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rm2 bless up Flashcards

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1
Q
  • Experiments
A

differences in IV leading to possible differences in the DV

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2
Q
  • Observations
A

recording what people do in a situation of interest

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3
Q
  • Self-reports:
A

asking participants about their behaviour.

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4
Q
  • Correlation
A

relationships between two variables.

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5
Q

diadvatage of correlation

A
  • They cannot tell you if two things are casually related
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6
Q

When should you use a correlation, rather than any other type of psychological investigation (e.g experiments)?

A
  1. To test a hypothesis about a relationship between two variables. You then might do an experiment with those conclusions.
  2. When using an experimental design to explore variables would be deemed too unethical or not practical.
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7
Q
  • Reasons to conduct a case study:
A
  • Opportunity to study an individual with a rare quality.
  • Studying the antecedents of interesting events.
  • Anywhere where depth of experience is more important than generalisability.
  • Create a theory, which will then be tested experimentally.
  • The emphasis is on qualitative data, though quantitative measures can be taken. The studies also tend to be longitudinal, so that they can capture insight over a longer period of time.
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8
Q

strengths of case studies

A
  • Rich in qualitative data, and thus insight, which the other research methods tend to lack.
  • Studying rare things in depth can give us understanding of ‘normal’ functioning.
  • Ability to generate hypotheses, from which experimental designs can be created.
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9
Q

disadvantage of case studies

A
  • Lack of generalisability.
  • As much of the data is qualitative, interpreting finding can be hugely subjective (e.g., Freud).
  • Investigator effects likely as you get to know the subject better.
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10
Q

content anyalis

A

As we’ve just seen, content analysis is a type of observation in which people are studied indirectly via their communication:

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11
Q

strengths of content anaylias

A
  • High ecological validity.
  • If in public, it can be more ethical.
  • Allows a lot of data to be analysed at once.
  • When quantitative, it can show differences between groups.
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12
Q

disadbvatgae of content analysis

A
  • Investigator effects. Especially, in thematic analysis, the prejudices of the investigator may influence the conclusions of the study. Known as reflexivity.
  • Presupposes the correspondence of language.
  • Time consuming.
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13
Q

reliability

A
  • Reliability: extent to which a measuring device or assessment (e.g., experiment) is consistent.
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14
Q

validity

A
  • Validity: extent to which results are legitimate. Whether a study measures what it claims to measure.
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15
Q

Internal reliability

A

extent to which a measure is consistent with itself (e.g., IQ must test IQ, not celebrity knowledge).

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16
Q

Assessing internal reliability:

A
  1. Split-half analysis – test randomly divided in 2. Is there a positive correlation between scores on one and the other?
  2. Item analysis – performance on item is compared with overall score. Again, positive correlation is desirable.
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17
Q

External reliability

A

extent to which a measure varies from one time to another

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18
Q

Assessing external reliability

A
  1. Test-retest- same person is tested twice over a period of time.
  2. Replication – any research should produce similar findings if repeated.
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19
Q

Inter-observer reliability

A
  • Measure of the consistency of ratings.
  • Basically, the ability to say that different people observing the same event will observe it in the same way.
    Example:
  • Judges in gymnastics rating a performer the same score
  • Talent shows judges rating a performance.
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20
Q

How would you assess inter-rater reliability?

A
  • Make sure that observers are seeing everything from the same perspective/viewpoint.
  • Observers should keep in discussion to make sure they’re on the same track
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21
Q

procedure for inter observer

A
  • Creating a scale or list of behaviours on which observers agrees.
  • Try to make the behaviour less subjective by using a behavioural coding system.
  • Make sure we observe the exact same situation.
  • Make sure we observe it from the same place.
  • Make sure we then check that we gained the same results (they should agree 80% of the time).
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22
Q
  • Face validity / Surface Validity
A

extent to which a measure appears to measure what it’s supposed to measure.

23
Q
  • Concurrent validity:
A

extent to which current results are consistent with another accepted set of results. Should have positive correlation with an already well-established measure.

24
Q

Internal validity

A

extent to which the independent variable can be shown to have an effect on the dependent variable

25
Q

External validity

A

extent to which results can be generalised beyond the present study:

26
Q
  • Population validity
A

extent to which results can be generalised to other people.

27
Q
  • Ecological validity
A

extent to which results can be generalised beyond the present setting or how realistic the circumstances are.

28
Q
  • Temporal validity
A

extent to which a finding is likely to occur in the past or in the future.

