Psychology > Research Methods: Maths > Flashcards

Research Methods: Maths Flashcards

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1
Q

Correlational analysis: what

A
  • A statistical technique for analysing the relationship between two sets of numerical scores (co-varaibles)
  • Represented using scatter grams
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2
Q

Correlations vs Experiments

A

Correlations are different from experiments as experiments contain a MANIPULATION of the IV to measure the effect on the DV → therefore causation can be inferred

Correlation does not involve any manipulation of variables and therefor can not show a causal relationship

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3
Q

Three types of correlations

A
  • Positive
  • Zero
  • Negative
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4
Q

Coreelation Coefficiants

A

MAX value of 1 and a MINIMUM of -1

Shows us how closely the variables are related

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5
Q

Correlations Stregths

A

Strengths:

A useful Primary Tool:

  • can help researchers in initial stages of research and or accruing research finding
  • a sufficient relationship between variables can trigger research

Use when other means cannot:

  • eg when it would be unethical or impractical to experiment on things
  • eg smoking causing cancer
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6
Q

Corelation Weaknesses

A

CANNOT show causation

  • You also do not know which variable is causing the correlation or if there is a third cause (interviewing variable)

Large Amount of Data needed

Lacs validity

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7
Q

Quantative Data

A

Data that focuses in number and frequences wich can be counted and compared

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8
Q

What produces Quantative Data

A
  • Experiments
  • Self Report Closed Qs
  • Observation structured
  • Correlation
  • Iterviews and Surveys have a mix of both
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9
Q

Qualatitive Data

A

Data that decribes meaning and experience which is expressed in words

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10
Q

What produces Qualatitive Data

A
  • Self Report Open Qs
  • Observation description
  • Case study
  • Iterviews and Surveys have a mix of both
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11
Q

Qual data AO3

A

Stregth:
- Richer data
- More Eco Val
Weak:
- Hard to catagorise and compare / analyise

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12
Q

Quant AO3

A

Stregth:
- Less richer data
- Less Eco Val
Weak:
- Easy to categories and compare / analyse

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13
Q

Primary Data

A

Data collected first hand for the purpose and aims of the study

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14
Q

Secondary Dayta

A

Data used in the aims of the study that was originally collected for a different purpose

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15
Q

Primary AO3

A

Strength
- Authentic and targeted towards the aims of the research → less redundant data to sort through
- Collection process can be controlled
Weak:
- Requires much time and effort

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16
Q

Secondary AO3

A

Stregth:
- cheap and easy
- can investigate historical things
- meta analyse
- However this itself has pros and cons
- Can be prone to publication bias and only incorporate data that suits the claim

Weakness:
- Lower quality / accuracy
- Gathered under differrent conditions
- may not be addressing the aims

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17
Q

What are the measures of central tendency

A
  • Mean
  • Meadian
  • Mode
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18
Q

Mean + AO3

A

Add all data together and divide them by the number of them there are

  • Most sensitive, takens in all values
  • But Can be distorted by extreme values
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19
Q

Median + AO3

A

Middle score after the data has been put in nemerical order

  • Not effected by extreme scores
  • But Less sensitive as not all scores in cluded
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20
Q

Mode + AO3

A

Most common number in a data set

→ there can be two modes (bi-modal)

  • Can be used for catagory data
  • But not representative of the data set
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21
Q

What are measures of Dispersion

A

Rang
Stanard Deviation

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22
Q

Range + AO3

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A

Differnece between the highest and lowest scores

  • Easy to calculate
  • BUT Only takes into account extreme values
23
Q

Standard Deviation + AO3

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A

Degree os variation around the mean

  • Much more precise than range
  • Can be easily distorted by anomalous data and takes a long time to calculate
24
Q

