The document provides an overview of research methods in psychology. It discusses the key stages of conducting research including formulating an operational hypothesis, designing the experiment, collecting and analyzing data, and reporting findings. It also defines important terms like participants, independent and dependent variables, and different sampling methods like random sampling and stratified sampling. Experimental and correlational methods are the main focus. Ethics approval is required before conducting research with people.

1. UNIT 3 RESEARCHUNIT 3 RESEARCH
METHODSMETHODS

2. Research MethodsResearch Methods
 Are the tools or techniques psychologistsAre the tools or techniques psychologists
use to obtain accurate and reliableuse to obtain accurate and reliable
information about thoughts, feelings andinformation about thoughts, feelings and
behaviour.behaviour.
 Our focus is on experimental methodsOur focus is on experimental methods
and correlational studiesand correlational studies

3. PsychologyPsychology
 Psychology is a study that uses scientificPsychology is a study that uses scientific
method to observe, describe predict andmethod to observe, describe predict and
explain behaviour.explain behaviour.

4. OPERATIONAL HYPOTHESISOPERATIONAL HYPOTHESIS
 Researcher poses a question based on previous findings and theoriesResearcher poses a question based on previous findings and theories
↓↓
RESEARCH IS DESIGNEDRESEARCH IS DESIGNED
 The experiment is designed: participants are selected, the dependent andThe experiment is designed: participants are selected, the dependent and
independent variables are defined and the experimental and control groups areindependent variables are defined and the experimental and control groups are
establishedestablished
↓↓
ETHICAL CONSIDERATIONSETHICAL CONSIDERATIONS
 An ethics committee approves the experimentAn ethics committee approves the experiment
↓↓
COLLECTION OF DATACOLLECTION OF DATA
 The experiment is conducted and data is collected, organised and summarised in aThe experiment is conducted and data is collected, organised and summarised in a
meaningful waymeaningful way
↓↓
INTERPRETATION OF DATA BY STATISTICAL ANALYSISINTERPRETATION OF DATA BY STATISTICAL ANALYSIS
 The data is analysed to make inferences about what it means. This can be done inThe data is analysed to make inferences about what it means. This can be done in
two stagestwo stages
 Descriptive StatisticsDescriptive Statistics – describe and summarise the data, does not allow for– describe and summarise the data, does not allow for
conclusions to be drawnconclusions to be drawn
 Inferential StatisticsInferential Statistics – mathematical procedures used to determine if the difference– mathematical procedures used to determine if the difference
between the experimental and control groups represent a ‘true’ different – if the IVsbetween the experimental and control groups represent a ‘true’ different – if the IVs
is having an effect on the DV. This also allows researchers to use the results fromis having an effect on the DV. This also allows researchers to use the results from
their sample to betheir sample to be generalisedgeneralised to the population as a whole.to the population as a whole.
↓↓
REPORTING THE FINDINGS AND CONCLUSIONSREPORTING THE FINDINGS AND CONCLUSIONS
 Researchers punish their work so that it can be replicated by others. In VCEResearchers punish their work so that it can be replicated by others. In VCE
psychology, you will write your research as anpsychology, you will write your research as an ERAERA..

5. Key TermsKey Terms
 ParticipantsParticipants – people taking part in– people taking part in
experiment or correlational studyexperiment or correlational study
 SamplingSampling – process of selecting participants– process of selecting participants
for researchfor research
 SampleSample – group which is a subset or portion of– group which is a subset or portion of
larger group chosen to be studied for researchlarger group chosen to be studied for research
purposes (It should mirror or be representativepurposes (It should mirror or be representative
of the entire population of interest)of the entire population of interest)
 PopulationPopulation – the larger group from which a– the larger group from which a
sample is drawnsample is drawn

6. Two methods psychologistsTwo methods psychologists
use to select a sampleuse to select a sample::
Random SamplingRandom Sampling – sampling procedure which ensures– sampling procedure which ensures
that every member of the population of researchthat every member of the population of research
interest has an equal chance of being selected as ainterest has an equal chance of being selected as a
participant for the study.participant for the study.
 HowHow? – putting everyone’s name on a slip of paper,? – putting everyone’s name on a slip of paper,
putting them into a container, mixing them thoroughlyputting them into a container, mixing them thoroughly
and choosing slips blindly, assigning each member ofand choosing slips blindly, assigning each member of
the population a number and randomly choosingthe population a number and randomly choosing
numbersnumbers
 WhyWhy? – Increases the likelihood that the sample is? – Increases the likelihood that the sample is
representative of the target population, thereforerepresentative of the target population, therefore
increases ability to make valid inferences about theincreases ability to make valid inferences about the
population.population.

