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Research methods Flashcards

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1
Q

What are the sampling techniques?

A
  • Volunteer
  • Random
  • Opportunist
  • Systematic = Selecting every “Nth” person
  • Stratified = Selecting people that are proportionate to the sample as a whole (so it is representative of the population)
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2
Q

What are the pros and cons of each sampling technique?

A

-Random:
Pro = Less biased
Con = Could have a smaller a range of people

-Random:
Pro = Less biased
Con = Impractical

-Systematic:
Pro = Less biased
Con = Not representative of population/ whole sample

-Stratified:
Pro = Representative of population/ whole sample and the amount from each group of people
Con = Can be inaccurate if the sample size is small

-Opportunist:
Pro = Convenient, easy
Con = Can be susceptible to researcher bias

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3
Q

What are the types of experiment?

A
  • Lab, IV is manipulated
  • Field, natural environment but IV is manipulated
  • Quasi, IV is not manipulated, it has happened due to naturally occurring events
  • Natural, studies where the experimenter cannot manipulate the IV, so the DV is simply measured and judged as the effect of an IV
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4
Q

What is meant by a self report technique?

A
  • Self reports are a method of gathering data where participants provide information about themselves without interference from the experimenter.
  • Such techniques can include questionnaires, interviews, or even diaries, and ultimately will require giving responses to pre-set questions.
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5
Q

What is a type 1 error? [:(]

A
  • A type 1 error is when a researcher incorrectly rejects a null hypothesis that is actually true
  • This means that you report that your findings are significant when in fact they have occurred by chance
  • This usually happens when the significance levels are too high (10%)
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6
Q

What is a type 2 error? [:)]

A
  • A type II error is when a researcher accepts a null hypothesis which is really false
  • Here a researcher concludes there is not a significant effect, when actually there really is
  • This usually happens when the significance levels are too low (1%)
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7
Q

What is a P-value?

A

-The level of statistical significance is often expressed as a p-value between 0 and 1
-The smaller the p-value, the stronger the evidence that you should reject the null hypothesis
-For example: P0.05 = The hypothesis is very valid
P0.1 = The hypothesis is not as valid

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8
Q

What is the table that shows the inferential statistical tests|? CSCMWSURP

A

|Unrelated | Related | Correlation
|Nominal | Chi2 | Sign test | Chi2
|Ordinal |Mann-Whitney U| Wilcoxon | Spearman’s Rho
|Interval | Unrelated T | Related T | Pearsons

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9
Q

Which experimental design is used for unrelated and related tests?

A 
Unrelated = Independent groups
Related = Repeated measures & Matched pairs
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10
Q

What are the levels of measurement?

A 
Nominal = Categorical (Smoker/Non-smoker) [Discrete]
Ordinal = Ordered data (Rating scales) [No equal intervals, subjective as it is ordered]
Interval = Data based on numerical scales (Weight, size, scores) [Objective and intervals are equal]
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11
Q

What is the correct order for referencing?

A

-Name, Date, Book name, Place, Publisher
-For example:
Duck, S. (1992) Human Relationships, London: Sage

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12
Q

What are descriptive statistics

A
  • Descriptive statistics analyse data to help describe, show or summarise it in a meaningful way
  • Examples are: Measures of central tendency and measures of dispersion
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13
Q

What are measures of central tendency?

A
  • Measures of central tendency are examples of descriptive data statistics that depict an overall ‘central’ trend of a set of data:
  • The mode
  • The median
  • The mean
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14
Q

What are measure of dispersion?

A
  • Measures of dispersion describe the spread of data around a central value (mean, median or mode)
  • They tell us how much variability there is in the data
  • There are two measures of dispersion:
  • The range
  • The standard deviation
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15
Q

What is standard deviation?

A
  • Standard deviation is a measure of dispersion that shows the spread of scores around the mean
  • The greater the standard deviation the great the spread of scores around the mean
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16
Q

What are correlation co-variables?

A

-The two variables that are measured/collected by the researcher and then compared to each other

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17
Q

What is a correlation coefficient?

A
  • A number used to represent the strength and the direction of the relationship between the co-variables as a number between -1 and +1
  • A perfect positive correlation is +1
  • A perfect negative correlation is -1
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18
Q

Why are correlations not a good way of analysing data?

A
  • Correlation does not show causation
  • This means that although a relationship may exist, it doesn’t show which co-variable caused the change
  • Doesn’t show other extraneous variables which may be the cause
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19
Q

How can investigator effects be avoided?

A
  • Have an interviewer who had not witnessed the event
  • Have an interviewer who doesn’t know the aims of the study so that they would not be affected by their own perception of the event
  • Use open-ended questions so the interviewees are able to give a more detailed and accurate version of what they saw
  • Use a questionnaire (Or other means) to collect data without face to face interaction
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20
Q

What does probability mean?

A
  • The numerical measure of the likelihood or chance that a certain event will occur
  • The accepted level of probability in psychology is P<0.05
21
Q

How are statistical tests used to determine the probability that their results could have occurred by chance?

A
  • 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
22
Q

What are the statistical tests used in psychology?

A
  • Chi2
  • Sign test
  • Chi2
  • Ordinal
  • Mann-Whitney U
  • Wilcoxon
  • Spearman’s Rho
  • Interval
  • Unrelated T
  • Related T
  • Pearsons
23
Q

What does P≤0.05 mean?

