Research methods - AS/A2 - Revision Cards in A Level and IB Psychology

Created by: Simba2604
Created on: 15-01-18 11:31

Key terms in research methods

Aim - general statement of what researcher is investigating, aka purpose of study

Hypothesis - statements that state the relationship between variables investigated

(one-tailed) Directional hypothesis - states outcome of whats expected from experiment

(two-tailed) Non-directional - states that there will be a difference but does not say in which direction

Null hypothesis - states there is no difference between the variables

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Aim vs Hypothesis

Aim:

identifies the purpose of the investigation
typically involves the word "investigate"
eg. Milgram (1963) investigated how far people would go in obeying an instruction to harm another person

Hypothesis:

precise, testable statement of what researchers predict will be outcome of study
usually involves proposing a possible relationship between two variables the IV + DV

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Types of hypotheses

Research hypothesis:

predicts a statistically significant effect of an IV on a DV (i.e. an experiment), or a significant relationship between variables

= directional hypothesis is used if there are theories or existing evidence that argues a particular "direction" of the predicted results e. bodybuilders with a healthy balanced diet are likely to gain more muscle mass than bodybuilders who do not follow a healthy balanced diet

= non directional does not predict a direction, so here would simply predict “a significant difference” between healthy eaters and non healthy eaters (in an experiment), or “a significant relationship” between questionnaire scores and number of training partners (in a correlation study).

Null hypothesis:

predicts that a statistically significant effect or relationship will not be found eg. there will be no significant difference between bodybuilders with healthy diets than those without

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Types of variables

Variables - anything that can change in the investigation

Independant V - variable manipulated by researcher

Dependant V - variable measured by researcher

Confounding V - if variable has actually been found to have influenced the results of research the would be considered to have counfounded the results

Extraneous V - any other variables expected from IV that could influence measurement of DV

3 key things to consider when controlling EV:

PP Vs - minimising differences between pps (eg. all similar age/gender/IQ)
Researcher Vs - factors that could affect pp responses (eg. researcher behaviour/appearance)
Situational Vs - control of setting where experiment takes place (eg. light/temp/sound consistent)

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Variables2

Operationalisation = clearly stating how a behaviour or factor is measured or manipulated

eg. "alcohol affects attraction" does not operationalise Vs

corrected ---> "200ml of 40% vodka" compared to IV of "200ml of water"

---> "attractiveness score on a scale of 1-7"

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Demand Chara

Demand Characteristics:

in an experiment pps are often unsure about what to do, so they actively look for clues to tell them what is expected of them, these clues are DCs
guessing aims of experiment will create pressure to respond in a certain way

Solutions:

Single blind ---> pps does not know true aims of experiment
Double bind ---> both pps and experimenter don't know aims of study, useful becauce experimenter can't give clues about what they expect

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Types of sample - Random

Random sample:

all members of target pop have equal % of being selected
members of pop are assigned a number and then selected from pool using ran no. generator

+ pps have an equal % of being selected as no one has an influence on who is selected therefore, researcher bias is eliminated

+ equal % of selection also means a representative sample can be created

- may be impractical or impossible if the target group may be too large to assign numbers to

- small mnority groups within target group may distort results, even with random sampling

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Types of sample - Systematic

Systematic sample:

every nth person of a target population is selected eg. every 4th person
a random sample may also be intergrated to reduce bias

+ assuming list order has been randomised, this method offers unbiased chance of gaining representative sample

+ also avoids an element of researcher bias as once the system for selection is established, they have no influence over who is chosen

- members of the pop dont have equal % of being selected is random sample not intergrated

- if list assembled incorrectly, bias may be present eg. every 4th person may be male

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Types of sample - Stratified

Stratified sample:

sampler divides target group into subgroups, each showing a key characteristic which should be present in the final sample
then each of the subgroups is sampled individually
eg. pop of 6th form students divided into races, age, hair colour, etc
sample created therefore, shoud contain members from each key characteristic in a proportion representative of target pop

+ avoids isse of misrepresentation that can be caused by purely random sampling

+ representative sample because is designed to accuratey reflect composition of the pop

- time consuming and expensive to indentify subgroups

- care must be taken to ensure each key characteristic present in the pop is selected across strata, otherwise will design biased sample

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Types of Sample - Opportunity

Opportunity sample:

pps who are both accessible and willing to take part are tageted
eg. pps from a conveniently located cafe near the laboratory could be selected for the sample

+ relatively easy and inexpensive method to carry out

- consequent sample may not be representative as it could be subject to bias eg. pps at the cafe may possess certain similar characteristics that are unrepresentative of the wider target group

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Types of Sample - Volunteer

Volunteer sampling:

sample consists of ppl who have volunteered to be in the study

+ often achieves large sample size through reaching a wide audience eg. online advertisements

- those volunteering may all display similar characteristics such as having a genuine interest in a subject, thus increasing the chances of yielding an unrepresentative sample

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Types of experiment1

Laboratory ex

one that takes place in a controlled environment
researcher manipulates IV and records effect on the DV, whilst maintaining control over EVs

+ high control over EVs, means thatresearcher can be sure that any change in DV is result of the IV alone and not other factors, c+e relationship is established (increasing internal valid)

+ standard procedures allow replication improving reliability

- artificial situations may make pps' behaviour unrepresentative and may result in low external V

- pps may respond to demand characteristics and alter their behaviour

- investigator effects may bias results

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Types of experiment2

Field ex:

IV is manipulated in a natural, more everyday setting

+ higher mundane realism than lab ex because environment is more natural, therefore, may produce behaviour that is more valid and authentic (high external valid)

+ pps are likely to be unaware that they're being studied and so DCs are less problematic

