Research Lec 2

Question 1

cofounding variable

Answer 1

another variable that might affect either your IV or DV (unintentional)

Question 2

quantitative

Answer 2

testing theories using numbers

Question 3

qualitative

Answer 3

testing theories using language

focuses on broad descriptions and understanding complex phenomena without direct manipulation

inteview

Question 4

between qualitative and quantitative which one do you control the IV?

Answer 4

quantitative

Question 5

single-subject

Answer 5

one or few participants are measured many times in order to better understand the process

usually a unique population

Question 6

scientific/alternative hypothesis:

Answer 6

statement about the expected outcome or relationship between variables of a study

Question 7

null hypothesis

Answer 7

no relationship

Question 8

which hypothesis is usually in the manuscript?

Answer 8

?

Question 9

The ____ of data is one of the key factors affecting the way you analyze the data

Answer 9

level

Question 10

what are the different levels of data

Answer 10

nominal - naming

ordinal - ordered set with direction

interval/ratio - ordered series of equal sized categories. direction AND magnitude nominal and ordinal

Question 11

nominal and ordinal variables are ____ data

whereas interval/ration are ___ data

Answer 11

qualitative

quantitative

Question 12

“no help, some help, independent” is an example of what kind of data

Answer 12

ordinal

Question 13

parameter statistics definition and type of data associated

Answer 13

Statistical methods that assume your data follows a specific distribution, usually a normal (bell-curve) distribution.

quantitative data - interval and ratio

need big enough sample size

Question 14

nonparametric statistics definition and type of data

Answer 14

Statistical methods that don’t assume a specific distribution — they’re more flexible, or sample size is small

quantitative or qualitative - nominal and ordinal

Question 15

type of data
ROM -
GAIT -
MMT -
Zip codes -
NPRS 0-10 pain scale -

Answer 15

ratio
ratio
ordinal
nominal
interval?

Question 16

what are the three types of research studies

Answer 16

descriptive
exploratory
experimental

Question 17

descriptive studies

Answer 17

describes data, no statistical analyses looking for relationships

retrospective data (previously collected), normative, qualitative

Question 18

exploratory studies

Answer 18

looking for relationships between variables

case-control, quasi-experimental, single subject

correlational/predictive/methodological

Question 19

experimental

Answer 19

true experimental design with randomization

RCT

also case-control, quasi, single subject

Question 20

T/F: one study alone can prove something

Answer 20

false

Question 21

goal of a true experiment

Answer 21

to demonstrate a cause and effect relationship between two variables

Question 22

quasi-experimental

Answer 22

do not manipulate IV to differentiate the groups, they use pre-existing participant variables

ex: pre/post op, dx vs dx

Question 23

alpha

Answer 23

The cutoff we choose to decide if something is statistically significant

The point which you would consider the result highly unlikely to be by “random” error or coincidence, therefore it must represent meaning or pattern or be significant.

0.05

Question 24

p value

Answer 24

The actual result from your test — it tells you how likely it is that your results happened by random chance/sampling error

if p = 0.0036, then there is a .36% chance that our decision to reject the null hypothesis is wrong

(rejected bc p < .05)

Question 25

research validity

Answer 25

the extent to which the conclusions of the research are believable and useful

Question 26

internal validity

Answer 26

how confident you can be that the results of a study are due to the intervention or variable being tested - is there evidence that the IV caused a change in dependent

Question 27

A ___ is the best design to maximize internal validity

Answer 27

RCT

Question 28

what are some things the could affect a change in DV other than IV?

Answer 28

history maturation attrition/mortality repeated testing instrumentation regression to the mean experimenter bias selection KNOW THESE

Question 29

construct validity

Answer 29

are we measuring the construct we think were measuring? say were measuring "fitness" and use BMI as definition of fitness

Question 30

external validity

Answer 30

can the results be generalized to my population? ex: if you do biceps training in elderly women, you can only generalize to elderly women

Question 31

Statistical conclusion validity

Answer 31

violation of statistical assumptions or used wrong test for type of data usually due to low power

Question 32

statistical power

Answer 32

Statistical power is the ability of a test to detect a true effect if it really exists High power = good chance of detecting a real effect low power = high chance of false negative, small sample size

Question 33

type 1 error

Answer 33

reject null when you shouldn't

Question 34

type II error

Answer 34

fail to reject the null

Question 35

which error is typically due to small sample size/low power

Answer 35

type 2

Question 36

simple random sample

Answer 36

everyone in your population of students in Tx has an equal chance of being selected

Question 37

systematic sampling

Answer 37

select students in a certain order - select every 20th student

Question 38

stratified sampling

Answer 38

the sample frame is divided into parts or sections, ex: randomly select 10 students from every program

Question 39

cluster sampling

Answer 39

the sample frame is divided into parts or sections but only certain parts or sections are used, however all members of the parts or sections are sampled ex - random students are selected from three randomly selected programs entire clusters slected

Question 40

convenience sampling

Answer 40

the members of the sample frame volunteer or self-select

Question 41

sampling error

Answer 41

Sampling error is the difference between the results from your sample and the actual values in the full population — just because you're looking at a subset, not the whole group.