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Into To Psych Research 1st Year Notes

Psychology Notes > Intro to Psychological Research (1st year) Notes

This is an extract of our Into To Psych Research 1st Year document, which we sell as part of our Intro to Psychological Research (1st year) Notes collection written by the top tier of Durham University students.

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3 Experimental methods
 1 or 2 tailed
 Confounding variables 4
Validity and Reliability 5
Within and Between subjects 6-8 Experiment types
 True
 Field
 Quasi

6 Non-equivalent

Time series

Time seires w/ non-equivalent: multiple time series
 6 Single case

o 7 Multiple baseline

Alternating treatments

Simultaneous treatments 9
Non-Experimental methods
 Correlations 10-12 Quantitative data
 Questionnaires
 10 Surveys
 Observations 13
Qualitative data
 Interviews
 Pictures
 Ethnographies
 Data displays 14-15 Descriptive Stats
 Function of stats
 Measures of central tendency and dispersion
 SD
 Types of data (Nominal, Ordinal, Interval, Ratio and Cont, Discrete, Dichotomous)
16-18 Tables and Diagrams
 Bar charts, histograms, freq polygons and cum freq polygons
 Distributions
 16 stem-and-leaf diagrams
 17 box-and-whisker plots 19
Probability 20
Sign test 21
Wilcoxon Matched Pairs Signed Ranks Test 22
Mann-Whitney U test 23
Parametric tests

1 24

1 sample T test 2-matched sample T test
Independent samples (equal groups)
Independent sample (unequal groups ie pooled variances)
Pearson's R
Spearman's Rho
Chi-Square- 1 sample
Chi-Square - 2 categorical variables sample (Contingency X 2)

25 26 27 28 29 30 31

Design = IV (state general and then what levels), DV; within-Ps, between-Ps, correlation, freq/chi-sq
Question type = relationship, differences
Data type = qualitative, quantitative
Data type = NOIR, parametric, non-parametric, variances


Mode 3: Stat 1: 1-VAR (if 1 set of data) 2: A+BX (if 2 sets of data  only use if equal number data)
Enter values than press AC
Shift 1: STAT
4: VAR
SX/SY for SD of x/y values
Mode 1: Comp for normal mode

Parameter: value of population - no uncertainty
Statistic: value of a sample - has uncertainty  stats used to estimate parameter


2 Involves some manipulation
 True experiments
 Field experiments
 Quasi experiments
 Single case experiments
Hypothesis = prediction of what you expect to find in your study 1 tailed = direction of the rel is fixed in advance ie there is a direction +ve or -ve 2 tailed = direction not specified ie there is a diff in a yet unknown direction

Allows investigating the casual rel
Control IV
Better control of extraneous variables
Random sampling and random allocation
Increases internal validity
At risk of decreasing external and ecological validity

Order effects
Performance in 2nd half may be better due to learning or worse due to tiredness
Participant Bias
Demand charac - tendency of Ps to respond in certain ways because they know they are being observed & believe that they know what the researcher wants
Volunteer Bias
When you seek volunteers, you will get a sample that is not rep of the larger pop
Experimenter Bias All stages from designing to analysing can be affected. Bias towards a result expected by experimenter

Minimising confounding variables
 Effective randomization
 Single blind = info that could introduce bias or skew the result is withheld from Ps, but the experimenter knows all facts  experimenter bias
 Double Blind = attempt to eliminate subjective bias on the part of both experimental subjects and the experimenters. The key that identifies the subjects and which group they belonged to is kept by a third party and not given to the researchers until the study is over.
 Standardised procedures
 Stats

= measures what it claims to measure. Whether a study scientifically answers the qs it intends to answer

3 Criterion Validity
 Compares the test with other measures or outcomes already held to be valid
 IQ test validated against academic performance
Content Validity
 Does an IQ questionnaire have items covering all areas of intelligence discussed in literature?
Construct Validity
 Involves the empirical and theoretical support for the interpretation of the construct
 To what extent is an IQ questionnaire actually measuring "intelligence"?
External Validity
 Can results of a study be held to be true for other cases e.g. to different people, places or times?
 Can findings can be validly generalized?
Ecological Validity
 To what extent can research results be applied to real life situations outside of research settings?
 To be ecologically valid, the methods, materials and setting of a study must approximate the reallife situation that is being studied

= a test is reliable when it gives consistent results of the same measure
Internal reliability = consistency of a measure within a test (ie all items measuring the same things)
 Split half method (same participant do both halves of the test. If both provide similar results =
test has internal reliability)
External reliability = ability to replicate the results and get the same/similar results
 Test-retest method (testing the same participant twice over a period of time on the same test.
Similar scores = test has external reliability)
 Inter-rater reliability (comparing the ratings of 2 or more observers)