- loss of control over Evs means that c+e relationship between IV and DV cannot be established accurately, problem because may mean other unknown factors may have played a role

- fewer controls also means harder to replicate than a lab ex

- if pps are unaware they are being studied they cannot consent to it and so there are ethical issues to address

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Types of experiment3

Natural ex:

researcher takes advantage of a pre-existing IV instead of manipulating it themselves
pps may be studied in a lab or field as it is the IV thats naturally occuring not the setting

+ provide opportunities to investigate variables that may not otherwise be ndertaken for practical or ethical issues eg. romanian orphans

+ can be used to study real-world issues and so often high externa validity

- naturally occuring events may only happen very rarely, reducing the opportunities for research but also cannot be replicated

- control over EVs is more difficult than in a lab ex

- the researcher cannot manipulate the IV and so we cannot be sure that it is causing changes in the DV than in other ex

- (QUASI) only possible when naturally ocurring differences arise

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Experimental designs - RMD

Repeated measured design:

where same participants are allocated to all groups (i.e. take part in all conditions) of an experiment.

+ results will not subject to participant variables (i.e. individual differences between participants), putting more confidence in dependent variable changes being solely due to manipulated changes in the independent variable

+ As the same pps are used twice, extra participants do not need to be recruited (less time consuming)

- risk of observing order effects (e.g. practice / fatigue effects, or demand characteristics), but risk is reduced by counterbalancing (i.e. controlling the order of variables so that each order combination occurs the same number of times)

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Experimental designs - IGD

Independant groups design:

where diff pps take part in each experimental condition (they will be allocated randomly)

+ no order effects can be observed, as no pps will be used in more than one condition

+ data collection will be less time-consuming if all conditions of the experiment can be conducted simultaneously

- diff pps need to be recruited for each condition, which can be difficult and expensive

- risk of ppt variables (individual diffs between pps) affecting results between conditions, rather than solely manipulation of the IV

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Experimental designs - MPD

Matched pairs design:

where pps take part in only one experimental condition, but they are recruited specifically to be similar in relevant characteristics eg. gender, age...

+ no order effects observed as pps only take part in one condition

+ tailored ppt-matching process reduces risk of ppt variables (individual diffs) from affecting results between conditions

- diff pps need to be recruited for each condition, which is difficult and expensive

- matching is a more complex process, and will always be difficult to match pps identically

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Experimental Design2

Matched pairs design - pps in an IV are matched with pps from another group on key variables (eg. same race)

+ pps only see experimental task once, reducing exposure to DCs

+ controls some individual differences

+ ppls only take part in one IV so order effects are less of a problem

- difficult to accurately match and find pairs into their groups

- more pps are needed than with a repeated measures design

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Controlled observation

Controlled obs:

carried out in a lab
researcher manipulates time, place, pps and circumstances of study and uses stand proc
pps randomly allocated to each independent variable group.

behaviour is coded using previously agreed scale using a behavior schedule
researcher systematically classifies the behavior they observe into distinct categories
might involve numbers or letters to describe a characteristics, or use of a scale to measure behavior intensity
data collected can be easily counted and turned into statistics.

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Evaluation of Controlled obs

+ easily replicated by other researchers by usuing same observation schedule, this means it is easy to test for reliability

+ data obtained from structurd observations is easier and quicker to analyse as it is quantitative, making this a less time consuming method compared to naturalistic observations

+ fairly quick to conduct which means that many observations can take place within a short amount of time, means a large sample obtained resulting in findings being representative and having the ability to be generalized to large pop

- lack validity due to DCs because when pps know they are being watched they may act differently

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Types of Observation - Naturalistic

Naturalistic:

= involves studying spontaneous behaviour of pps in natural surroundings

= researcher simply records what they see in whatever way they can

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Covert vs Overt observations

Overt:

involves researcher declaring to their participants what they are doing and gaining permission of participation at the beginning of a study

Covert:

do not inform the participants of their roles in a study and they are left completely unaware of the researchers aim

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Self report techniques

Self report techniques:

= describe methods of gathering data where participants provide information about themselves without interference from the experimenter

eg.

questionnaires
interviews

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Self RT - interviews

Interviews:

= involve researchers asking questions in a face-to-face situation, they can be diff but there are 2 broad types

Structured:

questionnaire is read to pps and the interviewer writes down their responses
interviews are identical for all pps and tend to involve more simple, quantitative questions
interviewers don't need a lot of training, since they are fairly easy to conduct

Unstructured:

less controlled and involve an informal discussion on a particular topic
however, while the topic is predetermined, the direction of the interview isn't
this allows the interviewer to explore areas of greatest interest ---> need to be trained for this
gains more detailed responses which increases amount of qualitative data obtained

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Evaluation of interviews

+ complicated and sensitive issues are best dealt with in face-to-face interviews, particularly true of unstruc interv, where natural flow of convo is likely to make the respondent feel more relaxed and will thus enhance the quality of the answers

+ any ambiguity or mistunderstanding can be clarified within interview as interviewer can follow up on any answers and explore them more fully

+ structured interviews follow a standardised procedure, which makes it easier to replicate. Another researcher achieving similar, if not identical results, means that it can be viewed as a reliable measurement

- ethical issues can arise when pps don't know the true purpose of the interview

- respondents may be unabe to put into words their true feelings about a particular topic therefore, we cannot say that the responses are entirely accurate

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Self RT - Questionnaires

Questionnaires:

written series of questions for the purpose of gathering information from reponsdents
may be carried out face to face, telephone, on a computer, or post
canconsist of closed qs ---> fixed choice of answers (easy analysis,quantitative data)
can consist of open qs ---> allows more detailed answers (creates more qualitative data)

