Final Exam - Weeks 5 & 6 Flashcards
Sample size calculation and choosing how many people should be sampled for a quantitative study depends on what factors?
- Research question
- How data will be analyzed
- Level of statistical significance
- Statistical power
- The effect size
The level of statistical significance is usually chosen at ________ or _________.
0.05 or 0.01
Why should the needed sample size be calculated before doing a study?
Make sure it is possible to recruit enough people into the study to detect a difference if it exists (have enough power to detect a difference).
In quantitative sampling the goal is to select a representative sample so you can generalize to the larger study population = good _______________________
external validity
Explain the 5 steps in sampling for quantitative studies.
- Define population: by specifying criteria
- Develop sampling plan: select the sampling method, try to minimize systematic error, simple random sampling, systematic, stratified, cluster
- Determine sample size: will depend on a number of factors, including the size of the population, the level of precision you require, and the amount of variability in the population.
- Implement sampling procedures:
- Compare critical values of sample to population
Explain systematic sampling.
Systematic sampling is a method of probability sampling where every nth member of a population is selected to be part of the sample. The process of selecting the sample involves selecting a random starting point from the population, and then selecting every nth element after that starting point.
Explain stratified random sampling.
Stratified random sampling is a method of probability sampling where the population is divided into smaller subgroups, or strata, based on certain characteristics that are relevant to the study. Then, a random sample is selected from each stratum, proportional to the size of that stratum within the population. This ensures that each subgroup is represented in the sample, allowing for more accurate estimates of population characteristics.
_______________ sampling has the advantage of ensuring that each subgroup is represented in the sample, allowing for more accurate estimates of population characteristics. It also reduces sampling error and increases precision.
Stratified random sampling.
Explain cluster sampling.
Cluster sampling is a method of probability sampling where the population is divided into clusters or groups, and a random sample of those clusters is selected for inclusion in the study. This method is often used when the population is spread out over a large geographical area or is difficult to access.
Suppose you want to conduct a study on the health of children in a particular region of a country. The region is divided into 20 school districts, and you want to select a sample of 400 children. What type of sampling might you use?
Cluster sampling (multistage sampling)
When it is not feasible to get a probability sample, or a sampling frame of the population is not available then ___________ methods are used.
non-random.
What is a sampling frame?
A sampling frame is a list or representation of the population from which a sample is selected. It serves as a basis for identifying and selecting the sample.
Name some types of non-probability sampling.
Convenience sampling, purposive sampling, snowball sampling.
Explain convenience sampling.
Individuals or units are selected for inclusion in the study based on their availability and willingness to participate. It is one of the easiest and least expensive methods of sampling, but it is also one of the least reliable.
Explain purposive sampling.
Involves selecting individuals or units for inclusion in the study based on specific criteria or purpose. In this method, the researcher selects the sample based on their knowledge and understanding of the population and the research question (deliberate selection of participants based on certain criteria).
Explain snowball sampling
Technically this type of sampling is a subtype of purposive sampling. Snowball sampling involves selecting participants based on referrals from other participants. The researcher starts with a few individuals who meet the selection criteria and then asks them to refer others who may also meet the criteria.
What is methodological rigor and why is it important?
Methodological rigor refers to the degree to which a research study is designed and conducted with high standards of quality and rigor, such that the study can be trusted to produce valid and reliable results.
Methodological rigor is essential in research because it ensures that the study is conducted in a way that minimizes bias and maximizes the accuracy and generalizability of the findings. This includes designing the study with appropriate research questions and hypotheses, selecting appropriate research methods and measures, using appropriate statistical analyses, and ensuring that the sample is representative and the data is reliable.
Explain what a priori hypothesis is.
A hypothesis based on assumed principles and deductions from conclusions of previous research, and are generated prior to a new study taking place.
The two levels of quantitative statistical analysis are:
- Descriptive: used to summarize the data
- Inferential: used to draw conclusions about population parameters based on data from sample
Frequency distribution, measures of central tendency, and measures of dispersion/variability are all what type of measure?
Descriptive measurements.
What are measures of central tendency?
Statistics that describe the location of the center of the distribution of numerical and ordinal measurements (mean, median, mode).
What are measures of dispersion/variability?
Statistics that describe the degree of dispersion of the differences among scores (range, standard deviation, variance, coefficient of variation, percentiles).
Explain the difference between median, mean, and mode.
The mean is also known as the average and is calculated by adding up all the values in a set of data and dividing by the total number of values in the set.
The median is the middle value in a set of data that has been arranged in numerical order. If the data set has an even number of values, the median is calculated as the average of the two middle values.
The mode is the most frequently occurring value in a set of data. It is the value that appears the most number of times and can be used to describe the most common value in a data set.