1. Research Methods Flashcards
experiment
investigation looking for a cause and effect relationship
Independent Variable and Dependent Variable
the factor under investigation in
an experiment which is manipulated to create two or more conditions (levels) and is expected to be responsible for
changes in the dependent variable.
the factor in an experiment which is
measured and is expected to change under the influence of
the independent variable.
experimental and control condition
control condition: a level of the IV in an experiment from which the IV is absent. It is compared to one or more
experimental conditions (situations in an experiment which represent different levels of the IV)
laboratory experiment
a research method in which there is
an IV, a DV and strict controls. conducted in a setting that is not in the usual environment for the participants, but artificial
validity and reliability
validity - the extent to which the researcher is testing what they claim to be testing
reliability - the extent to which a procedure, task or measure is
consistent, for example that it would produce the same results
with the same people on each occasion.
field experiment
an independent variable is manipulated
and is expected to be responsible for changes in the dependent variable. It is conducted in the normal environment
for the participants for the behaviour being investigated.
natural experiment
the independent variable cannot be
directly manipulated by the experimenter. Instead they
study the effect of an existing difference or change. Since the researcher cannot manipulate the levels of the IV it is not a
true experiment.
experimental design:
independent measure
where a different group of participants is used for each level of the IV
experimental design:
repeated measures design
each participant performs every level of the IV
experimental design:
matched-pair design
participants are arranged into pairs. each pair is similar in ways that are important to the study and one member of each pair performs a different of the IV
demand characteristics
demand characteristics: features of the experimental situation which give away the aims, which reduces
the validity of the study.
confounding and participant variables
Confounding variables are those that affect other variables in a way that distorted associations between two variables. They confound the “true” relationship between two variables.
Example: You collect data on sunburns and ice cream consumption. You find that higher ice cream consumption is associated with a higher probability of sunburn. Does that mean ice cream consumption causes sunburn?
participant variables: individual differences between
participants (such as age, personality and intelligence) that could affect their behaviour in a study. They could hide or
exaggerate differences between levels of the IV.
order effects and a solution?
practice effect: performance
improves because they experience the experimental task more
than once
fatigue effect: performance
declines because they have experienced an experimental task, due to boredom or tiredness.
counterbalancing (used to overcome
in a repeated measures design):
Each possible order of levels of the IV is performed by a different sub-group of
participants. This can be described as an ABBA design, as half the participants do condition A then B, and half do B then A.
aim and hypothesis
aim - tells you the purpose of the
investigation. It is generally expressed in terms of what the
study intends to show.
hypothesis - a testable statement predicting a difference between levels of IV in an experiment/relationship between variables in a correlation
- main/alternative hypothesis - the hypothesis in a particular investigation. needs to be falsified (can be proven wrong)
non-directional and directional hypothesis
non-directional - predicting only that one variable will be related to another
e.g: that there will be a diff in the DV between levels of the IV in an experiment
directional: predicting the DIRECTION of a relationship between variables
null and main hypothesis
main/alternative hypothesis:
the hypothesis in a particular investigation. needs to be falsified (can be proven wrong)
null:
saying that there won’t be any difference or correlation in results or that the diff will be due to chance
operationalisation
the definition of variables so that they
can be accurately manipulated, measured or quantified and
replicated. This includes the IV and DV in experiments and the
two measured variables in correlations.
for example a study testing the eff ect of age on susceptibility to false
memories:
operationalising the IV:
young –> 20 - 30; 30 - 40…
old –> 40 - 50; 50 - 60…
operationalising the DV:
counting the number of details ‘remembered’ about the false memory
standardisation
standardisation: keeping the procedure for each participant in an experiment (or interview) exactly the same to ensure that any differences between participants or conditions are due to
the variables under investigation rather than differences in the way they were treated.
population
the group, sharing one or more characteristics,
from which a sample is drawn.
opportunity sample
(sampling technique)
participants are chosen because they
are available
e.g. university students are selected because they
are present at the university where the research is taking place.
volunteer (self-selected) sample
(sampling technique)
participants are invited to
participate
e.g. through advertisements via email or notices. Those who reply become the sample.
random sample
all members of the population (i.e. possible participants) are allocated numbers and a fixed amount of
these are selected in a unbiased way, e.g. by taking numbers
from a hat.
naturalistic observation
a study conducted by watching
the participants’ behaviour in their normal environment
without interference from the researchers in either the social or
physical environment.
controlled observation:
a study conducted by watching
the participants’ behaviour in a situation in which the social or physical environment has been manipulated by the researchers. It can be conducted in either the participants’ normal environment or in an artificial situation.
structured and unstructured observation
unstructured observation: a study in which the observer
records the whole range of possible behaviours, which is
usually confined to a pilot stage at the beginning of a study to refine the behavioural categories to be observed.
structured observation: a study in which the observer
records only a limited range of behaviours.
participant and non participant observer
participant observer: a researcher who watches from the
perspective of being part of the social setting.
non-participant observer: a researcher who does not
become involved in the situation being studied, e.g. by
watching through one-way glass or by keeping apart from the social group of the participants.
behavioural categories
the activities recorded in an
observation. They should be operationalised (clearly defined) and should break a continuous stream of activity into discrete recordable events. They must be observable actions rather than inferred states.
overt and convert observer
overt observer: the role of the observer is obvious to the
participants.
covert observer: the role of the observer is not obvious,
e.g. because they are hidden or disguised.
correlation
a research method which looks for a causal
relationship between two measured variables. A change in one variable is related to a change in the other (although these changes cannot be assumed to be causal).
quantitive and qualitative data
quantitative data: numerical results about the quantity of
a psychological measure such as pulse rate or a score on an intelligence test.
qualitative data: descriptive, in-depth results indicating the
quality of a psychological characteristic, such as responses to open questions in self-reports or case studies and detailed observations.
measure of central tendency, of spread
measure of central tendency: a mathematical way to find
the typical or average score from a data set
(mode, median, mean)
measure of spread: is an indicator of how varied the
results are within a data set, a mathematical way to describe the variation or dispersion within a data set.
(range, standard deviation)
bar chart, histograms, scatter graph, normal distribution curve
bar chart: there are gaps between each bar that
is plotted on the graph because the columns are not related in a linear way.
histogram: a graph used to illustrate continuous data, e.g. to show the distribution of a set of scores. It has a bar for each score value, or group of scores, along the x-axis. The y-axis has frequency of each category.
normal distribution curve:
* has the mode, median and mean together in the centre
* has 50% of the scores to the left and 50% to the right of
the mean
* is symmetrical.
Ethical guidelines relating to the use of animals
housing, replacement, species and strain, no of animals, procedures:pain and distress, Reward, deprivation and aversive stimuli, anaesthesia, analgesia and ethanasia