+ relatively cheap, quick and efficient way of obtaining data as from large sample of ppl, this is because researcher does not need to be present when the questionnaires were completed ---> useful for large pop when interviews would be impractical

+ questions standardised as all respondents are asked same qs in the exact same order, means questionnaire can be replicated easily to check for reliability

- respondents may lie due to social desireability as most ppl will want to present a positive image of themselves eg. students exaggerate revision duration

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Correlations

Correlation:

means association
measure of the extent to which two variables are related
each of the 2 numbers represents a co-variable, once data has been collected for each of the co-variables, it can be plotted on a scattergram and/or statistically analysed to produce a correlation coefficient

Scattergrams and coefficients indicate strength of a relationship between two variables, which highlights the extent to which two variables correspond

= if increase in one variable tends to be associated with an increase in the other then this is known as a positive correlation

= if an increase in one variable tends to be associated with a decrease in the other then this is known as a negative correlation

= When there is no relationship between two variables this is known as a zero correlation

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Evaluation of correlations

+ allows the researcher to investigate naturally occurring variables that maybe unethical or impractical to test experimentally

+ allows the researcher to clearly and easily see if there is a relationship between variables. This can then be displayed in a graphical form.

- is not and cannot be taken to imply causation. Even if there is a very strong association between two variables we cannot assume that one causes the other.

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Diff between Experi and Correlation

= experiment isolates and manipulates the independent variable to observe its effect on the dependent variable, and controls the environment in order that extraneous variables may be eliminated. Experiments establish cause and effect.

= correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables.

This means that the experiment can predict cause and effect (causation) but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.

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Content analysis

Content analysis:

method used to analyse qualitative data (non-numerical data), technique that allows a researcher to analyse qualitative data and transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.
researcher conducting a content analysis will use ‘coding units’ in their work eg. the number of positive or negative words used by a mother to describe her child’s behaviour or the number of swear words in a film

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Content analysis

Content analysis:

method used to analyse qualitative data (non-numerical data), technique that allows a researcher to take qualitative data and transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.
researcher conducting a content analysis will use ‘coding units’ in their work eg. the number of positive or negative words used by a mother to describe her child’s behaviour or the number of swear words in a film

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Evaluation of content analysis

+ reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers

+ easy technique to use and is not too time consuming

+ allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure

- causality cannot be established as it merely describes the data

- as it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising.

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Case studies

Case studies:

very detailed investigations of an individual or small group of people
usually regarding an unusual phenomenon or biographical event of interest to a research field
due to a small sample, the case study can conduct an in-depth analysis of the individual/group

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Evaluation of Case studies

+ create opportunities for a rich yield of data, and the depth of analysis can in turn bring high levels of validity

+ studying abnormal psychology can give insight into how something works when it is functioning correctly, such as brain damage on memory (e.g. the case study of patient KF)

+ detail collected on a single case may lead to interesting findings that conflict with current theories, and stimulate new paths for research

- little control over a number of variables involved in a case study, so it is difficult to confidently establish any causal relationships between variables

- case studies are unusual by nature, so will have poor reliability as replicating them exactly will be unlikely.

- due to the small sample size, it is unlikely that findings from a case study alone can be generalised to a whole population.

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Pilot studies

Pilot studies:

small, trial versions of proposed studies to test their effectiveness and make improvements
helpful in identifying potential issues early, which can then be rectified before committing to the length and expense of a full investigation.

make sure that your answers are specific to the context or study presented.

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Ethics

BPS ethical code = ethical guidelines set up to protect rights of pps

informed consent ---> pps must be told true purpose of research before agreeing to take part

Deception ---> must be kept to a minimum, only used with good reason

Protection from harm ---> pps not exposed to phy/psych risk more than expected in day to day life

Right to withdraw ---> pps can leave study at any stage and told they can withdraw data after

Confidentiality ---> keep names out of publications to avoid embarrassment; anonymity

Privacy ---> pps are not observed unless it is in a public place

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Dealing with ethical issues

Dealing with ethical issues:

Prior general consent ---> pps sign a consent form with arrange of potential studies/agree not to be told

Presumptive consent ---> detailes explained to a similar/representative group, asked if would agree to conditions, if yes then continue

Retrospective consent ---> pps asked to give consent after study, if not remove data

Cost-benefit analysis ---> benefits to society are compared to potential costs to the pps

Ethics committe ---> a group decide if your research should go ahead using cost benefit analysis

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Peer review

Peer review:

all psychological studies must pass this process before publication
practice of using independant experts to assess quality of scientific research
eg. look at quality of methodology (CVs), data analysis and justification

+ keeps scientists honest if they know it will be reviewed, only research that has sound methodology is passed

- critisized for having publication bias becuase journals prefer to publish positive results, whilst avoiding negative results and replications ---> can lead to misunderstanding of scientific facts

- peer review tends to prefer research that goes with existing theory, rather than original or dissenting work

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Psychology and the economy

= implications of psych research for economy are concerned with how knowledge and understanding gained from psych research (theories and studies) contribute towards economic prosperity.

eg. if more effective treatments can be developed for psych health problems then means ppl will be able to return to work and reduces the burden on the employers, NHS and taxpayer
eg. improving EWT process to produce better more secure convinctions, through perhaps cog interview, will create a more efficient and fairer justice system, with less money spent on false imprisonments

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Reliability

Reliability: refers to consistence of a measurement

External validity ---> extent to one measure of object varies from another eg. ruler should give same measurement if used next month, same interview should have same outcome

Inter-rater reliability ---> 2 interviewes should produce same outcome, compare results of 2+ researchers and see how well they correlate

Test-retest ---> test should give same results twice, give test to same person again on another occasian

Internal reliability ---> extent test is consistent with itself eg. all 1s in IQ test measure IQ

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Validity

Validity = refers to whether a measure actually measures what it claims to be measuring

Face valid ---> measure of whether it looks subjectively promising that a tool measures what it's supposed to (eg. on the surface appears to show...)

