Research methods Flashcards

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1
Q

Explain paradigms as a feature of science

A

its a shared set of assumptions about the subject matter and the methods appropriate to study it. idea developed by Kuhn. 3 stages in the development of science
pre science- no paradigms
normal science - paradigm established
revolution - when a paradigm is challenged and disproved, replaced with one that satisfies any contradictory finings

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2
Q

how do you report psychological investigations 9 stages

A

title - clear relevant informative
abstract- summary of the research
intro- why study was conducted and provides context for the rest of the work
hypotheses - experimental and null
method- design, pp, materials, standardised procedures, controls
results- table, descriptive and inferential stats
discussion- explanation, relationship to background research, limitations modifications and implications, suggestions for future research
references
appendices

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3
Q

what is quantitative data positive and negative

A

measuring behaviour in a numerical way. e.g score on a memory test. easy to analyse, can replicate objective
less meaningful and low in ecological validity

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4
Q

describe qualitative data positive and negative

A

focuses on pp thoughts and feelings about their life. non numerical data. rich and detailed meaningful with high validity and context
difficult to replicate and analyse low reliability (subjective)

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5
Q

what is primary data positives and negatives

A

the original data that was collected specifically for the purpose of the investigation done by the researcher. + psychologists can collect exactly what they want and can trust the results
- time consuming and may be difficult to access

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6
Q

describe secondary data and positives and negatives

A

collected by someone other than the researcher. data already exists before the investigation takes place and comes from an outside source. e,g in journals or websites
+ quicker to collect than primary, can access data that an individual may not e.g crime stats
-might not contain info that relates exactly to the investigation
less trustworthy

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7
Q

describe meta analysis and the positives and negatives

A

combining the findings from several studies that allows the identification of trends and relationships that might not be possible from smaller studies. especially helpful when smaller studies have found contradictory evidence or weak results.
+ very useful for comparing
- compared studies may not have been carried out in exactly the same way

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8
Q

what are the measures of central tendency

A

mean- calculated by adding all the numbers together and dividing by the number of values. takes all values into account so is most sensitive but outliers can skew the result
mode- most common. easy to calculate, least sensitive least useful
median - middle score. put in order find middle value
good because not distorted by extreme values. less sensitive than mean because doesn’t take into account every value

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9
Q

what are the measures of dispersion

A

range and standard deviation

they measure the spread of data

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10
Q

how to find the range

A

subtract the lowest score from the highest score and add 1. problem= the range can be distorted by an extremely high or low score (has to beat either end) leading us to believe it is spread out when the other data points are close to the mean

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11
Q

what standard deviation does and what it means

A

takes into account every score so represents the spread as a whole. low sd means the scores aren’t spread out from the mean. its more sensitive as every value taken into account but can be distorted by an extreme value

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12
Q

what do correlation coefficients tell you

A

the strength of the correlation 0.8 is strong 1 is a perfect correlation
the direction of the correlation positive or negative

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13
Q

what must the title in for the row of a table include

A

both conditions e.g male and female and the unit of measurement

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14
Q

when are bar charts used

A

when data is in categories (discrete)
frequencies on the y axis and variables on the x
bars don’t touch

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15
Q

when are histograms used

A

bars touch each other continuous data

x axis equal sized intervals and y axis the frequency

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16
Q

when are scattergrams used

A

for correlations, relationship between two co variables

one co variable on each axis

17
Q

what are the characteristics of a normal distribution curve

A

specified by the mean and the standard deviation
curve is symmetrical about the midpoint of the horizontal axis
mid point is the mean median and mode
68% is within the standard deviation of the mean

18
Q

what are the skewed distributions

A

deviate substantially from the characteristics of the normal distribution
negative skew- most of the values are to the left of the peak frequency, mean is the middle of x axis then median then mode (peak away from y axis)
positive skew - most values to the right of the peak, mode then median then mean. peak close to the y axis

19
Q

what are the levels of measurement

A

Nominal- involves frequency data. the number of times something happens in a category e.g number of sunny days
ordinal - data that is ranked with no set interval between the results, e.g 1st 2nd 3rd in a race is ranked but the differences between athletes could be different
interval - most accurate form of measurement, uses equal measurement intervals. standardised measurement units are used like time weight temperature etc.

20
Q

what does p<0.05 mean

A

there is a 5% possibility that the observed difference (or relationship) between the two sets of data is not a real difference but occurred due to chance factors.

21
Q

when do you use a directional hypothesis

A

if there is evidence form previous research that predicts the direction of the results. if only one result is desired e.g drug efficacy

22
Q

when do you use a non directional hypothesis

A

if there is no evidence from previous research or if there is conflicting previous research

23
Q

whats a type one error

A

false positive. when we say the difference is significant and its not. null hypothesis wrongly rejected. the significance level too high e.g 10% instead of 5%

24
Q

whats a type two error

A

false negative. we reject the experimental hypothesis when we should accept it. happens when the significance level too low e.g 1% instead of 5%

25
Q

type 1 and 2 errors and how to solve them

A

the stricter the significance level the less likely a type 1 error is but more likely for type 2 and vice versa. reduce the chance of errors by increasing the sample size

26
Q

what do you need to include when explaining why you have chosen a particular stats test

A

difference correlation or association
which measure you’ve used if its a difference e.g independent groups or repeated
which level of measurement it is

27
Q

write out the table

A

I r a c
n chi sign chi
o m w wc pearsons
I unr.t rel t spearmans

28
Q

how to do a sign test

A

subtract the second column from the first column if answer is positive put a + next to it or a - if its negative
add up total number of + and - whichever is lower that is the calculated value
n = number of participants. if a pp gets a 0 (neither positive or negative) take them out of the sample

29
Q

write a model conclusion

A

the calculated value of 7 is more than the critical value of 5 so for a p<0.05 two tailed test we reject the experimental hypothesis and accept the null as there was a greater than 5% probability the difference was due to chance.