7. Two methodsTwo methods
psychologists use topsychologists use to
select a sample:select a sample:
 Stratified Sampling –Stratified Sampling – sampling procedure involvingsampling procedure involving
dividing the population to be sampled into distinctdividing the population to be sampled into distinct
groups, or strata, then selecting a separate samplegroups, or strata, then selecting a separate sample
from each stratum, usually in the same proportions asfrom each stratum, usually in the same proportions as
they occur in the target populationthey occur in the target population
 Income, age, sex, religion, ethnic background,Income, age, sex, religion, ethnic background,
residential area, IQ score are examples ofresidential area, IQ score are examples of
characteristics which may be used as the bases ofcharacteristics which may be used as the bases of
dividing a population into strata.dividing a population into strata.
 How? –How? – Obtain accurate lists of all people within eachObtain accurate lists of all people within each
stratum, random samples of each proportionate sizestratum, random samples of each proportionate size
are drawn from within each stratum.are drawn from within each stratum.
 Why?Why? – Eliminates bias and ensures that groups in a– Eliminates bias and ensures that groups in a
population of interest are represented in the sample inpopulation of interest are represented in the sample in
thethe same proportion that they are represented in the populationsame proportion that they are represented in the population

8. Forms of Non RandomForms of Non Random
SamplingSampling
 Although random sampling offers the bestAlthough random sampling offers the best
assurance that samples drawn from aassurance that samples drawn from a
population will be representative, in reality, apopulation will be representative, in reality, a
considerable amount of research is undertakenconsiderable amount of research is undertaken
using non-random sampling procedures.using non-random sampling procedures.
 Non-random sampling techniques may be usedNon-random sampling techniques may be used
when a research requires a sample group thatwhen a research requires a sample group that
possess a particular characteristic who wouldpossess a particular characteristic who would
be difficult to locate with random sampling.be difficult to locate with random sampling.
Another reason could be because of theAnother reason could be because of the
complexity or opportunity sampling andcomplexity or opportunity sampling and
snowball sampling.snowball sampling.

9. Forms of Non RandomForms of Non Random
SamplingSampling
Convenience SamplingConvenience Sampling
 In convenience sampling, participants are obtained atIn convenience sampling, participants are obtained at
the researcher’s convenience (meaning the researcherthe researcher’s convenience (meaning the researcher
uses anyone they can get hold of who is willing touses anyone they can get hold of who is willing to
participate in the study).participate in the study).
 For example, a good deal of research conducted inFor example, a good deal of research conducted in
universities has used convenience samples ofuniversities has used convenience samples of
university students as their participants.university students as their participants.
 Although this type of sample is relatively easy toAlthough this type of sample is relatively easy to
obtain, there are obvious disadvantages to its design.obtain, there are obvious disadvantages to its design.
These include how realistic that particular sampleThese include how realistic that particular sample
group is of the broader population and thereforegroup is of the broader population and therefore
whether the results can be accurately generalisedwhether the results can be accurately generalised
beyond the sample itself.beyond the sample itself.

10. Forms of Non RandomForms of Non Random
SamplingSampling
 Snowball SamplingSnowball Sampling
 Snowball sampling is often employed in research with special groups ofSnowball sampling is often employed in research with special groups of
participants who have specific characteristics of interest to the researcher.participants who have specific characteristics of interest to the researcher.
 For example, a sports psychologist may be interested in studying theFor example, a sports psychologist may be interested in studying the
athletic attitudes and performance of children who have a parent who is aathletic attitudes and performance of children who have a parent who is a
champion athlete. It would be very difficult to obtain a sizeable sample ofchampion athlete. It would be very difficult to obtain a sizeable sample of
children in this category by a process of random sampling of a population.children in this category by a process of random sampling of a population.
Using snowball sampling, the researcher would first identify one or twoUsing snowball sampling, the researcher would first identify one or two
children (or perhaps their parents) who are members of athletic families tochildren (or perhaps their parents) who are members of athletic families to
participate. These participants would then be asked to bring along peopleparticipate. These participants would then be asked to bring along people
they know, who are similar to themselves, into the study. These newthey know, who are similar to themselves, into the study. These new
people are then asked to contact others they know and so on.people are then asked to contact others they know and so on.
 ‘‘Snowballing’ can often be a very efficient way to develop a specialSnowballing’ can often be a very efficient way to develop a special
sample of people with similar characteristics.sample of people with similar characteristics.
 However, just as in Convenience sampling, it is always difficult to estimateHowever, just as in Convenience sampling, it is always difficult to estimate
how accurately these findings would apply to the broader population (buthow accurately these findings would apply to the broader population (but
often, the researcher’s interest is mainly in the results obtained for theoften, the researcher’s interest is mainly in the results obtained for the
sample itself.sample itself.

