Research Design and Reporting

Question 1

A ———— study collects data from a population at a single point in time to analyze and compare different groups or variables at that specific moment

Answer 1

cross-sectional

Question 1

A ———– study follows the same subjects over a period of time, collecting data at multiple time points to observe changes and trends over time.

Answer 1

longitudinal

Question 2

What are the key differences between experimental, observational, and quasi-experimental study designs?

Answer 2

:
-Experimental studies involve manipulation of variables with random assignment.
-Observational studies observe without intervention. —Quasi-experimental studies resemble experiments but lack random assignment.

Question 3

study design that involve researchers manipulating an independent variable to observe its effect on a dependent variable, often utilizing random assignment to control for confounding variables.

Answer 3

Experimental

Question 4

studie design that involves researchers watching and analysing naturally occurring events or phenomena without intervention, aiming to understand relationships or associations between variables.

Answer 4

Observational

Question 5

this study design resembles an experimental designs but lacks random assignment to treatment groups, often due to ethical or practical constraints, leading to potential biases in causal inference.

Answer 5

Quasi-experimental

Question 6

Quasi Experimental designs are prone to:

Answer 6

potential biases in causal inference caused by the lack of random assignment

Question 7

Quasi-experimental studies lack random assignment because they

Answer 7

using existing groups or conditions (as opposed to random allocation) because they aim to mimic experiments in real-world settings.

Question 8

Educational research, policy evaluations, clinical studies with ethical constraints, and organizational psychology often use —— research designs.

Answer 8

quasi-experimental

Question 8

What characterizes a between-subject design?

Answer 8

Data is collected from participants in relation to one condition only.

Question 9

What defines a within-subject design?

Answer 9

Data is collected from participants in relation to more than one condition, typically all conditions (typically all the conditions that are being assessed in the study)

Question 10

incorporates both between-subject and within-subject assessments is called a

Answer 10

mixed design

Question 11

What is another term for a within-subject design?

Answer 11

Repeated measures design.

Question 12

If I have taken data on a group of people that studies two variables, and I only took data one day and did not repeat it - is it a within-subjects or a between subjects design?

Answer 12

It is bewteen subjects as it has not been repeated. Although data was taken on more than one variable, there is only one set of data per variable. (If a repeat capture of data was take - say a day later - then it would be a between subjects design)

Question 13

Any design that is measuring the same individual more than once is a ——- subject design

Answer 13

Within Subjects design, which we also call repeated measures

Question 14

Sample types

Answer 14

Population based - representative, random
Convenience - not representative
Stratification - sampling base on pre-defined groups

Question 15

3 methods of data collection

Answer 15

In the field
survey
Laboratory

Question 16

Sampling Bias Types:

Answer 16

Self Selection (volunteering)
Healthy Bias (more healthy people liekly to volunteer)
Under Coverage Bias (people in care may be excluded for example)

Question 17

WEIRD (sample) stands for

Answer 17

Western educated Industrialised Rich Democratic

Question 18

—— of a measure tells you how precisely you are measuring something,

Answer 18

reliability

Question 19

——– of a measure tells you how accurate the measure is

Answer 19

validity

Question 20

This relates to consistency over time. If we repeat the measurement at a later date do we get the same answer?

Answer 20

Test-retest reliability.

Question 21

This relates to consistency across people. If someone else repeats the measurement (e.g., someone else rates my intelligence) will they produce the same answer?

Answer 21

Inter-rater reliability.

Question 22

This relates to consistency across theoretically-equivalent measurements. If I use a different set of bathroom scales to measure my weight does it give the same answer?

Answer 22

Parallel forms reliability.

Question 23

If a measurement is constructed from lots of different parts that perform similar functions (e.g., a personality questionnaire result is added up across several questions) do the individual parts tend to give similar answers.

Answer 23

Internal consistency reliability.

Question 24

“to be explained”
dependent variable (DV)
modern name:

Answer 24

outcome

Question 25

“to do the explaining”
independent variable (IV)
modern name:

Answer 25

predictor

Question 26

when and experiment fails, because it violates the structure of the “natural” world - it will give an _____ result

Answer 26

“artefactual” result

Question 27

——– validity refers to the extent to which you are able draw the correct conclusions about the causal relationships between variables.

Answer 27

Internal.
It’s called “internal” because it refers to the relationships between things “inside” the study.

Question 28

——– validity relates to the generalisability or applicability of your findings. That is, to what extent do you expect to see the same pattern of results in “real life” as you saw in your study.

Answer 28

External
(if it turns out that the results don’t actually generalise or apply to people and situations beyond the ones that you studied, then what you’ve got is a lack of external validity.)

Question 29

——– validity is basically a question of whether you’re measuring what you want to be measuring.