Internal valid ---> measure of whether results obtained are solely affected by changes in the variable being manipulated (IV on DV) in a cause-and-effect relationship

Concurrent valid = type of internal valid that asks whether measure is in agreement with pre-existing measures that are validated to test for the same concept

External valid ---> measure of whether data can be generalised to other situations outside of the research environment

Temporal valid = high when research findings successfully apply across time
Eco valid =whether data is generalisable to the real world

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Features of science

Features of Science:

Empirical ---> data is info gained through direct observation rather than reasoned argument or belief

Objective ---> data should not be affected by expectations of researcher, data collection should be systematic and free from bias

Controlled ---> if not controlled, cannot assume changes of DV are result of changes in IV as EV's may have affected DV (useful in establishing causal relationship)

Replication ---> scientists record their methods and standardise them carefully so same procedures can be followed in future (replicated)

Paradigm shift = over time paradigms become challenged eg. behav theory more and more until suddenly there is paradigm shift, based on amount of new evidence old paradigm cannot be accepted (paradigm shift)

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Reporting psychology

Title = state what the study is about, including IV+DV

Abstract = concise summary, includes research and infings without having to read whole report, includes brief decription of aims, hypothesis, method and summary result

Introduction = general overview of area studies, existing theories

Aims/hypothesis = state purpose of study

Method = how the research was carried out

Design = research method, research design, EVs n how to control, how data was collected etc

Results = what researcher found, with descriptive statistics + inferential statistics

Discussion = summary of results, relating to aims.hyp, implications of study

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Primary + Secondary data

Primary data = researcher gains data first hand

- significantly more cost/time

+ data may be more valid as shaped to demands of Q

Secondary data = researcher uses data collected by others

- data may be less valid as shaped to answer demands of another Q

+ data is more easily collected from external sources

Meta analysis = data gathered from lots of studies on same topic, and combine them to see overall effect

+ large no. of overall pps, statictically powerful

- studies don't often match in methodology or research aims so combining them may not be appropriate/valid

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Descriptive statistics

Measures of central tendency - mean, median, mode

Mean +makes use of all values in calc - extreme scores can shift mean signif in 1 direction

Median + not affected by extremes - not as sensitive as mean cos not all values used

Mode + not affected by extremes - can be multiple modes

Measures of dispersion - range, standard deviation

Range + easy to calculate - very affected by extremes

Standard deviation + more precise measurement - affected by extreme scores, hard to calculate

