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Psych: Unit 1: Research Methods > Unit 1 - Extra Bits > Flashcards

Unit 1 - Extra Bits Flashcards

1
Q

(Extra Bits) What does descriptive statistics relate to?

A

Ways of summing up and presenting your findings.

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2
Q

(Extra Bits) What can descriptive statistics be contrasted to? Give an example.

A

Inferential statistics, i.e. statistical tests.

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3
Q

(Extra Bits) What does inferential statistics relate to?

A

Working out what your findings are telling you (i.e. what you can infer/conclude from them, principally in terms of which hypothesis has been supported).

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4
Q

(Extra Bits) State the 6 ways of displaying data.

A
  1. Frequency tables (tally charts)
  2. Line graphs
  3. Pie charts
  4. Bar charts
  5. Histograms
  6. Scatter diagrams
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5
Q

(Extra Bits) What level of data do pie charts work well with?

A

Nominal level data.

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6
Q

(Extra Bits) What do pie charts enable the researcher to present?

A

Percentages from within an overall total and show proportions of a whole.

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7
Q

(Extra Bits) What are line graphs useful for showing?

A

Change over time.

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8
Q

(Extra Bits) What do histograms convey?

A

Information about the frequency with which something occurs, through the area of the bars.

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9
Q

(Extra Bits) What do bar charts convey?

A

Information about frequencies through the height of the bars.

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10
Q

(Extra Bits) When and why would you use a histogram?

A

When you have continuous data and want to emphasise the role of category width a well as frequency.

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11
Q

(Extra Bits) What do inferential statistics enable you to work out?

A

Which hypothesis (null/alternative) has been supported by the data from either an experiment or a correlation study. They enable you to draw conclusions/inferences from your findings.

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12
Q

(Extra Bits) What non-parametric inferential test would you use if you had: nominal data & a test of difference with unrelated data (i.e. independent measures)?

A

Chi Square test

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13
Q

(Extra Bits) What non-parametric inferential test would you use if you had: ordinal data & a test of difference with unrelated data (i.e. independent measures)?

A

Mann-Whitney U test

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14
Q

(Extra Bits) What non-parametric inferential test would you use if you had: interval/ratio data & a test of difference with unrelated data (i.e. independent measures)?

A

Independent t-test

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15
Q

(Extra Bits) What non-parametric inferential test would you use if you had: nominal data & a test of difference with related data (i.e. repeated measures/matched participants design)?

A

Binomial Sign test

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16
Q

(Extra Bits) What non-parametric inferential test would you use if you had: ordinal data & a test of difference with related data (i.e. repeated measures/matched participants design)?

A

Wilcoxon Signed Ranks test

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17
Q

(Extra Bits) What non-parametric inferential test would you use if you had: interval/ratio data & a test of difference with related data (i.e. repeated measures/matched participants design)?

A

Related t-test

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18
Q

(Extra Bits) What non-parametric inferential test would you use if you had: nominal data & a test of correlation)?

A

N/A

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19
Q

(Extra Bits) What non-parametric inferential test would you use if you had: ordinal data & a test of correlation)?

A

Spearman’s Rho Correlation Coefficient

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20
Q

(Extra Bits) What non-parametric inferential test would you use if you had: interval/ratio data & a test of correlation)?

A

Pearson’s Product Moment

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21
Q

(Extra Bits) State the 3 criteria that has to be met for using a parametric test.

A
  1. The data has to interval or ratio.
  2. The data has to have a curve of normal distribution.
  3. The variances should be similar.
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22
Q

(Extra Bits) What does an alternative hypothesis always predict?

A

A significant outcome (difference or relationship).

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23
Q

(Extra Bits) What does a null hypothesis always sate?

A

That any difference or relationship ‘could be due to chance factors’.

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24
Q

(Extra Bits) What is the standard level of significance?