11. Subject andSubject and
ExperimenterExperimenter
ExpectationsExpectations Placebo effect: a response is influenced by a person’sPlacebo effect: a response is influenced by a person’s
expectations of what to do or how to think or feel, rather than theexpectations of what to do or how to think or feel, rather than the
specific procedure which is used to produce that response.specific procedure which is used to produce that response.
 Single-blind study: subjects are not aware of which condition ofSingle-blind study: subjects are not aware of which condition of
the experiment they have been assigned to.the experiment they have been assigned to.
 Double-blind study: neither the subjects nor experimenter areDouble-blind study: neither the subjects nor experimenter are
aware of the conditions to which the subjects have been assigned.aware of the conditions to which the subjects have been assigned.
 Experimenter effect: experimenter’s personal characteristics,Experimenter effect: experimenter’s personal characteristics,
actions or treatment of the data affect the DV and therefore theactions or treatment of the data affect the DV and therefore the
results of the experiment.results of the experiment.
 Self-fulfilling prophecy: tendency of subjects to behave inSelf-fulfilling prophecy: tendency of subjects to behave in
accordance with how they believe an experimenter expects themaccordance with how they believe an experimenter expects them
to behave/to behave/
 Hawthorne effect: subjects are aware that they are members ofHawthorne effect: subjects are aware that they are members of
an experimental group and their performance may improve simplyan experimental group and their performance may improve simply
because of that fact, rather than because of the IV to which theybecause of that fact, rather than because of the IV to which they
are exposed.are exposed.
 Experimenter bias: unintentional biases in the collection andExperimenter bias: unintentional biases in the collection and
treatment of data by the experimenter.treatment of data by the experimenter.

12. Formulating anFormulating an
OperationalOperational
HypothesisHypothesis
 An operational hypothesis: is a tentative and testableAn operational hypothesis: is a tentative and testable
prediction or explanation of the relationship between two orprediction or explanation of the relationship between two or
more events or characteristics.more events or characteristics.
 An operational hypothesis states how the variables (IV andAn operational hypothesis states how the variables (IV and
DV) will be observed, manipulated and measured and theDV) will be observed, manipulated and measured and the
population from which the sample will be drawn.population from which the sample will be drawn.
 An operational hypothesis must:An operational hypothesis must:
 Begin with ‘That…’Begin with ‘That…’
 Mention the sample being studiedMention the sample being studied
 Mention the IV and DV involvedMention the IV and DV involved
 Mention how the variables will be measuredMention how the variables will be measured

13. Example OneExample One
 General hypothesis:General hypothesis:
 That drinking coffee negatively affects sleepThat drinking coffee negatively affects sleep
 Operationally defining each variable:Operationally defining each variable:

 IV: drinking coffee =IV: drinking coffee = having two cups of coffee one hour prior tohaving two cups of coffee one hour prior to
going to bedgoing to bed
 DV: sleep =DV: sleep = length of undisturbed sleep (hours)length of undisturbed sleep (hours)
 Formulated Operational Hypothesis:Formulated Operational Hypothesis:
 That drinking two cups of coffee prior to going to bed will reduceThat drinking two cups of coffee prior to going to bed will reduce
the length (hours) of undisturbed sleepthe length (hours) of undisturbed sleep
 Now just need to add sample:Now just need to add sample:
 That drinking two cups of coffee prior to going to bed will reduceThat drinking two cups of coffee prior to going to bed will reduce
the length (hours) of undisturbed sleep in 15 females aged 20 –the length (hours) of undisturbed sleep in 15 females aged 20 –
22.22.

