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پستهای کانال
A brief mention of the important sample designs is as follows:
👍a. Deliberate sampling: Deliberate sampling is also known as purposive or non-probability sampling. This sampling method involves purposive or deliberate selection of particular units of the universe for constituting a sample which represents the universe. When population elements are selected for inclusion in the sample based on the ease of access, it can be called convenience sampling.
👍b. Simple random sampling: This type of sampling is also known as chance sampling or probability sampling where each and every item in the population has an equal chance of inclusion in the sample and each one of the possible samples, in case of finite universe, has the same probability of being selected.
👍c.Systematic sampling: In some instances the most practical way of sampling is to select every 15th name on a list, every 10th house on one side of a street and so on. Sampling of this type is known as systematic sampling. An element of randomness is usually introduced into this kind of sampling by using random numbers to pick up the unit with which to start. This procedure is useful when sampling frame is available in the form of a list.
👍d.Stratified sampling: If the population from which a sample is to be drawn does not constitute homogeneous group, then stratified sampling technique is applied so as to obtain representative sample. In this technique, the population is stratified into a number of non-overlapping subpopulations or strata and sample items are selected from each stratum.
👍e. Quota sampling: In stratified sampling the cost of taking random samples from individual strata is often so expensive that interviewers are simply given quota to be filled from different strata, the actual selection of items for sample being left to the interviewer judgment.
👍f. Cluster sampling and area sampling: Cluster sampling involves grouping the population and then selecting the groups or the clusters rather than individual elements for inclusion in the sample. Suppose some departmental store wishes to sample its credit card holders.
👍g. Multi-stage sampling: This is a further development of the idea of cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an entire country. Under multi-stage sampling the first stage may be to select large primary sampling units such as states, then districts, then towns and finally certain families within towns.
👍h. Sequential sampling: This is somewhat a complex sample design where the ultimate size of the sample is not fixed in advance but is determined according to mathematical decisions on the basis of information yielded as survey progresses. This design is usually adopted under acceptance sampling plan in the context of statistical quality control.
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| 2 | Identification of a Research Problem
🍏The following steps are to be followed in identifying a research problem;
Step I Determining the field of research in which a researcher is keen to do the research work.
Step II The researcher should develop the mastery on the area or it should be the field of his specialization.
Step III He/She should review the research conducted in area to know the recent trend and studies are being conducted in the area.
Step IV On the basis of review, he/she should consider the priority field of the study.
Step V He/She should draw an analogy and insight in identifying a problem or employ his personal experience of the field in locating the problem. He/She may take help of supervisor or expert of the field.
Step VI He/She should pin point specific aspect of the problem which is to be investigated
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| 4 | The main characteristics of a good sample
✍️ A good sample is the true representative of the population corresponding to its properties.
The population is known as aggregate of certain properties and sample is called sub-aggregate
of the universe.
✍️A good sample is free from bias, the sample does not permit prejudices th65e learning and pre-
conception, imaginations of the investigator to influence its choice.
✍️A good sample is an objective one, it refers objectivity in selecting procedure or absence of
subjective elements from the situation.
✍️ A good sample maintains accuracy. It yields an accurate estimates or statistics and does not
involve errors.
✍️A good sample is comprehensive in nature. This feature of a sample is closely linked with
true-representativeness. Comprehensiveness is a quality of a sample which is controlled by
specific purpose of the investigation. A sample may be comprehensive in traits but may not
be a good representative of the population.
✍️ A good sample is also economical from energy, time and money point of view.
✍️ The subjects of good sample are easily approachable. The research tools can be administered
on them and data can be collected easily.
✍️The size of good sample is such that it yields an accurate results. The probability of error
can be estimated.
✍️ A good sample makes the research work more feasible.
✍️ A good sample has the practicability for research situation.
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| 5 | بدون متن... | 912 |
| 6 | Reliability and validity
🍎Reliability and validity are two key concepts in research methodology, particularly in the context of measuring instruments, such as surveys, tests, or questionnaires. They are used to assess the quality and rigor of a research study or measurement tool.
✍️Reliability
👍Reliability refers to the consistency and stability of a measure. A reliable instrument produces similar results under consistent conditions.
Types of Reliability:
👍Test-Retest Reliability: Measures the consistency of results when the same test is administered to the same group at different times.
👍 Inter-Rater Reliability: Assesses the degree of agreement between different raters or observers.
👍Internal Consistency Reliability: Evaluates the consistency of items within a test (e.g., Cronbach's Alpha).
👍Parallel-Forms Reliability: Measures the correlation between two equivalent versions of a test.
How to Test Reliability:
🌱Use statistical measures like Cronbach's Alpha (for internal consistency), Pearson's correlation coefficient (for test-retest reliability), or Cohen's Kappa (for inter-rater reliability).
🌱A reliability coefficient of 0.7 or higher is generally considered acceptable.
🌎Validity
👉🏿Validity refers to the extent to which a tool measures what it is intended to measure. A valid instrument accurately reflects the concept it is supposed to measure.
Types of Validity:
🤙Content Validity: Ensures the test covers all aspects of the concept being measured.
🤙Construct Validity: Assesses whether the test measures the theoretical construct it claims to measure.
🤙 Convergent Validity: Measures how closely the test is related to other tests that measure the same
construct.
🤙 Discriminant Validity: Ensures the test is not related to measures of different constructs.
🤙 Criterion Validity: Evaluates how well the test predicts or correlates with a criterion.
🤙 Concurrent Validity: Measures how well the test correlates with a criterion measured simultaneously.
🤙 Predictive Validity: Assesses how well the test predicts future outcomes.
How to Test Validity:
✏️ Use expert reviews for content validity.
✏️ Conduct factor analysis or correlation studies for construct validity.
✏️ Compare the test results with an established criterion for criterion validity.
🍏Key Differences:
💎Reliability is about consistency (does the test produce stable results?).
💎Validity is about accuracy (does the test measure what it claims to measure?).
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