A

1/20

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25
(Extra Bits) If the standard level of significance is not reached, how would this be expressed, and what hypothesis would be supported?
p>0.05 (probability of results being due to chance is greater than 1/20). The null hypothesis would be supported.
26
(Extra Bits) If the standard level of significance is reached, how would this be expressed, and what hypothesis would be supported?
p≤0.05 (probability of results being due to chance is less than, or equal to, 1/20). The alternative hypothesis would be supported.
27
(Extra Bits) If the standard level of significance is exceeded, how would this be expressed, and what hypothesis would be supported?
The psychologist would express this by writing the highest level of significance that has been achieved by their results. The alternative hypothesis would have been strongly supported.
28
(Extra Bits) Explain what a type 1 error is?
The alternative hypothesis is accepted and the null hypothesis is rejected, and the behaviour shown was really due to chance.
29
(Extra Bits) Explain what a type 2 error is?
The rejection of the alternative hypothesis and acceptance of the null hypothesis when the independent variable is having a significant impact on the dependent variable.
30
(Extra Bits) Out of type 1 and type 2 errors, which mistake would researchers most likely make?
Type 1 because if you accept your alternative hypothesis you have a greater chance of your research being published (unethical reason).
31
(Extra Bits) What is this symbol: =
Equals
32
(Extra Bits) What is this symbol: <
Less than
33
(Extra Bits) What is this symbol: ≪
Much less than
34
(Extra Bits) What is this symbol: >
Greater than
35
(Extra Bits) What is this symbol: >>
Much greater than
36
(Extra Bits) What is this symbol: ∝
Proportionally
37
(Extra Bits) What is this symbol: ≈
Approximately
38
(Extra Bits) Define internal reliability.
The consistency of a measuring device.
39
(Extra Bits) Define external reliability.
The consistency of a study's findings.
40
(Extra Bits) Explain the spilt-half method.
Test a half of the questions and gain a score, then test the other half and see is the same level of score was achieved on both halves. Results should be consistent to prove reliability.
41
(Extra Bits) Explain the test-retest method.
Used to see if the same results are achieved. A high level of standardisation would make a study reliable. Same results on a retest will show external reliability.
42
(Extra Bits) Define and explain inter-rater reliability.
2 observers consistently rate/observe the same behaviour and the two sets of rating are correlated. If a significant positive correlation is seen, inter-rater reliability has been established + objectivity of results confirmed. Increased by having a standardised procedure and training for researchers.
43
(Extra Bits) State 2 strengths of nominal levels of data that make it better than ordinal and interval data.
1. Quick and easy to obtain because it is a headcount. 2. Can be displayed in pie charts.
44
(Extra Bits) State 3 strengths of ordinal levels of data that make it better than ordinal levels of data.
1. can calculate mean, median and mode as measures of central tendency (so more detailed). 2. Can also calculate measures of dispersion. 3. Can calculate individual scores of participants and see how they differ.
45
(Extra Bits) State 3 strengths of interval levels of data that make it better than nominal levels of data.
1. Can calculate mean, median and mode as measures of central tendency. 2. Can also calculate measures of dispersion. 3. Can calculate individual scores of participants and see how they differ.
46
(Extra Bits) State 2 strengths of interval levels of data that make it better than ordinal levels of data.
1. Scores can be compared directly as precise values are recorded. 2. The scores are more consistent as the same universal scale is used.
47
(Extra Bits) State 3 weaknesses of nominal levels of data that make it worse than ordinal and interval levels of data.
1. Can only analyse the mode of data and cannot calculate the mean or median. 2. Cannot analyse measures of dispersion (i.e. range or SD). 3. Less precise as data is grouped into categories.
48
(Extra Bits) State 2 weaknesses of ordinal levels of data that make it worse than interval levels of data.
1. Ordinal data can be subjective (as people may interpret rating scales differently). 2. Although we can work out the rank order of participants, we don't always know the exact difference between individual scores.
49
(Extra Bits) State 1 weakness of ordinal levels of data that make it worse than nominal levels of data.
1. More time consuming and complex to analyse.
50
(Extra Bits) State 1 weakness of interval levels of data that make it worse than ordinal levels of data.
1. Can only be used with concepts that are measurable through universal scales.
51
(Extra Bits) Explain what representativeness refers to.
The sample used in the research. If the sample is diverse and includes people of different ages, genders, occupations, education levels, etc., it will represent the (target) populations) better.
52
(Extra Bits) Explain what generalisability refers to.
The results of the research. If the sample used in the research is biased and not very diverse, the results cannot be generalised to everyone in the target population.