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Graphs

Normal + Skewed distribution

How to write reference

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**+hQdvmu1BQhcwEQRhBFEEYQSUBF5O9HIQ4qA9sTMgRaA7DHwHQC**/QDtAH2IlQeXN0L25QhH9uVIR/blJwEx/sTvJ4iMR+r3on6vwPg5jpJoSuz3c/0+gfZBnEL9ATEeEGiP2O8Ve/18v1/s90r9HrHPI/Z5pLhPZCipP4j6fALdJLB10FfLWU8FdrwS3LlJvGAGwSdwzWLcy8d9POOXOEoSApIQQHwA2DCwIUEISCgkiUGUmJSVD4hSWJLCPB/kBI8g+kWBQmIA8UHgQ4IQFsWIxEWAbUF8ROIpifci1oe4AM9TAk+JQiDxV+QpSQggjhJEryB4JSYkMgGe87G8j+M8PN3Esc280MzxzSzTLPQ2cuELQPuAbgbGB4wfuBDiw4gNAh0ANijwPp5vEmgfG29m6GaG8Qi0R6R9EPdBvxfiAWDDILSieBi4MIhRxEeADQ/MoiAEUWK+4**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**THaUky5T6w3z1htU+G2JKL84eXOKdlFj2ZbZ6406w0leoNFkel4LNs1N7vKtLmkPtr/5PJiVWqJ2mieZLJnbj736CqXDndOyq54NKdi1mrHw7hdZbTMfI5c/OrZKbjjlLv3uD02dYljYo4jbevZx4jyianWJ5eXPLO+QpddrVhcMn1VsT0ipL/YoF5Q+sTqsvnPVT9icmoNVo3JrsdJreH0BLzwyQ1lM1aX6TJc2ozSle+4zsb68BdKHsZJJWFVmUh1hvthvGzOOvcTS0ntIlKXcX4Cbpu13vLkuhJ1aqE2ndQanHq87OnV1hmmkpT5eafKOo4UBqYZ8mesNCenW9WZZ/VYtQ47q8OqUvDqh+bmrdtWmOiykfQlgsxV76kX5OqynepsUptF6kykhnCocZnPAxVhVxEWlcmiI+xj5hxfu6N0BDpVjhw5I5obfB6LudW4ZQKRf6a+/VR1p25R3v1zTj8w78yy/Y17HX2axY6nVrpPXxQeXWMltp+zN/bbLsdffyfw/I7aM+f7cmu6yACz6YA/eX5e9p7GY+f7j5/vyK3pm55teyzLeuJi/6Hqbou//7Sr7f2CgKs**nap8+lni6cYCjcfrT11vi//ct+OokAKXqI0ulUGR4rRvLkgdOhS75nmvq1F0bGLrSv3XSi53HG6sqfcx247Ua9ceGL+arf7fE/FpX5bXaw23DdjeYkio1SFmaflWA5WdD25yZ72qq3yIl1k6y/3cnsKo+pFVmL7+b3WSMYr1fVdvLdH2Fba+ejz1n1ky5NZFYufu3C0pv3Epbb8c8zCDc7JS0tclJi2/lzWq5fP1MdzL7Ib3m1NyixSZpdpDAVPrDvx/tlofjNd6GM351FT8MLD1W0hUTpM9s1YU6MgXOPTLVOXlu1zBI9foApqu5bvbkh+puSl3PDJho68prbdZeGHDSXTlpbud7VbKaa0lklfZ/vgRKDBwxW4ulwUveatC8pFVi12VoNX6bAqPX529NzcddsKEl02wr5ceUA9P1+X7VJnO7Uml87k1BCkinDIfC4M9eWJtTvKRqBT5ciRM6L5iM9jbfq0yoznLlqauqrC7CtHqPHz391wsDK3gVXNtzyzxn0pJj2xquTZd8754tKylyumzSvcc9RT3yNmPF+26e3L51r5x3POzFpFpm9wYhvN1S3dL7xT+zhRcqELNhz0zHm+tKUPnalqf2ZNqdvXveWDmqUbyy9E+PWbq5ZsrDzXJWTvqk1KdWiNtsmYNeOlymfWOdbvOVfVJj6xuvylg9X17QK+rvrFN8svd7KPLbEeKIqRNS0Llha/c8jra2UfW1mUnGlTGs2PLi+tiIoLttmWvlHU1ieu3HQ555WKCzFxzmr7hv2XSpq6UjLezXUH8p2RFOOZKavKrFT/gtXkk0uq0jaWLnz2SPllauuxSylZ+eVeMXO9+7AzdqyycwZmm57l0mZZVdlWVWbu2w6quJmZucyR/frFCz1SxqbKZTsvOKm+OSvPqAwONVE9ZtGZ7cXNZm/3ovUn95w+X9kiziDci5+tXLi2bNUue3mMmb269NX8S4W+nqdXFOXsqJ3/fMW7xaHKhp6FSwvfyL1IhrtTiDMq3KE22dUmh46oGD0377PwJYsgc+UB1YJ8/YAvnTfypRp3qHFSjTvV+OBK/EoZOYR/SxvXDLluBKnCnSqcVOGJheu4wYY3GNh9NNccy3Vb2T/Vwd7Il7NHyJdf+AQbMiOFnC9HRl3pDbKGenDOfi1WkoI5dZhbi7seNlx4eG75o4vzXnr7XH2vtHa389l9zvyabvW8ojkrK86HhSdWFG5899zJiq7xc82KuaV7TjWfKO966MnT89a7KlrYuevOZD5nOeNqKzwbaexiXj3Y+PjS0pL6/sdyKsYudrjq6NW7z//tqdMnKmJ7ipuee/s81SMct0aP2GMOX/eKPU3KVJcec041mF/aX1N4tiO/KnquhX5yjf2lQ7WHyfCDTxdMX1ZK+nqe3uAsqml//VjFX2blPbO66nKAnrk6f5zBkmx0PLKipCLApG4hl+0hbbWx5EX5U02Fbj+T8WL5s/vOWmo6NXNPnrLFTlhaxswrnL7M5mzsnb/GsniV5aQjklcZqY92vHq8bpIpr8LDZ6xzEpsqypq4M+e60tY7krHSMUsc6iy7pZl59VD9Q7Mt+gVOu4959kBD2ktV9vreacTR5MwSBeYan34670L0cqT/iK2tsKb79EX6yRzHul2kuaLHXN5e28otWEceq4luK/CMf7JAsbBk3KLTh81t7+f7kx47vXyLuzzU/UhOsQIjVVmk2kTqiMrPyJc8grTVB5IW5WuynGoTmeAG1iE1mFOLubS4Q43bNbhLZ3TqcLMWt6jxhDzsQwx6RQZXuKEz1IRDg1t1eJkON2txmwa/upUaT5DwtEuNudVYuRorV2PuYbhSc9XoDg3u0GLDcGN32ofo0K4i7GrCPmQrm8pkURIOJV6pJFxKk/16PuZRf8iXWsI+7t/xpSQBEgEJgMTE5BMiAkHmK4E4ct+zlvPpMkoa7Af7Rer+Z/arsRId5tRibh1ePtnonp7uTHmq4KFH3s91hw+Xtrzw1sXiy23KOQfmr7FcapFmLj/z/P6q45V9YzNcSem23XnU0cqOMQvy5r9kd0V656zPLanpee/4pUfn7LVfjr5+tHl6TqGlqefJtU4F4XA0xVftrvj7vBMnqtp2ltRv2OeupZgZK/OV+Kkp6cenZtonGM8q0+0LNribYuLqF8wLso6WN/XNWmPbdOTSoUr/A4uPT1tVYvV0z3qupKi27c3i5j/POLLilXPeqDhzVeFYoznJ6JqyvOQsxaS/6l66u9xc3zk+M/+RNQUVAXbxRvL5/eWumg7V7KPHrdFjlpaHnsl/MttaWd//zMqiIld7nq1pguHIqUrvljzPJPxUlUfAn784eVHJY9jRg9aLlQF24pLiZNyhJqynazr3FgYfmFM4**D6XDuTtaciffM5VwM9PTs/CStMwuxj04tKanrPOL2PLDg5Nd2**Suc+ZzD2yWu2FwyOyev2ksvXl/5XnnL22T4oVlHdIaCFFPJUVvbgZJg0pz85TtcrmD3tJziJJxUZJGqrM/QlyKC1NUHxi7O12Q5VYRDSTiUJodyeF86tZhLZyzXGirUhiq14azaWK7GHWrMrjTYlQanGitX4y4VTqoJtxJ3JBstKsKixM0qwvovfIk7dLgjxejQZVjU6WVqzKI2WVUmqxK3qLAyDW7V4KQGd2owlwZzazC3BisfXPgQTg3uVBptKnzgEdXX+lJjtGuMdnWmTYs5tASpxR0Jrh2/liuN5SrcrSJIFWFTEVY1YbvaAm5TExYVUabAbCrCkUxYkgmzwmRRmK**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**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**15Y9NzherOnY7ctVFzHbjpcr8g4PTnL/EZ+c0FD76m6zhVvufXzTz9mKNt1xnuqLna8tte4s/mBefl7SsL7c+sfT80/XNGes++8KrNQQTiSTQ6FyZmEkVNXuvedic1aYTNurjzkiOxztBdd7luz97LSYNXilSlGciJWsOVEw7yN5946090qoOK6viWvkat2u45X9eTV9G0+3aBIO5P1erW5tkudVvjc0fqsN+qTFlmefbPaUtt3urYP21L99Erze/b2E5fizx1pVRnNatysISxq/Eafzf7bvhQBpANnLo6ee0JFlCtxl0rmq4USdyQZS8fOfLuy2j8CT3U5nyIf05c2pdE26MsPo8K**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**kWIKKsiDCyya7OmDeEMAAAIABJREFUXSdq10YWr1dXnAgUBu4h8WXWlXrN5eGkDbrZrqKUdypd4Wq9l7yt9tss2dP9ZFaWJyKxMm6je3/lzWzv7eoHI2nb69ndNVzbYEwyX6Q93nXu8r6C61zXPSSlPCpZCE8uXfVjXaDz+Ul373erNJQQsPERi7eMx8oII1sYLXJRyZr9wrtctV/zkvUizKdv3z/rg0qFGdkMPFHzTwfqrn2An/pn+wP2Xv1LCaaEV8Zjx4XSIg4knJFxRsYYBaFlZIKXJlYxswrMyggjYYyIs6+ELGcVhFUQoKKEE03RLYSK0xwOBAxIaGgNAKui7DgpUVZFHDLikJB0EXWIGFAwUpma6ZqzrhphJDOthEZizYxkZkQI8AjgcYa3MDwOQj6NGk5rOK1YgBhNCzFARIEHcXAI60dYHmU4lOIRGxdN8xgjIOy4ftGbfEFNZnwL6pAxVsJZEWdFnJEwWsFpLw5eHPAveDnBVPW3etG5RByqOUOCHBJCqBH20khQ9c1abwwIhQb0oowPZ3QL4wtLKFq7vzx0yT5gjuf35KViZpVIoEAEV1rXf8LVHZ5UEWnjc6Tbhy/0WOKL1eYnK3fJk2aWbNjXwbXcW7zSU9cxsu+Ud0H6xQUrC77NKImleYRSLaQgXHq0r7jt/35TOjOxouPK0NazXYk7Pe6r92MzXDApRAMuKs25Ja8vX73xRaIHspY29T3YfkJ31jw4WtAz5fv8OXTOleuPNp0MOOvvHDzbEvF9+cwMb831Z1m7avbmdjprO8Tue9suNpoSKvEk0ZJaFU17MIY32dTv1klq78j8jUrGaflM4AaaItg2NHt7nidtUM2EjNBqDFXMtdxN3RFYsDqg9jz72iEgSW7t2rOfS5uJDQWey7eo7TK70+dqvzct2VOiPtmV3b7zbCfffT8quQRJE6YBN7ak/DtQfpLrqOoZmu5QEFLG3iFCggXoEYtL1xwQ3+WqveRleVN4fAHm8KGsjgDtA4VQ+Hd6z3gRn/U7xUom2mNakF1b3/vBfvCf7XfZ23iJMBI04YX4Ci9/278UcDBOwd/y0vQmXjIKykgQUbnsZMPGsz2o1Y3TAg4EjJEQRoZYBXaoyAQsUVZFHBKcLiDpPOLgUVqE0uTtFb0/cb2RhMtEhyYyFRMjQaxkBjzMeFBQhQNuYgGAOs5Lmo8GHGStWprd7cjuiCQVhBXMhNu2v31d7o1o8Dt4KeAsjwMumhIslALRfpipRl7Hy9C3fpNwRkEdMpwumgkucVvTj64bMFu82927+WITlFqKA/8EL/1hCQVr95eGLlnwwwHzd/AyCshmgittun+K74tILIu0OnP0wUPFN6IWl1f5Hwitd4lt9ZsONnsvP57DOHOLb2rNd1YdU9ecrUnbIcUQTgutQSme7ee7mgZHf8hpzpdvNXYOfZcu2vd5tdtD07J4iOAtDB+ZVrX2bO+158ahiusnXFdqeh4tSL/4Y25r4PbQ9hz/eaE90PNk1rKqHy40B24Orc+t/rn6Ktf2aLq9/KT73s9lnanblMAjI33/pflZtSc9g7OzFJiSzTZ9wYbawG1j/hbfsvMN5xsHzSly8qZmpddI3lCNWOVoWp7ucEpdQ8Tu5u8Y7VLfcJ50zbqxxtM1eNx1edeF9vbno9R2Pn23Ll59FJNcUlJ9f09eb/wyveH26En+yoGy7u3ne7ad69p4snlnQZvn+tOZGQpMvCsvp3zm5YeR/MslsG/3T/7rCnUoMMObF575zMtPbm/kJcaqKBAj7J4pKVxksgci+SirJyLZGZFcZUpzYRQH2VwRyRVRKVWQnbMAESF4s5U3pwrmNAGjFYgUpqRxYVZhik2AGQkGoinNY7Z6zDYPTkuIVYBSRThJhpa4j7l787TbpsVFpmQXYucwRoJpPtLGRdrESKsI0wrOqma7EGnjIu0umOXQdAFnxOmZpe4bj20H66bYhChCDU8VwlOFCKsnwu6eva52h/NeTHoJQnI4VW2hdQslwWk8nMLBKVWYvTJ**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**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**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**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**DNS7/O4N9JxphhDAeNkY93ioyPwssxwzCM/oFHszIKIgkeB97fPTbyCi8VhPLCQI1cUrh0W/nI6MsdvsJLycTyUSwfbuf2irfKuh5jCS5md0PgrjF/uTNqYZ4l1QWnuE2pVRaaR0gOZvlI2h1mq4RYDmL5KaQrkvQghIITPpzwY7QeafOEE04T8ERZnXBy1bQUJ9/0OIe7NJ+uik7VUXsAp72mNM+U5EqzzW2yVmKgKtrhQuzOWJsw3SZPTZMtieLCZT7uylDc+opo2yk89RyyJH/xGrd6dWT+MjVxvY/YWTuVqYLt5TBRjpFOxCYgVsGUzKF2Hkqrwkk3SlQe0e5knq4zWQu+XakvzW6