What is skewed distribution

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A

a set of data that is asymmetrical

25
Normal Distribution
- Data distributed symmetrically - Mean, Median, and Mode are all in the middle - AKA Bell Curve - EG IQ
26
Positive Skewed Distibution
- Not a normal distribution - Data is NOT symmetrical - Scores are dispersed consistently around LOWER scores and NOT the mean - MMM not the same - **Called a positive skew because the MEAN has been shifted to the RIGHT**
27
Negatively Skewed Distibution
- Not a normal distribution - Data is NOT symmetrical - Scores are dispersed consistently around HIGHER scores and NOT the mean - MMM not the same - **Called a negative shift because the MEAN has been shifted to LEFT**
28
Frequency Tables
More useful than raw data organise values into groups when there are a large number of them Patterns can be clearer
29
Bar Charts
- used to represent DISCRETE data - data in catagories, placed on the x-axis - colomns DO NOT touch and equal width and spacing
30
Histograms
- represent continuous scale - columns touch because one forms a single score on a related scale - Scores (intervals) are placed on the x axis - highest column shows the frequency of values
31
Frequency Polygon
- alternative to the histogram - lines show where mis points of histogram columns would be - Useful when comparing two or more conditions at the same time
32
Scattergrams
- used for measuing the realtionships between two varaibles - data from one varaible is presented on the x-axis while the other is presnted on the y-axis - → x is plotted where the two varibles meet
33
Line Graph
- Rep of continuous data - link up a line to show a continuous trends - IV on the x-axis - DV on the y-axis
34
Four Steps of the Sign Test
**Step One:** - determines the differences in data sets using + or - signs - remove any sets that contain no change → the number of conditions after this will become N → N = 8 **Step Two:** - Add up the +s and -s → the LESS FREQUENT value will become S → S = 2 (the - is the less frequent, and there are 2 -s) **Step Three:** - Work out the type of hypothesis → one or tow tailed - EG: assume it is a two tailed one **Step Four:** - check the significance level (should be in the question) → if not then assume always it is 0.05 - EG: - N = 8 - S = 2 - Two Tailed - Therefore, the null hypothesis is accepted as S (2) is not equal to of less than 0
35
When you don't know the level of significance, assume it is
0.05
36
Significance means
it is more likely the data did NOT come about by chance
37
Hypothesis Testing
In order to test hypothesis, inferential tests are done in order to accept or reject a null hypothesis → calculate the probability that chance / coincidence has caused the difference in the test/experiment
38
Infenerational vs Descriptive
**Inferential**: tell us whether or not the statistics are significant or not **Descriptive**: tell us things about the data set
39
Nominal Data
- binary, can only be in one category - NO order to the categories - eg yes/no questionnaire
40
Ordinal Data
- data with a natural ordered but can only be loosely compared in terms of magnitude - the intervals between data not necessarily equal - eg any data that is in a scale
41
Interval Data
- data with intervals that can be compared in terms of magnitude - most accurate - eg time, temp, weight
42
Three Steps to choosing an inferential test
1. Find out the **type of study** - Either difference or correlation based → can be inferred from the hypothesis 2. Find out the **type of experiment design** - Matched Pairs: **Related** - Repeated Measures: **Related** - Independent Measures: **Unrelated** 3. Find out the **type of data**
43
Mathced pairs =
related
44
Repeated measures =
related
45
Independent Measures =
Unrelated
46
Mnumonic to remenber infernetial tests
Can (Chi-Squared) Salamanders (Sign) Cope (Chi-Squared) Manually (Mann-Whitney) With (Wilcoxon) Specific (Spearman's Rho) Umbrella (Unrelated T-Test) Rain (related T-Test) Proteters (Pearson's R)
47
Probability
- A numerical value that represents the likelihood of an event happening - Represented by the letter “p” - Between 0 and 1
48
Proabaility based on
how likely are the results from an experiment just random
49
Levels of Significance
- 0.05 (5%): typical - 0.01(1%): used when the research needs to be more confident and eliminate as much of the possibility of chance as possible - eg drug trial - 0.1(10%): used when a larger margin of error - eg a new area of little established research as to not dismiss it too quickly
50
Signigicance
- statistical term which indicates that the association between two or more variables is strong enough for us to accept the experimental hypothesis - A test is significant if we can reject the null hypothesis
51
Type 1 error
Thinking you **have** found something when you **haven’t** Rejecting a null hypothesis that is actually true A FALSE POSITIVE
52
Type 2 Error
Type 2 Thinking you **have not** found something when you **have** Accepting a null hypothesis that is actually false A FALSE NEGATIVE
53
To Avoid Type 1 Error
To avoid: stringent level of significance
54
To Avoid Type 2 Error
To avoid: looser level of significance
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