A
  • This means that the likelihood that the results occurred due to chance/luck is equal to or less than 5%
  • So the psychologist can be 95% certain that the results are down to changes in the independent variable
  • If the results pass the statistical test at a P≤0.05 level then the null hypothesis can be rejected
24
Q

What is significance?

A

The level of probability (P) at which it has been agreed that the null hypothesis can be rejected

25
Q

What is a pilot study?

A
  • A small scale practice investigation
  • Pilot studies are conducted before the full research to identify problems with the design, methodology the analysis of the findings and to gain feedback from the participants
  • The issues that are identified in the pilot studies are then dealt with before the main study is conducted
26
Q

What are some of the problems in experiments which could be identified in pilot studies?

A
  • P’s could not understand the questions/instructions/tasks
  • P’s could figure out the aim of the experiment and develop demand characteristics which influenced the DV
  • The experiment could be too expensive or take up too much time due to the method or analysis of data. This could happen if the behavioural characteristics are too vague and hard to identify
27
Q

What are the different experimental designs?

A
  • Independent groups
  • Repeated measures
  • Matched pairs
28
Q

What is counterbalancing?

A
  • Splitting P’s into groups to change the order in which they encounter the different conditions in the study
  • This method is used in repeated measures design in order to counteract order effects
29
Q

How does counterbalancing increase the validity of an experiment?

A
  • Counterbalancing counteracts order effects as 50% of the P’s are encountering condition 1 for the first time and the other 50% are encountering condition 2 for the first time
  • Counterbalancing counteracts the effects of boredom or fatigue on P’s
  • Counterbalancing can reduce the effects of demand characteristics being developed in the conditions
30
Q

How is the standard deviation calculated?

A
  • √Variance = SD
  • For example: If the data’s highest score was 10 and the lowest score was 1 then the variance would be 9 and the SD would be √9 which is 3
  • If the data’s highest score was 5 and the lowest score was 1 then the variance would be 4 and the SD would be √4 which is 2
  • Therefore a lower standard deviation means that the data is more clustered CLOSE to the mean rather than a higher SD which would be clustered further away from the mean
31
Q

What is discrete and continuous data?

A
  • Discrete data is data is data that can only take certain values
  • For example: The number of students in a class, whole numbers and goals in a football match
  • Continuous data is data that can be measured on an infinite scale, It can take any value between two numbers
  • For example: Height, weight and length
32
Q

If a test in a class is too hard which type of skew would show on a distribution graph showing the scores?

A
  • A positive (Right) skew would be shown as the majority of the students would get lower scores than normal
  • The data on the graph would be mostly on the left
33
Q

If a test in a class is too easy which type of skew would show on a distribution graph showing the scores?

A
  • A negative skew would be shown as the majority of the students would get higher scores than normal
  • The data on the graph would be mostly on the right
34
Q

What are the features of science?

A
  • Empirical method
  • Objective research
  • Controlled research
  • Replicability
  • Falsifiability
  • Theory construction
  • Hypothesis testing
35
Q

What does empirical mean?

A
  • The information that is used is gained from direct observation of participants rather than providing information from reasoned argument or beliefs
  • This means theories must be tested
36
Q

What does objective mean?

A
  • Data should not be affected by the expectations of the researcher, data collection should be systematic and free from bias
  • This maintains the validity of the findings
37
Q

What does controlled mean?

A
  • In an experiment we assume that any changes in the DV is due to changes in the IV
  • However this may not be true if extraneous variables have affected the DV
  • A lack of control of variables makes it difficult to establish cause and effect
38
Q

What is replication?

A
  • Scientists must record their methods and standardise them carefully so the same procedures can be followed in the future
  • Repeating a study is the most important way to demonstrate the validity of an observation or experiment
  • If the outcome is the same, this indicates the original findings are valid
39
Q

What is a paradigm shift?

A
  • An important change in the basic concepts and experimental practices of a scientific discipline
  • It is a change from one way of thinking to another
40
Q

What is falsifiability?

A

-The principle that a proposition or theory could only be considered scientific if in principle it was possible to establish it as false

41
Q

What is theory construction?

A

-To be scientific, a theory needs to be a logically organized into a set of propositions that defines events, describes relationships among events, and explains and predicts the occurrence of events

42
Q

What is hypothesis testing?

A

-A scientific theory should also guide research by offering testable hypotheses that can be rigorously tested

43
Q

How does the sign test show that data is significant/non significant?

A

-If the S value is equal to or less than the critical value then the data shows a significant difference

44
Q

How is a content analysis performed?

A

-Firstly the transcript (What was said) must be categorized into data sets which could be particular terms or descriptions or how often something is shown

45
Q

What is thematic analysis?

A
  • Thematic analysis is a form of content analysis
  • The outcome is qualitative
  • Thematic analysis identifies themes that are recurrent which can be developed into broader categories
  • For example: Stereotyping, treatment or control
46
Q

What are the strengths of content analysis?

A
  • There are less/no ethical issues as the data is already in the public domain
  • The data has high external validity as it is from real sources that are published to the public
47
Q

What is a weakness of content analysis?

A
  • People are studied indirectly meaning that communications are analysed outside of the context in which they occurred in
  • This means that the researcher may attribute opinions and motivations to the communications, therefore the analysis could lack objectivity due to biases from the researcher which could taint how the original communication is perceived
48
Q

What points must be identified when designing a study?

A
  • Methodological issues
  • Ethical issues
  • Design
  • Materials/apparatus
  • Participant variables
  • The task chosen
  • The environment
  • Behavioural categories
  • The reliability/validity

Psychology (53 decks)

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