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• Theoretical framework describes the theoretical underpinnings of your work based on existing research.
• The formula for Slovin's formula is: n = N /(1+N ) 𝑒 2.
• When taking statistical samples, sometimes a lot is known about a population, sometimes a little and sometimes nothing at all.
• Slovin's formula is used when a sample is taken from a population, and it must be used to take into account confidence levels and margins of error.
• Conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.
• Theoretical framework often inspires the research question based on previous theories' predictions or understanding about the phenomena under investigation.
• Conceptual framework emerges from the research question, providing a contextualized structure for what exactly the research will explore.
• Theoretical frameworks provide four dimensions of insight for qualitative research that include: providing focus and organization to the study, exposing and obstructing meaning, connecting the study to existing scholarship and terms, and identifying strengths and weaknesses.
• Historical study is the ideal choice for studies that involve extensive examination of the past — including people, events and documents.
• Phenomenology is a wide-ranging form of study where the researcher looks to gather information that explains how individuals experience a phenomenon and how they feel about it.
• Grounded theory seeks to develop a theory surrounding a social issue, identifying problems in social scenes and defining how people deal with those problems.
• Ethnography is the study of a specific grouping within a culture, where researchers immerse themselves into the culture they are researching.
• An effective purposive sample must have clear criteria and rationale for inclusion.
• Snowball sampling is used when the population is hard to access, recruiting participants via other participants, but it can lead to sampling bias due to the reliance on participants recruiting others.
• Purposive sampling involves the researcher using their expertise to select a sample that is most useful to the purposes of the research, often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific.
• Voluntary response sampling is similar to a convenience sample, based on ease of access, where people volunteer themselves by responding to a public online survey.
• Quota sampling relies on the non-random selection of a predetermined number or proportion of units, this is called a quota, where you first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota.
• The aim of quota sampling is to control what or who makes up your sample.
• Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others, leading to self-selection bias.
• Slovin’s Formula is used to calculate sample size, it is computed as n = N / (1+N).
• Case studies are used to examine a person, group, community or institution, often using a bounded theory approach that confines the case study in terms of time or space.
• Convenience sampling is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results.
• In cluster sampling, instead of sampling individuals from each subgroup, you randomly select entire subgroups.
• Stratified sampling involves dividing the population into subpopulations that may differ in important ways.
• To use stratified sampling, the population is divided into subgroups (called strata) based on the relevant characteristic, such as gender identity, age range, income bracket, job role.
• Cluster sampling involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample.
• Non-probability sampling methods involve individuals being selected based on non-random criteria, and not every individual has a chance of being included.
• In systematic sampling, every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
• Convenience samples are at risk for both sampling bias and selection bias.
• Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct.
• Convenience sampling is a type of non-probability sampling method where the individuals who happen to be most accessible to the researcher are included.
• Stratified sampling allows for more precise conclusions by ensuring that every subgroup is properly represented in the sample.
• Simple random sampling involves assigning a number to every employee in a company database from 1 to 1000, and using a random number generator to select 100 numbers.
• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Focus groups are similar to interviews, but involve multiple participants at once, and are another route to obtaining responses and making interview observations.
• All participants chosen must share a unifying factor, which means they all must have a direct or indirect connection to the research question or subject being studied.
• Sampling methods are used to draw valid conclusions from your results, and there are two primary types: probability sampling and non-probability sampling.
• In a simple random sample, every member of the population has an equal chance of being selected, and your sampling frame should include the whole population.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
• Probability sampling means that every member of the population has a chance of being selected, and is mainly used in quantitative research.