Week 2:Intro To Stats

Question 1

What is Quantitative Research?

Answer 1

-collects numerical data analysed by stats to explain phenomena
-It can use quantities, surveys, tasks e.g. reaction time tasks,experiments etc.

Question 2

What is Qualitative Research?

Answer 2

-it’s exploratory to help us gain understanding of underlying reason
-opinions,motives,asking people,interviews,focus groups etc.

Question 3

Define a sample

Answer 3

a group of participants from the target population (matches general characteristics to make it representative) where the findings can be generalised.

Question 4

What can be the issue with samples?

Answer 4

it’s not easy because we can’t be 100% confident that the findings will fit the whole population so we can be limited by the sample we use.

Question 5

Define Within-subjects design

Answer 5

essentially repeated measures design (doing both tasks)

Question 6

What are the advantages of within-subject design?

Answer 6

-good to use when number of resources/participants are limited
-good when studying a real life setting

Question 7

Define Between-subjects design

Answer 7

essentially independent group design (only doing one condition)

Question 8

What are the pros/cons of between-subjects design?

Answer 8

P-shorter study duration prevents carry over effect of learning/fatigue
C-larger number of participants required for higher statistical power

Question 9

Define the IV/DV

Answer 9

IV-what the researcher manipulates
DV-what the researcher measures and can change based off the IV

Question 10

Define nominal data

Answer 10

categorical data e.g. male/female, blue eyes/brown eyes etc. (qualitative data NOT quantitative)

Question 11

Define ordinal data

Answer 11

ordered data presented in rank order but the intervals between data is not necessarily the same e.g. shoe size,level of attractiveness etc.

Question 12

Define interval data

Answer 12

data has equal intervals between points measured in fixed units and can also go into the minuses but has no ‘true zero’ e.g. temperature,voltage etc.

Question 13

Define ratio data

Answer 13

similar to interval data with equal intervals measured at a continuous scale but has a ‘true zero’ e.g. height,weight,scores on a test etc.

Question 14

Explain what descriptive statistics are

Answer 14

-describes the data letting us see what it looks like
Two types-measure of central tendency AND measures of dispersion/spread(you report these together)
-only tells us about the sample not really the population

Question 15

Explain inferential statistics

Answer 15

random sample of data taken from the population so we can make inferences about the population and reach conclusions that reach beyond our data

Question 16

How do we determine what statistic to use?

Answer 16

we base it off the distribution and data level (NOIR) of our data

Question 17

Define central tendency

Answer 17

describes how ‘most’ people behave and can involve the mean,median and mode (5% trimmed mean)

Question 18

Why can the mode be problematic?

Answer 18

It’s mainly used for nominal data as its the most common value BUT it ignores a lot of information about the other scores

Question 19

Define the median

Answer 19

central value usually used by ordinal/skewed data and only uses one/two values which can sometimes be problematic

Question 20

Why can the mean be problematic?

Answer 20

it can be affected by extreme values due to an outlier

Question 21

Explain what a normal distribution looks like (histogram)

Answer 21

-majority of values in the middle with ‘bell-shaped curve’
-few extreme scores on either side

Question 22

Explain what a non-normal distribution looks like(histogram)

Answer 22

-majority of scores at one end of the distribution
-skewed data so no longer central meaning extreme scores affect the mean

Question 23

How do we get around the problem of extreme scores?

Answer 23

-trim our data could use 5% trimmed mean (take 5% of scores from each end high/low) tend to use the median as a method more
-makes it more representative

Question 24

Give examples of measures of dispersion/spread

Answer 24

-the range
-the interquartile range(the difference between the middle 50% of scores)
-variance and standard deviation(the average distance of scores from the mean)

Question 25

How can the range be problematic?

Answer 25

-it can be affected by extreme scores because it only takes into account the highest/low score
-BUT if our data is skewed we can use the interquartile range

Question 26

How can we calculate variance?

Answer 26

subtract the mean from each score so we need to find the average of these

Question 27

Why can’t we make inferences with descriptive statistics?

Answer 27

■ Although one group may differ from the other (have a greater measure of central tendency etc.), this doesn’t mean that there is actually a difference as it could just happen by chance
■ Descriptive statistics don’t tell us whether the difference between these groups can be inferred beyond our sample to the population
■ What we need is inferential statistics and we need to look at probability (p) values