Data Collection and Sampling

What is the purpose of data collection in research?

• To make the research process more difficult

• To waste time and resources

• To confuse researchers

• To gather information and evidence for research purposes.

What are the two main types of data collection methods?

• primary and secondary

• random and systematic

• experimental and observational

• quantitative and qualitative

What is the difference between primary and secondary data?

• Primary data is more expensive to collect than secondary data.

• Primary data is more reliable than secondary data.

• Primary data is collected firsthand by the researcher, while secondary data is collected by someone else.

• Primary data is more time-consuming to collect than secondary data.

What are the advantages of using interviews as a data collection method?

• Interviews can be time-consuming and costly.

• Interviews may introduce bias in the data collection process.

• Interviews allow for in-depth and detailed information to be gathered, enable observation of non-verbal cues, and provide qualitative data.

• Interviews may not be suitable for collecting large amounts of data.

What is random sampling?

• A method of selecting a subset of individuals from a larger population in a way that each individual has an equal chance of being chosen.

• A method of selecting individuals based on their income level.

• A method of selecting individuals based on their gender.

• A method of selecting individuals based on their age.

What are the different types of random sampling techniques?

• convenience sampling

• quota sampling

• simple random sampling, stratified random sampling, cluster sampling, and systematic random sampling

• purposive sampling

What is simple random sampling?

• Method of selecting a subset of individuals from a larger population with equal chances of selection.

• Method of selecting a subset of individuals from a larger population with unequal chances of selection.

• Method of selecting a subset of individuals from a larger population with no chances of selection.

• Method of selecting a subset of individuals from a smaller population with equal chances of selection.

What is stratified random sampling?

• Sampling technique where the population is divided into distinct subgroups or strata, and then a non-random sample is taken from each stratum.

• Sampling technique where the population is divided into overlapping subgroups or strata, and then a random sample is taken from each stratum.

• Sampling technique where the population is divided into distinct subgroups or strata, and then a random sample is taken from the entire population.

• Sampling technique where the population is divided into distinct subgroups or strata, and then a random sample is taken from each stratum.

What is cluster sampling?

• Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of clusters is selected. Then, all individuals within the selected clusters are included in the sample.

• Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of clusters is excluded from the sample.

• Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of individuals from each cluster is selected.

• Cluster sampling is a sampling technique where the population is divided into clusters, and a random sample of individuals within each cluster is selected.

What are the advantages of using random sampling in research?

• Random sampling increases the likelihood of obtaining accurate results.

• Random sampling eliminates the need for statistical analysis.

• Random sampling ensures that all individuals in the population are included in the sample.

• Random sampling helps to ensure that the sample is representative of the population, reduces bias, and allows for generalization of findings to the larger population.