Answer 29

Construct

A measurement has good construct validity if it is actually measuring the correct theoretical construct, and bad construct validity if it doesn’t

Question 30

—— validity simply refers to whether or not a measure “looks like” it’s doing what it’s supposed to,

Answer 30

Face Validity

(face validity isn’t very important from a pure scientific perspective. After all, what we care about is whether or not the measure actually does what it’s supposed to do, not whether it looks like it does what it’s supposed to do)

Question 31

———- validity is a different notion of validity, which is similar to external validity, but less important. The idea is that, in order to be ecologically valid, the entire set up of the study should closely approximate the real world scenario that is being investigated

Answer 31

Ecological validity

(It relates mostly to whether the study “looks” right, but with a bit more rigour to it. The idea behind it is the intuition that a study that is ecologically valid is more likely to be externally valid. It’s no guarantee, of course)

Question 32

——– is an additional, often unmeasured variable5 that turns out to be related to both the predictors and the outcome.

Answer 32

Confounder

(The existence of confounders threatens the internal validity of the study because you can’t tell whether the predictor causes the outcome, or if the confounding variable causes it.)

Question 33

The possibility that your result is an ——— describes a threat to your external validity, because it raises the possibility that you can’t generalise or apply your results to the actual population that you care about.

Answer 33

artefact

(artefactual results tend to be a concern for experimental studies than for non-experimental studies.)

Question 34

———– effects are fundamentally about change over time.

Answer 34

maturational effects.

(maturation effects aren’t in response to specific events. Rather, they relate to how people change on their own over time.)

Question 34

——- effects refer to the possibility that specific events may occur during the study that might influence the outcome measure

Answer 34

History

Question 35

When running a very long experiment in the lab (say, something that goes for 3 hours) it’s very likely that people will begin to get bored and tired, and that this ————– effect will cause performance to decline regardless of anything else going on in the experiment

Answer 35

maturational

Question 36

is a history effect in which the “event” that influences the second measurement is the first measurement itself!

Answer 36

Repeated testing

Question 37

—— ——— occurs when participants in a study are systematically different from the population, leading to skewed or inaccurate results.

Answer 37

Selection bias

Question 38

——– refers to the reduction in participants or dropout rate over the course of a study, which can affect the validity and generalisability of results.

Answer 38

Attrition

Question 39

—————- Attrition: Participants drop out across different groups, leading to varied group compositions.

Answer 39

Heterogeneous or differential attrition (this is a kind of selection bias that is caused by the study itself. )

Question 41

———— Attrition: Participants drop out from similar groups, maintaining group similarities.

Answer 41

Homogeneous

Question 42

——– attrition, in which the attrition effect is the same for all groups, treatments or conditions.

Answer 42

Homogeneous

he attrition would be homogeneous if (and only if) the easily bored participants are dropping out of all of the conditions in my experiment at about the same rate

Question 43

——— occurs when participants who choose not to respond to a survey or study differ in meaningful ways from those who do respond, leading to skewed or unrepresentative results.

Answer 43

Non-response bias

Question 44

A ——- measures the number of standard deviations a data point is from the mean of a dataset, helping to standardise and compare values across different distributions.

Answer 44

Z-score

Question 45

A zscore helps convey how —— or ——– a data point is

Answer 45

usual or unusual

Question 46

a z score of 1.77 communicates it —– than one but ——- than 2 standard deviations from the mean

Answer 46

more

less

Question 46

Zscore equation

Answer 46

z-score = data point - mean divided by the standard deviation (from the mean)

Question 47

the tendency for extreme values in a dataset to move closer to the mean upon subsequent measurements, often observed in repeated measurements or interventions.

Answer 47

Regression to the mean

Question 47

—— ——-can come in multiple forms. The basic idea is that the experimenter, despite the best of intentions, can accidentally end up influencing the results of the experiment by subtly communicating the “right answer” or the “desired behaviour” to the participants

Answer 47

Experimenter bias

Question 48

—— effect, where if you expect great things of people they’ll tend to rise to the occasion. But if you expect them to fail then they’ll do that too.

Answer 48

Pygmalion

Question 49

trying to analyse your data in lots of different ways, you’ll eventually find something that “looks” like a real effect but isn’t. This is referred to as”

Answer 49

“data mining

Question 50

The different types of reliability commonly discussed in research are:

Answer 50

Test-Retest Reliability: Measures consistency by comparing the results of a test taken by the same group of people at two different times.

Internal Consistency Reliability: Assesses the consistency of results across items within a test or instrument, often measured using Cronbach’s alpha.

Inter-Rater Reliability: Examines the consistency between different raters or observers assessing the same phenomenon.

Parallel Forms Reliability: Compares the consistency of results between different versions of the same test.

Question 51

An effect to consider when perform subsequent or repat testing is the ——- — —- —-

Answer 51

regression to the mean
(statistical tendency that is likely, not guaranteed to occur, the more testing the more likely)