14. Example TwoExample Two
 General hypothesis:General hypothesis:

 That the number of classes you attend willThat the number of classes you attend will
affect your gradeaffect your grade
 Operationally defining each variable:Operationally defining each variable:
 IV: number of classes attended =IV: number of classes attended = attendance at 90% ofattendance at 90% of
classesclasses
 DV: grades = performance on end of course examDV: grades = performance on end of course exam
 Formulated Operational Hypothesis:Formulated Operational Hypothesis:
 That students attending more that 90% of classes willThat students attending more that 90% of classes will
attain a higher score on the end of year exam thanattain a higher score on the end of year exam than
students attending less than 90% of classesstudents attending less than 90% of classes
 Now just need to add sample:Now just need to add sample:
 That 20 male students aged 14 – 15 attending more thanThat 20 male students aged 14 – 15 attending more than
90% of classes will attain a higher score on the end of year90% of classes will attain a higher score on the end of year
exam than students attending less than 90% of classes.exam than students attending less than 90% of classes.

15. Example ThreeExample Three General hypothesis:General hypothesis:

 That alcohol intake will impair drivingThat alcohol intake will impair driving
performanceperformance
 Operationally defining each variable:Operationally defining each variable:
 IV: alcohol intake =IV: alcohol intake = 0.05 blood alcohol level0.05 blood alcohol level
 DV: driving performance = performance number of conesDV: driving performance = performance number of cones
hit on obstacle course.hit on obstacle course.
 Formulated Operational Hypothesis:Formulated Operational Hypothesis:
 That drivers with a blood alcohol intake of 0.05 will hit moreThat drivers with a blood alcohol intake of 0.05 will hit more
cones in the driving obstacle course than drivers with acones in the driving obstacle course than drivers with a
blood alcohol intake of 0.blood alcohol intake of 0.
 Now just need to add sample:Now just need to add sample:
 That 20 male and 20 female drivers aged 30 - 32 with aThat 20 male and 20 female drivers aged 30 - 32 with a
blood alcohol intake of 0.05 will hit more cones in theblood alcohol intake of 0.05 will hit more cones in the
driving obstacle course than drivers with a blood alcoholdriving obstacle course than drivers with a blood alcohol
intake of 0.intake of 0.

16. Example FourExample Four
 General hypothesis:General hypothesis:

 That warm weather leads to a better mood than cold weatherThat warm weather leads to a better mood than cold weather
 Operationally defining each variable:Operationally defining each variable:
 IV: cold = air temperature below 15 CIV: cold = air temperature below 15 C
 hot = Air temperature above 25 Chot = Air temperature above 25 C
 DV: mood = defined by a students response to mood rating scale where theyDV: mood = defined by a students response to mood rating scale where they
were required to identify on a scale from 1 – 10 how happy they felt. A score ofwere required to identify on a scale from 1 – 10 how happy they felt. A score of
1 represented very happy while a score of 10 was considered unhappy.1 represented very happy while a score of 10 was considered unhappy.
 Formulated Operational Hypothesis:Formulated Operational Hypothesis:
 That subjects will rate themselves as being happier on a mood rating scale ifThat subjects will rate themselves as being happier on a mood rating scale if
during its administration the air temperature was above 25 C rather than belowduring its administration the air temperature was above 25 C rather than below
15 C.15 C.
 Now just need to add sample:Now just need to add sample:
 That 15 female subjects ages 14 – 17 will rate themselves as being happier onThat 15 female subjects ages 14 – 17 will rate themselves as being happier on
a mood rating scale if during its administration the air temperature was abovea mood rating scale if during its administration the air temperature was above
25 C rather than below 15 C.25 C rather than below 15 C.

17. OperationalOperational
HypothesisHypothesis
 REMEMBER: An operational hypothesisREMEMBER: An operational hypothesis
must:must:
 Begin with ‘that’Begin with ‘that’
 Mention the sample being studiedMention the sample being studied
 The IV and DV involvedThe IV and DV involved
 How the variables will be measuresHow the variables will be measures

18. VariableVariable
A variable is the name of a ‘factor’ beingA variable is the name of a ‘factor’ being
studied that will change (vary) over time.studied that will change (vary) over time.

19. INDEPENDENTINDEPENDENT
VARIABLEVARIABLE
Is the variable that is manipulated by theIs the variable that is manipulated by the
researcher to assess the effect(s) of theresearcher to assess the effect(s) of the
DV; the treatment.DV; the treatment.

