non-experimental methods
interview
observation
case study
correlations
content analysis
experimental methods
investigating whether there is a cause and effect relationship between IV and DV
MORE sleep = HIGHER score ??
or
LESS sleep = LOWER score
Experimental/alternative hypothesis
-what you genuinely think will happen/ educated guess based on past research or observation
-states that IV will affect the DV
-both variables must be operationalised
non-directional/ two-tailed hypothesis
(think of a two-tailed cat it can go both ways)
&
Directional/ one-tailed hypothesis
-does NOT tell you which direction results are predicted to go
-it is left open as to the concluded effect
e.g there will be a difference in time taken for both genders to complete the puzzle
as opposed to directional hypothesis:
-does tell you which way the results are predicted to go
e.g males will complete the puzzle in less time than females.
Null hypothesis
predicting that the change in the IV does not have any effect on the DV
-has no direction or tails
-both variables still have to be operationalised
Hypothesis
A statement which predicts the potential results of a study
Independent variable (change)
IV
Be specific!
-always has 2 parts/levels that can be compared e.g silent vs pop music playing
-can be naturally occurring e.g age
-
Dependent variable (measure)
DV
-‘might’ be affected by the IV
-has to be measurable in some way
e.g number of words recalled after 3 minutes
Operationalise
-being VERY specific about the IV and DV
-allows the study to be replicable
(doesn’t have to be long just precise)
Extraneous Variables
:variables that are a nuisance that may affect the DV that aren’t the IV.
-should be controlled or dealt with from the start
e.g. age, background noise, memory ability, gender
3 types:
1) Participant variable: differences between the individual participants (personality, age, gender, intelligence, concentration, motivation)
2) Situational variable
3) Demand characteristics
Situational variable - an extraneous variable
differences in the situation/way participants are tested (background noise, time of day, temperature, weather)
Demand characteristics-an extraneous variable
when participants try to work out what’s going on in the experiment and change their behaviours according to what they think the researcher is looking for, their behaviour is not natural anymore
1)please-you effect: tries to do things to please the researcher
2)screw-you effect: tries to mess everything up for the researcher
Investigator effects
any unwanted influence of the investigator on the DV
-usually unintentional
-may be because of the way an experiment is done e.g picking certain participants for each group because of subconscious bias or explaining the instructions better to one group
= use a script etc
Participant variable- an extraneous variable
differences between the individual participants (personality, age, gender, intelligence, concentration, motivation)
Standardisation
ensuring the procedures used to test participants are the same for all participants
e.g use a script, standard equipment, timings
:we want only the IV to vary not environment
Randomisation
when the researcher does not choose something themselves and every potential option has an equal chance of happening
e.g using a random word generator for the word test, using names out of a hat for groups (random sampling) or for their environment (random allocation)
Control group
a group that is not exposed to the variable being tested, serves as a base line to compare against the experimental group (basically the group that tests the nothing part)
e.g one group is tested with coffee and the control group without coffee.
Order effects-an extraneous variable
when participants’ responses change because they have done the test before
1) practice effect: they get better the more times they repeat the test
2) fatigue effect: they get more tired or worse after each repetition (e.g sport studies)
Validity VS Reliability
Validity: how accurately a method measures what it is intended to measure.
Reliability: the consistency of a measure. A reliable measure will produce the same results under the same conditions each time it is applied.
If a study lacks reliability it lacks validity
if it lacks validity it still may be reliable e.g a scales weighs the object the same every time but every time it is wrong, reliable but not valid.