Ceiling and floor effects
Ceiling effects
 Test is too easy and many Ps score near the top
 Test can't distinguish between individuals
Floor effects
 Test is too difficult

= each P takes part in each level of the IV

4 

Controls for indiv diff between Ps
Reduces number of Ps needed - higher consistency
Allows to follow Ps over time
Time constraints
Exp mortality
Order effects - practice/fatigure effect
Demand charac
o Randomize order of conditions


 Balance effects or order of conditions
 Split each group in half - group 1 does A then B, group 2 does B then A
 Order effects balanced out as they occur equally in both groups
Complete Counterbalancing:
 Requires that all possible condition orders are used
 Eg if 6 levels = 6x5x4x3x2x1=720 orders
Latin Square:
 Ensures that each level of the IV appears equally in each position
Matching - treated as within-subjects
Matched groups = ensures that each group is matched on a particular charac or variable eg age
Matched pairs = each member of a group has a corresponding 'pair', eg that there is an individual in another group who is the same in a variable

= 2+ separate groups receive different levels of the IV
 Generally easier on the P
 Hypotheses not guessed (P bias ie demand charac)
 Avoids order effects
 Indiv diff between Ps in the groups may affect results
 More Ps needed
 Solution: Random allocation or Matching
True experiments

5 

Sample groups must be assigned randomly
Ps must be randomly assigned to either control or experimental group
There must be a variable control group
Only one variable can be manipulated and tested

Results can be stat analysed
Much easier to replicate and validate the results
Usually gives a Y/N answer
Almost too perfect - conditions under complete control ie not rep
Difficult to exclude other factors that may affect the manipulated variable

Field experiments
 Experiment in a natural setting/performed outside the lab
 Situational variables are allowed to vary as they normally would eg noise, presence of other people
 A manipulation is introduced

Less control - more ecologically valid
Ps may be unaware of the aims of the experimenter
Looking at a behaviour in its context is more meaningful
Less control - confounds
Normally more time consuming/expensive
Might not end up with optimal settings due to other restrictions
More difficult to replicate
Any generalization is tenuous

Quasi-experimental design
 = still includes manipulation but lacks random assignment (i.e. uses pre-existing groups)
 Random allocation cannot be used in all research eg looking at the effectiveness of treatments or comparing teaching methods across schools
 Allows for research where it is not practical/ethical/possible to randomly assign Ps to groups
 Cannot control for some diff between groups that could cause diff in DV

3 types:
 Non-equivalent control group designs
 Time series designs
 Time series with non-equivalent control group designs

Non-equivalent control group designs
 You can't assume that groups are equivalent before testing occurs
  must test both groups before and after the manipulation (Pre- and Post-testing)

6  It's often likely that the groups are not equivalent, therefore NECG
 Selection bias - initial advantage of one group
 Selection/maturation interaction - groups mature at diff rates which creates illusion of a program effect when there is not one
Time series designs
 One sample
 You make a few observations (measurements of the DV) to establish a baseline, do the intervention, and then make a few more measurements

Any change could be due to something other than the treatment
Testing effects  Repeated testing: are we measuring X or the skill with the instrument?
Instrumentation effects  Does the measurement instrument decay and gain/loose sensitivity?
Experimental mortality  Ps drop out

Time series with non-equivalent control group designs (Multiple Time Series Design)
 Combination of the previous two designs
 Time Series Design with the addition of a comparison group
 Measures of DV taken on many occasions for an intervention group and non- equivalent control
 Provides some info about what might have happened to the experimental group had the experimental treatment not been applied
 Expensive and need more Ps
 In longitudinal studies - Ps in the 2 groups may discuss intervention

Single Case Experimental Design
 A special case of the time series design
 Measurements taken repeatedly (eg 10 times) before and after an intervention on one or a few Ps
 P serves as his/her own control, rather than using another individual/group 4 types:
 A-B designs
 Multiple baseline designs
 Alternating treatments design
 Simultaneous treatments design
A-B designs
 A is a baseline phase during which the natural occurrence of the target behave is monitored
 In B the treatment is introduced
 Is it a behav change that follows the onset of treatment due to treatment or some other reason?
- Overcome by using ABA design BUT ethical issues and effects of medication persist
Multiple baseline designs
 Multiple aspects of behaviour are identified and measured over time to provide baselines against which changes can be measured


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