10 Questions
Match the random sampling method with its description:
Simple Random Sampling = Each member has an equal probability of being chosen Stratified Random Sampling = Ensures the sample is representative of each subgroup Systematic Sampling = Selects every 'rth' member after an initial starting point
Match the steps with the corresponding random sampling method:
List the members of target population, Choose starting point, Sample every 'rth' member = Simple Random Sampling Divide population into homogeneous subgroups, Ensure sample is representative of each stratum = Stratified Random Sampling Choose starting point, Select every 'rth' member until desired sample size is reached = Systematic Sampling
Match the key characteristic with the appropriate random sampling method:
Equal probability for each member to be chosen = Simple Random Sampling Division of population into subgroups based on common characteristics = Stratified Random Sampling Selection of every 'rth' member after a starting point = Systematic Sampling Ensuring sample represents each subgroup accurately = Stratified Random Sampling
Match the type of sampling interval selection with its corresponding method:
Randomly choosing 'r' between 1 and N = Simple Random Sampling Selecting a starting point and using a fixed interval 'r' = Systematic Sampling Ensuring each subgroup is represented accurately in the sample = Stratified Random Sampling Equal chance for all individuals or units to be selected into the sample = Simple Random Sampling
Match the goal of accurate representation with the suitable random sampling method:
To make sure the sample is representative of each stratum = Stratified Random Sampling To provide an equal probability for each individual to be selected = Simple Random Sampling To select members based on a fixed interval after a starting point = Systematic Sampling To improve accuracy by dividing the population into homogeneous subgroups = Stratified Random Sampling
Match the following sampling methods with their descriptions:
Simple random sampling = Selecting individuals randomly from the population Stratified random sampling = Identifying important strata, allocating sample proportionally, and selecting randomly within each stratum Systematic sampling = Selecting individuals at regular intervals after determining a sampling interval Cluster sampling = Dividing the population into clusters, then randomly selecting entire clusters to sample
Match the following steps with the correct sampling method:
Identify characteristics or strata, determine proportion, allocate sample size, select randomly in each stratum = Stratified random sampling Determine sampling interval, select every 'rth' individual, continue until desired sample size is reached = Systematic sampling Select individuals randomly, ensure each has an equal chance of being chosen = Simple random sampling Divide population into clusters, randomly select entire clusters for sampling = Cluster sampling
Match the following statements with the correct sampling method's advantage:
Less time-consuming and simpler to implement = Simple random sampling Ensures representation from all strata of the population = Stratified random sampling Easy to conduct when population is not large and units are easily identifiable = Systematic sampling Efficient when natural groupings are present within the population = Cluster sampling
Match the following scenarios with the appropriate sampling method:
Wanting to ensure fair representation of different age groups in a survey = Stratified random sampling Needing a method that is easy to implement for a small population = Systematic sampling Randomly selecting households by picking numbers from a hat = Simple random sampling Sampling households in different neighborhoods in a city = Cluster sampling
Match the following definitions with the correct sampling method:
Selecting individuals at equal intervals after an initial random start point = Systematic sampling Randomly choosing individuals without any specific pattern or criteria = Simple random sampling Dividing the population into subgroups based on characteristics of interest before selection = Stratified random sampling Sampling by dividing the population into naturally occurring groups before selection = Cluster sampling
Study Notes
Random Sampling Methods
Random sampling is a statistical method used to select a representative sample from a population to estimate key parameters of the whole group. It eliminates biases by providing equal chance to every individual or unit in the population to be selected into the sample. There are three main types of random sampling methods: simple random sampling, stratified random sampling, and systematic sampling.
Simple Random Sampling
In simple random sampling, also known as random sampling with replacement, each member of the target population has an equal probability of being chosen for the sample. This means each selection event is independent of all previous events. To carry out this type of sampling, follow these steps:
- List the members of your target population in order.
- Choose a starting point between 1 and N using any arbitrary constant, where N is the number of members.
- Starting at this point, sample every 'rth' member, where the sampling interval r is randomly chosen between 1 and N. This process continues until you reach your desired sample size.
Stratified Random Sampling
Stratified random sampling is an extension of simple random sampling that divides the population into homogeneous subgroups called strata. Each stratum is a subgroup of the population sharing a common characteristic. The goal is to ensure that the sample is representative of each stratum, which can lead to more accurate estimates. To carry out this type of sampling, follow these steps:
- Identify the characteristics or strata that are important for your sample.
- Determine the proportion of each stratum in the population.
- Allocate the total sample size proportionally to each stratum.
- Within each stratum, select individuals randomly.
Systematic Sampling
Systematic sampling is a simple method of sampling that involves selecting individuals at regular intervals from a population. This method is particularly useful when the population is not large and the sampling unit is easy to identify. To carry out this type of sampling, follow these steps:
- Determine the sampling interval, which is the distance between the individuals selected in the population.
- Starting with any individual, select every 'rth' individual, where the sampling interval r is determined by the size of the population.
- Continue this process until the desired sample size is reached.
Each of these random sampling methods has its own advantages and disadvantages, and the choice of method depends on the characteristics of the population and the objectives of the study.
Learn about the three main types of random sampling methods: simple random sampling, stratified random sampling, and systematic sampling. Understand how each method works and the steps involved in implementing them effectively.
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