20. DEPENDENT VARIABLEDEPENDENT VARIABLE
Is used to assess the effect(s) of the IV;Is used to assess the effect(s) of the IV;
the participants responses – what we arethe participants responses – what we are
watching.watching.

21. ExampleExample
EG: TheEG: The increased number of alcoholicincreased number of alcoholic
drinksdrinks (IV) will impact upon(IV) will impact upon driverdriver
performanceperformance (DV)(DV)

22. EXTRANEOUSEXTRANEOUS
VARIABLEVARIABLE
 Is any variable other than tha IV thatIs any variable other than tha IV that cancan
cause a change in the DV and thereforecause a change in the DV and therefore
affect the results of an experiment in anaffect the results of an experiment in an
unwanted way; it may become aunwanted way; it may become a
confounding variable.confounding variable.
 EG: The weather may have an impact onEG: The weather may have an impact on
the drivers ability – the experimenterthe drivers ability – the experimenter
cannot control the weather.cannot control the weather.

23. CONFOUNDINGCONFOUNDING
VARIABLEVARIABLE
 Is any variable other than the IV that is uncontrolledIs any variable other than the IV that is uncontrolled
and allowed to change together with the IV, having anand allowed to change together with the IV, having an
unwanted effect on the DV. When present theunwanted effect on the DV. When present the
experimenter cannot determine whether changes in theexperimenter cannot determine whether changes in the
DV are due to solely the IV.DV are due to solely the IV.
 EG: The gender of the driver may have an impact onEG: The gender of the driver may have an impact on
the driver’s ability – the experimenter can controlthe driver’s ability – the experimenter can control
gender my separating males and females intogender my separating males and females into
subgroups.subgroups.


24. WAYS TO MINIMISE THE EFFECTSWAYS TO MINIMISE THE EFFECTS
OF EXTRANEOUS VARIABLESOF EXTRANEOUS VARIABLES
(Experimental Designs)(Experimental Designs)
 REPEATED MEASURES DESIGNREPEATED MEASURES DESIGN
 Each participant is involved in both the experimental andEach participant is involved in both the experimental and
control conditions of an experiment so the effects ofcontrol conditions of an experiment so the effects of
individual differences between participants’ characteristicsindividual differences between participants’ characteristics
balance in both designs.balance in both designs.
 EG. Investigating effects of loud music on performance inEG. Investigating effects of loud music on performance in
problem solving task. Using the repeated measures design,problem solving task. Using the repeated measures design,
the same group of participants would be given a problemthe same group of participants would be given a problem
solving task when loud music is playing and then withoutsolving task when loud music is playing and then without
loud music playing. Also, how well participants perform inloud music playing. Also, how well participants perform in
the problem solving task is assessed twice (hence thethe problem solving task is assessed twice (hence the
‘repeated measures). This design would give the‘repeated measures). This design would give the
experimenter control over participant-related extraneousexperimenter control over participant-related extraneous
variables that may have influenced the results, such asvariables that may have influenced the results, such as
differences in participants’ problem solving abilities anddifferences in participants’ problem solving abilities and
motivation, because they are identical in both groups.motivation, because they are identical in both groups.

25. WAYS TO MINIMISE THE EFFECTSWAYS TO MINIMISE THE EFFECTS
OF EXTRANEOUS VARIABLESOF EXTRANEOUS VARIABLES
(Experimental Designs)(Experimental Designs)
 Another extraneous variable that can arise using aAnother extraneous variable that can arise using a
repeated measures design is called the ‘Order Effect’,repeated measures design is called the ‘Order Effect’,
whether a task is performed first or second. (Considerwhether a task is performed first or second. (Consider
benefiting from experience-enhanced performance,benefiting from experience-enhanced performance,
boredom, fatigue-impaired performance etc). One wayboredom, fatigue-impaired performance etc). One way
to deal with this is to increase the time period betweento deal with this is to increase the time period between
the measurement of the dependent variable. Whenthe measurement of the dependent variable. When
this is not possible, ‘Counterbalancing’ occurs.this is not possible, ‘Counterbalancing’ occurs.
 ‘‘Counterbalancing’ involves arranging the order inCounterbalancing’ involves arranging the order in
which the conditions of a repeated measures designwhich the conditions of a repeated measures design
are experienced, so that each condition occurs equallyare experienced, so that each condition occurs equally
often in each position. For example, half theoften in each position. For example, half the
participants do experimental condition first, then controlparticipants do experimental condition first, then control
and the other half do the reverse order.and the other half do the reverse order.