29
Q

How do you assess:

A
  • Internal: search for extraneous variables/confounding variables. Make sure it’s standardised to exclude them.
  • Concurrent [extent to which a study’s results agree with the results of a study that is supposed to study the same thing]: research if other people found the same thing.
  • Population: test different kinds of people.
  • Ecological: different situation better matching the real-world.
  • Temporal: look if same in the past.
  • Face: Glance at whether it seems legitimate.
30
Q

Nominal Data

A
  • Most basic level of measurement.
  • Only tells you how many pieces of data there are in a category/group.
  • Used when data is put into tally charts or categories. It’s consequently often called categorical data.
  • Gives very little information as it’s essentially just a head count.
  • Participants don’t get a score; they are the score.
31
Q

Ordinal Data

A
  • Used when data can be put into an order e.g., 1st, 2nd, 3rd.
  • However, units of measurement are not of equal nor of a definable size.
  • Data cannot tell us what the gap is between 1st and 2nd or 2nd and 3rd.
  • Is often a sort of ranking by the participant and is therefore often subjective
32
Q

Interval Data

A
  • More complex level of measurement than nominal or ordinal
  • Like ordinal except we are sure that the intervals between values are equally split or are quantifiable.
  • Examples: reaction times, height, weight.
  • Essentially, if it is a scientific unit, it’s interval and if it isn’t but can be ordered then it’s ordinal.
  • “Data where we are sure of the absolute values of each data point”.
33
Q

which is mode interval ordinal or nominal

A

all of them

34
Q

what is range type of data

A

ordinal data

35
Q

what type of data is standard deviation

A

interval data

36
Q

what type of data is median

A

interval and ordinal

37
Q

what type of data is mean

A

interval

38
Q

Significant Level

A
  • So, if a set of results are less than 5% likely to have occurred by chance, we say ‘they are significant’.
  • If, however, there is a greater than 5% chance that results occurred by chance we say that ‘they are not significant’.
  • P <_ 0.05 means there is a 5% chance or less that the results were a fluke.
  • In other words, we are 95% sure our results are significant and not due to chance.
39
Q

Type One and Type Two Errors

A
  • P <_ 0.05 is a happy medium between being too lenient and being too stringent.
  • Type 1 error is a false positive.
  • Type 2 error is a false negative.
40
Q

Type One Error

A
  • Wrongly assuming that our findings show something when they don’t.
  • Different ways of expressing a type one error:
  • Rejecting a null hypothesis when it is true
  • Wrongfully accepting the experimental hypothesis
  • Assuming the results were due to the IV when they were actually due to chance.
41
Q

Type 2 Error

A
  • Wrongly assuming that our data shows nothing when it actually does show something.
  • Different ways of expressing a type two error:
  • Rejecting an experimental hypothesis when it is true.
  • Wrongfully accepting a null hypothesis.
  • Assuming the results were due to chance when they were actually due to the IV.
42
Q

Assumptions of the parametric test:

A
  • Populations drawn from should be normally distributed.
  • Variances of populations should be approximately equal.
  • Should have at least interval or ratio data.
  • Should be no extreme scores.
43
Q

Normal vs Skewed Distributions

A
  • A frequency distribution curve gives us a visual representation of variability and tells us how frequently each score occurs.
  • Some frequency distributions curves produce a symmetrical bell-shaped curve, known as a normal distribution.
  • A normal distribution has the following properties:
  • The mean, median and mode all occur at the same point.
  • The distribution of scores is identical each side of the mean.
  • Not all sets of scores are normally distributed. Sometimes distributions are skewed, either negatively or positively.
  • A positive skew occurs when most scores fall below the mean.
  • A negative skew occurs when most of the scores fall above the mean.
44
Q

All sciences have key features in common:

A
  • Objectivity: free from subjective (personal) views and should thus report as things are, not how the researcher would like them to be.
  • Falsifiability: capable of being disproved. If it can be disproved it can be shown that it does not work – which is a good thing.
  • Replicability: only findings that can be re-done by other researchers should be accepted.
44
Q

Using tables of critical value three criteria

A
  • One tailed or two tailed? One is for directional and two is for a non-directional hypothesis. Probability levels double when two-tailed tests are being used as they are more conservative predictions.
  • Number of participants/degrees of freedom.
  • Level of significance, standard level is 0.05.
45
Q

how to conduct a case study

A

Create a theory, which will then be tested experimentally.
The emphasis is on qualitative data, though quantitative measures can be taken. The studies also tend to be longitudinal, so that they can capture insight over a longer period of time.

46
Q

Limitations of Standard deviation

A

May hide some characteristics of the data set eg extreme values

47
Q

Strengths of Standard deviation

A
  • is a precise measure of dispersion as it takes all the exact values into account
48
Q

Strengths of Mode

A
  • Unaffected by extreme values


- It is much more useful for discrete data.


The only method that can be used when data is in categories i.e nominal data

49
Q

Strengths of median

A

not affected by extreme scores 
-

Appropriate for ordinal (ranked) data


  • it can be wiser to calculate the mean
50
Q

Limitations of Median

A
  • Not as sensitive as the mean as the exact values are not reflected in the final calculation
51
Q

Limitations of Mean

A

Can be biased by skewed (overlying) scores.



- often mean score is not one of the original scores (2.4)

-

Can not be very representative of the data at times eg average family is 2.4 children

52
Q

Strengths of mean

A

Most sensitive measure of central tendency includes all the raw data. Most suitable for interval or ratio state

psychology (317 decks)

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