dkaGakxVTqhOnqmCraE7So0kfRvCm1ErExkFpEpSqWAg/TuooqWJARRk9PP6c78/h5YTdfToSt/pUZHIu7lDQd01q/+bbgFXhDOlf1pzknSXPDGPCJ+Yfy8t3t1/yshBz+FHWiwA95GjzyRv6d9GvctRA7+87A7++EvXtHcQ/R6E0ZPB4UrDfKZhRIEaAF5//2Lz8+1swaAx91HG0j9W/DBoGucvzZeJFnPaitDY+GfCeT77jvKQVlNJQSodJHlp8wq20vLrD4aBB/VQ6KbUMYmQzw0Os8FVKxdcrXL7+0RX75Dxv7/7ivlm2ysNlV6qa75zjbsU7Cr+z5uy70FXaMFjSdGf1cX2q/dCKw+oJrreidWBT3nXYpltIH2J1p2zzFNXfEjrvnXBfm2krXnOo7fqT0frrdzccvYymarBNn5np2VN2ZZ+zq6Rz8Gf5xpysym+zqo65+hekyzNS3AcLbq/c1zWfVcWux9lSh6f75mm+a7b9wpIVlWr704UZ0oq9TeuPNeKJzrQtel797fKOOxlH/HNXlP6s3imof3DWd3v+6vylJ32tz0aVm4+3FV5O3lK3q7gzliiYTrgOu/qc3XeKGu9ZNzYgyQXrz9Xvrey42HTnQt39JWtr8VRXDKHgpIIBBWW8fz4vDcM4L9V9tXAPClzoeELE309NmFXRdOVfyedW/cyNGUYwOPbvd/8fY+NuyWWNkxdfQBkvDDSIViFagSYCgHwQvdfqr/eq2cy8Ru97eG+qBJ7IFzbh3TruGftniFURRnmv8/bm8ymZAWdedCZQe/Wj3kv3Bh+UlmvFBQ2FxW255c3nypvOlTedK2/+W+h8YU3rpdB6m/fyMX8/+1i8NAwjW+iasiQbI3SEDMC0D6Z1BHjfK2U5DBQIKDCtYYTXQnjhxLJFjjMDD56/usPhoEH9VP5FSgXEKBAQYIY3UZ4pKSW7i660DDwV2x5/TVcdc19ztg7Yd3gK/B2V6sD3dNHGUz7iB9fu/EZv37PZSy8cLWq5OWSsO187b4sfphTEKs9bIddeeXqm7PLSXVpd76MTFVfmZbrU7vtHq9q/yfCihG62KbOXudrujBbX3mQOKdK1R4fLe+ZmVdTdGI5focYmuytanu0pu/INcLcODuX7u1celGpuDB0ubo/PLPS1PvrOUXVG6r3o651BuvUrowedLStOebfmX0n5oW71mTpih7Oise+UdGWmo1DueXDE0/z1SlfGwRbflaezwPkzfJfS84Q66P1ZvOq9Njo9vSTX29P8cBgcbrjQ0F/SctdCXMQJEaPUT8XLoGHcH36evPZoePIFmFVeSaT1e6iJMhoK9C8WnzhRVmsYn1fcvmLBoGGMGoaRW+mN+G49FHfSFH/KFH/KlHAqKn7i9V9cCa/TB6jkpDn+uCnhxJSE/Kg0HqV8OFUdTWk4rf0xV6C3S0YYGWXkCIcWni5BSdkxiT+jS05C8SfMcb9X8SfM8cci4w7Ai7b7G7s/6t1U33Q58uttYfPywhbmfrkw58uFOZMW5kxamPvFwtwvFuZ9sTDvi/HXfzXl/M+CvP9v6v5dh52GETSCw3+f/uUrVtP30GI7ZU4RIaIGpqph2vv+vFTNQIVpL273x9r80IKcoxerJ6ofJ/Nw0KB2lk9KqYAYBQYCDHiYEUyEa8FKV8+j4Kmyq2ELznJXB4XLt/a7Oiube5X2Z9PTS6m95ce41pLqa7XXh2cuFY6WXi3Rrv1ryblJdpcZiFFJnlWn2mt7hmalub6YV/rD+UbfleczQVVZw+0VRwOTE6pQxhtFyLOzxOrmZ2nLKyfNO7crv15sexi3mgtcfrR4qRKd5CpoeLC95NKcrErlyuD8pRURc/OOlTY5mwZSlhcHWh/OTa884e4+x3dRe301vc++ZfPR5Kpom/K1Q12bU/8z3xnovXvBe2PKwhxn/cDWbN+X8SXM7gZv68NFK1xNN0ZW7av913ee+ZlC260hYm8gm79+rKrrv74vWnaiUbj26Ousctj+KXk5aowahlGu1IclHo0CbpTlEZZDWOFX+ZnfUTijo3b9q0XHK9V2wzDGRv/d7v9zLDhqBEcMI3jzwZOatms1rbdq2vpr2vprxzXw4dT/PvqA+/39qmvrb+y46wncWphRhCwunE6oU0nNQn1UXkoIKyCMYHbIXySc/vlCoKPrbnP7QH1bf93v1qX+uku3a9tuNHT0PXw69GKe8WP8kOtae5Gkc1B6AMtUsHQNd2g4q2Gsjjq8KOtDWR/q8KIO/S8oxOGfvKRk3ynVMMaM0aGP18x9RF7eCQbTtpeFxRfBlBemvL+rfznBS6LakqLFJJ6uvXozdMf8lpfwBC8RRjDT8vQMp35taPu5jqj4ArXnQXZFS+JqJWmr75uVfMJm8dLNoQ0na1bskdSuoZkrfIfKbubw3ZOtxeGsbKJ5Uwq36mR74NrTb2nnpIVFuwqbtM5nXzuc5U23V5+sDU9yIyFeZko1zcNgnfSvOecPljZ5Gge+X1rVeHUoMUPD5peW1Q3uKGz+elmZeOXu/JVc+LyCE5UdpbX9qasqai49+TbdddR59ZzYY9+rNdwempNZbFpchiaU7y3tFfrugx3lF5WeC75bUfHnlaY7u04GpiwoyvqptvrSo/nLnE19IxsON06eI6YuVa72DVm3a9nCrWPOni/iq1acanNdfjIz0wPZVYTSPt147KhhDD8Zep648c**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**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**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**27kxlyILiQhPBLKjoE5EL20w+AEAIBgYFovX4ix6r2zb3hpZtHs5PpJehkLi2LprKgN2fCJIpktvBF/4tErbxdvTS9tvt4ltPtRyVwK0hS5sbK8TgMAAKFQWMReP4ITuphtqs5FSbdiU2rm08RxtOYFNBG09uG242xPfwAAEAiF8J2M03gNCAVBwA8GAAg6HJ4l6fciUlgkuogIC0iIMDK5pEKkD7ub1stpvDWExmqL0SDwAwCUju71O4oIG0soMD8GEUSmcVbsqnP0jQIwXn29Gx2idw1jSWJ09i5Iz42kcgmZAiJDQIaFFLpgPsyJS6mZu/zad9eFzsEAwMdgA2MzYe8A3ppeBgHYfrpyzoZyUmrD2sxCp3sQABA2gANAMGyI4vUm0sRpyOO36qMTbi9KFy3MaI9cXZi0956lexCMndw2jdeGUAgEQ2AUr2bO5TbNSSggU3lEOptI40EpxY1qV9jdtF5O4y0jBEAgBIJ4hdCh716SmhuZWE9AmyI2sWjfsfsCY67C/00X1MkIAYDPsrUoOmM23o5GBGN6KZiPCKDkYmjdufvlvBF8MWBoTCzfmbG9t6aXAICiOmlEQnbEqjtX74kBGGvDhQB4U3qJvxQv/lpH9+LEyw**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Probability and significance

