:
1. Probability sampling methods like simple random sampling, stratified sampling, and systematic sampling which give all units an equal chance of selection.
2. Non-probability sampling methods like judgement, quota, and convenience sampling which do not give all units an equal chance and can be biased.
3. Factors to consider for good sampling include accuracy, precision, defining the target population, determining the sampling method and size. The document provides details on each sampling technique and their advantages and disadvantages.
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This document discusses various sampling techniques used in research. It begins by defining key terms like population, sample, and sampling unit. It then explains different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and probability proportional to size sampling. For each method, it provides details on the procedure and highlights advantages and disadvantages. The document aims to help readers understand different sampling designs and how to select appropriate techniques for research studies.
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Chapter Eight Quantitative Methods
This document provides an outline and guidelines for developing quantitative research methods plans for surveys and experiments. It defines key components and considerations for survey design, experimental design, developing instrumentation, identifying variables, sampling procedures, data analysis, and interpreting results. Checklists are provided to guide the planning of surveys and experiments. Potential threats to validity are also discussed.
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Sample design
- 2. Sample Design • A sample design is framework, or road map, that serves as base for selection of a survey sample and affects many other important aspects of a survey as well. For example, a researcher may want to interview males through a telephone survey. • Selection of a suitable sample design method ensures that the samples you invest time and money into collecting can support the inferences you want to make.
- 3. Implication of a sample design • A sample design is a definite plan for obtaining a sample from given population. • Technique or the procedure researcher would adopt in selecting items. • Sample design is determined before data are collected. • Researcher must select sample design which should be reliable and appropriate for the study.
- 4. Steps in Sample Design Sample design is the heart of research work • Define the universe • Sampling unit • Source list or sampling frame • Size of sample • Sampling method or technique • Parameters of interest • Budgetary constraints • Select the sample
- 5. Methods/Types of Sampling Samples may be grouped into broad classes according to their method of selection. Namely: • Random or Probability Sampling: Involves random selection, allowing you to make statistical inferences about the whole group. • Non-Random or Non-Probability Sampling: Involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data.
- 6. Random or Probability Sampling Major random or probability samples are: • Simple random sample • Systematic sample • Stratified random sample • Multi-stage random sample • Cluster sample or area sample • Sequential sample or sample in instalments • Replicated or interpenetrating sample
- 7. Non-Random or Non-Probability Sampling Major Non-random or Non-probability samples are: • Purposive sample • Quota sample • Convenience sample
- 8. Criteria for selecting a sampling procedure In selecting a sampling procedure must remember that two costs are involved in a sampling analysis: • The cost of collecting data and, • The cost of an incorrect inference resulting from the data. Researcher must keep in view two causes of incorrect inferences: systematic bias and sampling error
- 9. Characteristics of a good sample design • Sample design must result in a truly representative sample. • Sample design must be viable in the context of funds available for the research study. • Sample design must be such so that systematic bias can be controlled in a better way. • Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.
- 10. Measurement scales Scales of measurement in research and statistics are different ways in which variables are defined and grouped into different categories. It describes nature of values assigned to variables in a data set. Scaling Techniques Scaling technique is a method of placing respondents in continuation of gradual change in pre- assigned values, symbols or numbers based on the features of a particular object as per the defined rules: •Paired Comparison •Rank Order •Constant Sum •Q-Sort Scaling •Continuous Rating Scales •Itemized Rating Scale