53
(Extra Bits) Explain when demand characteristics occur, and how this affects research.
They occur when participants work out the aim of the research either because it is obvious or because of a repeated measures design being used. They may change their behaviour and act in the way they think the researcher wants them to act.
54
(Extra Bits) Explain what social desirability is.
When participants change their behaviour to present an image of being a good member of society or to fit into social norms, rather than showing their true behaviour.
55
(Extra Bits) What is researcher/observer bias?
The way the researcher collects and interprets the results of research. They may interpret behaviour based on their expectations and therefore this would lower the validity of the findings.
56
(Extra Bits) What are researcher/observer effects?
The way that participants' behaviour is influenced by the presence (and their characteristics) of the research.
56
(Extra Bits) What 3 ethics come under the term 'respect'?
1. Informed consent 2. Right to withdraw 3. Confidentiality
57
(Extra Bits) What 2 ethics come under the term 'responsibility'?
1. Protection from harm 2. Debrief
58
(Extra Bits) What ethical consideration comes under the term 'integrity'?
Deception
59
(Extra Bits) Explain the meaning behind the ethical consideration 'competence'.
Researchers need to operate within their capabilities, and not give advice beyond that which they are qualified to give.
60
(Extra Bits) State the 7 sections and sub-sections of a practical report.
1. Abstract 2. Introduction 3. Method (design, sample, materials/apparatus, procedure) 4. Results 5. Discussion 6. References 7. Appendices
61
(Extra Bits) State the format used when citing academic references. (7)
1. Author(s) - surname followed by the initials of the first name 2. Year of publication of the article (in brackets) 3. Article title (in single inverted commas) 4. Journal titles (in italics) 5. Volume of the journal 6. Issue number of the journal (in brackets) 7. Page range of article
62
(Extra Bits) What is meant by peer review?
Academic articles need to be read and evaluated by experts in the sample field before being published so that they can ensure that the methodology is robust (i.e. valid and reliable measures have been used to collect results).
63
(Extra Bits) State 3 strengths of peer review.
1. Can be used to check that research will be useful before it is published. 2. Ensures only the most relevant and robust research is published. 3. It ensures that only valid results are published so the journals retain their reputation.
64
(Extra Bits) State 3 weaknesses of peer review.
1. Can take a long time. 2. Some reviewers may not pass research that contradicts their own. 3. May not be possible to detect research that has used false data.
65
(Extra Bits) Define the term 'the study of cause-and-effect'.
Where a researcher can show that one variable is causing a change in another variable.
66
(Extra Bits) Define the term 'falsifiability'.
The ability, in principle, to prove a claim wrong.
67
(Extra Bits) Define the term 'replicability'.
The ability to repeat a study and therefore test to see if its findings are reliable.
68
(Extra Bits) Define the term 'objectivity'.
When a claim is a matter of fact, rather than opinion.
69
(Extra Bits) Define the term 'induction'.
Empirical research is carried out and then a theory is developed to make sense of findings.
70
(Extra Bits) Define the term 'deduction'.
A theory is developed and then empirical research is carried out to see if the theory is correct (i.e. supported by evidence).
71
(Extra Bits) Define the term 'hypothesis testing'.
Based on a psychological theory, a prediction is made about how participants would be expected to behave, which can be tested through research.
72
(Extra Bits) Define the term 'manipulation of variables'.
When an independent variable is changed (manipulated) to see what effect this has on a dependent variable (how it affects behaviour).
73
(Extra Bits) Define the term 'control'.
This is imposed on experiments to ensure that results are due to the independent variable, rather than extraneous variables.
74
(Extra Bits) Define the term 'standardisation'.
The test conditions are kept the same for all participants.
75
(Extra Bits) Define the term 'quantifiable measurements'.
The use of numerical data, which can be used to compare between conditions. This should be observable and objective.
76
(Extra Bits) Define the term 'interval/ratio level of data'. (4)
- Highest level of data. - Analyses are made of the scores achieved by individual participants. - Involves the use of standard universal scales. - The sizes of the gaps between (say) the highest score, second and third highest etc. are taken into account.
77
(Extra Bits) Define the term 'ordinal level of data'. (3)
- Medium level of data. - Analysis is made of individual scores achieved by participants, but only concerning each other. - No account is taken of how much further the highest is from the second highest.
78
(Extra Bits) Define the term 'nominal level of data'. (2)
- The lowest level of data. - A 'headcount' of the number of participants who do one thing as opposed to another.

Psych: Unit 1: Research Methods (6 decks)

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