Foundations of Quantitative Research

Experimental or Non-Experimental?:

Physical activity measurement in people with spinal cord injury: Comparison of accelerometry and self-report (the physical activity recall assessment for people with spinal cord injury.

Non-experimental

just comparing reports

Experimental or Non-Experimental?:

Using variance to explore the diagnostic utility of baseline concussion testing.

Experimental

“using variance to explore…”

actively trying things

what are the 4 foundational concepts of quantitative research?

sampling

variables

hypothesis testing

correlation and causation

population

the total number of possible units or elements that could be included in a study

possible units:

individual, a team, a muscle cell, a class of students

sample

a subset of the population used to represent the population

theoretical population

everyone youre interested in studying

everyone that contains the characteristics youre looking at

eg: how does caffeine impact sprint running

theoretical population criteria:

• people who sprint 100m

• people not yet using caffeine

• elite healthy athlete

the study population

everyone you have feasible access to that meets the criteria

eg: how does caffeine impact sprint running

study pop:

sprinters in the lower mainland universities that meet our criteria

the sampling frame

how you get access to the people in your study population… how do you get them to come to your lab?

e.g., where are you advertising? are you giving incentives?

random sampling

any person in the study population has an EQUAL chance of ending up in the actual sample population

problem: may end up oversampling a certain group (e.g., mostly third years because this is the largest year at uni, they will be overrepresented in study compared to the other years)

stratified random sampling

population is divided by a characteristic and then randomly sampled

e.g., dividing a group by year in uni before randomly sampling them from each group

systematic sampling

e.g., pick every 100th person

problem: have potential to oversample certain groups (e.g., mostly third years because this is the largest year at uni, they will be overrepresented in study compared to the other years)

purposive sampling

selection is based on specific criteria (in order to generate info rich data)

use information rich cases

have a reason why these cases are selected

when to use purposive sampling

when studying a rare disease —> not a lot of people have the disease: will go to a registry book where people who have it are registered and take your sample from thereexamples

examples