2 - Collecting Quantitative Data

Data Collection

- Systematic process of gathering observations or measurements

- Whether you are performing, research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem

Population

- group of individuals who have the same characteristics

Complete Enumeration Method

- when the entire population answers the survey. Used when the population is very small

Sample

- subgroup of the target population that the researcher plans to study for generalizing about the target population

Representative

- selection of individuals from a sample of population such that the individuals selected are typical of the population under study, enabling you to draw conclusions from the sample about the population as a whole

Slovin’s Formula

- Many journal articles state that this is not accurate and therefore isn’t as used anymore

Raosoft Sample Size Calculator

- alternative to the Slovin’s formula

G*Power

- Application where the input is very detailed

- Used for higher forms of statistics and higher levels (Grad school) as it is more accurate

- It considers what kind of research (e.g., comparative, correlational) and the statistical tool

Probability Sampling

- Involves random sampling, allowing you to make strong statistical inferences about the whole group

Non-Probability Sampling

- Involves non-random selection based on convenience or other criteria, allowing you to easily collect data

Simple Random Sampling

- Every member of the population has an equal chance of being selected. Sampling frame should include the whole population

- You can use tools like random number generators or other techniques that are based entirely on chance

- Participants are numbered and a random number generator selects who the sample will be

Systematic Sampling

- Easier than simple random sampling

- Every member of the population is listed with a number and individuals are chosen at regular intervals

Stratified Sampling

- Dividing the population into subpopulations that may differ in important ways; homogenous in nature

- Allows you to draw more precise conclusions by ensuring that every subgroup is properly represented in the sample

- Divide the population into subgroups (Strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role)

Cluster Sampling

- Divides the population into subgroups but each subgroup but each subgroup should have similar characteristics to the whole sample; heterogenous in nature

- Instead of sampling individuals from each subgroup, you randomly select entire subgroups

- Typically used when the sample is geographically limited

Convenience Sampling

- Simply including the individuals who happen to be most accessible to the researcher

- 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

- At risk for both sampling bias and selection bias

Voluntary Response Sampling

- Mainly based on ease of access

- Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g., responding to a public online survey)

- Always at least somewhat biased, as some people will inherently be more likely to volunteer than others, leading to self-selection bias

Purposive Sampling

- Judgement sampling

- Involves the researcher 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

- An effective purposive sample must have clear criteria and rationale for inclusion

- Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments

Snowball Sampling

- If the population is hard toa access, this can be used to recruit participants via other participants

- The number of people you have access to “snowballs” as you get in contact with more people

- Downside is representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others, leading to sampling bias

Quota Sampling

- Relies on the non-random selection of a pre-determined number or proportion of units (quota)

- Divide the population into mutually exclusive subgroups and then recruit sample nits until you reach your quota

- Aim is to control what or who makes up your sample