Probability = refers to the likelihood of an event occurring

can be expressed as a number (0.5) or a percentage (50%)
statistical tests allow psychologists to work out the probability that their results could have occurred by chance, and in general psychologists use a probability level of 0.05. This means that there is a 5% probability that the results occurred by chance.

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Levels of measurement

Nominal:

data in separate categories eg. pet choice --> dog, cat, gender --> male or female
frequency count = each category has a count of number in that category

Ordinal:

ordered in some way, though diff between each point is not same eg. small and large, 1-5th
measures relate to same variables, placed in ascending/descending order

Interval:

data is measuring units of equal intervals eg. length in mm using speed in mph
deepest level and most precise level of measurement

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Errors

Type I error:

when null is rejected and alternative is supported when effect was not real
FAKE RESULTS
usually occurs when level of significance is too lenient (eg. p<0.1)

Type II error:

when alternative is rejected annd null accepted when there was an effect
IGNORING REAL RESULTS
usually occurs when the level of significance is too straight (eg. p0.01 rather than p<0.5)

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Key terms in research methods

Aim vs Hypothesis

Types of hypotheses

Types of variables

Variables2

Demand Chara

Types of sample - Random

Types of sample - Systematic

Types of sample - Stratified

Types of Sample - Opportunity

Types of Sample - Volunteer

Types of experiment1

Types of experiment2

Types of experiment3

Experimental designs - RMD

Experimental designs - IGD

Experimental designs - MPD

Experimental Design2

Controlled observation

Evaluation of Controlled obs

Types of Observation - Naturalistic

Covert vs Overt observations

Self report techniques

Self RT - interviews

Evaluation of interviews

Self RT - Questionnaires

Correlations

Evaluation of correlations

Diff between Experi and Correlation

Content analysis

Content analysis

Evaluation of content analysis

Case studies

Evaluation of Case studies

Pilot studies

Ethics

Dealing with ethical issues

Peer review

Psychology and the economy

Reliability

Validity

Features of science

Reporting psychology

Primary + Secondary data

Descriptive statistics

Graphs

Normal + Skewed distribution

How to write reference

Probability and significance

Levels of measurement

Errors

Comments

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