26. WAYS TO MINIMISE THE EFFECTSWAYS TO MINIMISE THE EFFECTS
OF EXTRANEOUS VARIABLESOF EXTRANEOUS VARIABLES
(Experimental Designs)(Experimental Designs)
MATCHED PARTIICPANTS DESIGNMATCHED PARTIICPANTS DESIGN
This design involves selection of pairs of participants whoThis design involves selection of pairs of participants who
are very similar in characteristic(s) that can influenceare very similar in characteristic(s) that can influence
the dependent variable (eg. Sex, age, intelligence),the dependent variable (eg. Sex, age, intelligence),
then allocating each member of the pair to differentthen allocating each member of the pair to different
groups. Randomly allocating one member of eachgroups. Randomly allocating one member of each
matched pair to different groups helps ensure thatmatched pair to different groups helps ensure that
each group is fairly equivalent in terms of the spreadeach group is fairly equivalent in terms of the spread
of participant characteristics that can cause a changeof participant characteristics that can cause a change
in the DV.in the DV.

27. WAYS TO MINIMISE THE EFFECTSWAYS TO MINIMISE THE EFFECTS
OF EXTRANEOUS VARIABLESOF EXTRANEOUS VARIABLES
(Experimental Designs)(Experimental Designs)
INDEPENDENT GROUPS DESIGNINDEPENDENT GROUPS DESIGN
 In this design, each participant isIn this design, each participant is
randomly allocated to one of tworandomly allocated to one of two
entirely separate (‘independent’)entirely separate (‘independent’)
groups. The random allocationgroups. The random allocation
procedure is used after the participantsprocedure is used after the participants
have been selected for the experiment,have been selected for the experiment,
but before the experiment beginsbut before the experiment begins

28. STATISTICALSTATISTICAL
SIGNIFICANCESIGNIFICANCE
 Tests of statistical significance enable researchers toTests of statistical significance enable researchers to
consider the extent to which change operated in theconsider the extent to which change operated in the
experiment.experiment.
 The difference is statistically significant if the likelihoodThe difference is statistically significant if the likelihood
of the difference occurring by chance is extremely low.of the difference occurring by chance is extremely low.
 A true difference can be said to be due to the IV whenA true difference can be said to be due to the IV when
the probability that it might be due to chance is 5 orthe probability that it might be due to chance is 5 or
fewerfewer times in 100 repetitions of the study.times in 100 repetitions of the study.
 EG – the result is significant at the 0.05 level.EG – the result is significant at the 0.05 level.
 Significance Level is know as the ‘p value’ (probabilitySignificance Level is know as the ‘p value’ (probability
value)value)
 P≤ 0.05P≤ 0.05

29. CORRELATIONALCORRELATIONAL
METHODMETHOD
 Correlation method enables us to identify and describeCorrelation method enables us to identify and describe
the relationship between two variables.the relationship between two variables.
 It does not indicate cause-effect relationship.It does not indicate cause-effect relationship.
 Correlation is often described by a number known asCorrelation is often described by a number known as
the correlation coefficient. This is expressed as athe correlation coefficient. This is expressed as a
decimal number which can range from +1.00 to -1.00decimal number which can range from +1.00 to -1.00
 + positive correlation+ positive correlation
 - negative correlation- negative correlation
 +1.00 high positive correlation (very strong+1.00 high positive correlation (very strong
relationship)relationship)
 - 1.00 high negative correlation (very strong- 1.00 high negative correlation (very strong
relationshiprelationship
 .00 no relationship.00 no relationship

30. Correlational MethodCorrelational Method
 What conclusions could be drawn fromWhat conclusions could be drawn from
the following correlation coefficients?the following correlation coefficients?
 Length of time spent studying for examsLength of time spent studying for exams
and exam grades (+0.72)and exam grades (+0.72)
 Distance from goal and goal shootingDistance from goal and goal shooting
accuracy (-0.92)accuracy (-0.92)
 Colour of socks worn in an exam andColour of socks worn in an exam and
grade achieved (+0.06)grade achieved (+0.06)