More Methodology (Hypothesis, Aim) etc Flashcards
What is an Aim?
It describes what the research is for + states a question of what the researcher hopes to find
What does the Aim need?
- clear + precise statement of the purpose of the study
- explains why the research is taking place
- includes what is being studied + the goal of the study
- realistic
- ethical
e.g. “This study aims to investigate the aim effects of alcohol on reaction times”
Define Hypothesis?
It is a prediction of what they expect the results of the study to be
(*states after giving the aim of the research)
What are the 2 different types of Hypothesis and explain them (include phrasing)
*hint - each types has a specific wording that is always used
Experimental Hypothesis - predicts that there will be a difference or an effect between the variables (IV & DV) + (has significant results = have a difference)
Phrasing = varies if its One Tailed or Two Tailed
Null Hypothesis - predicts that here will be no difference between the 2 variables (has no results = have no difference)
Phrasing: start = no difference/ End = any difference will be due to chance factors
What are the different Experimental Hypothesis and explain them
One tailed experimental hypothesis (directional) - predicts that there will be a difference (mostly has significant difference written to compare between the 2 variables) and states the direction. (one group will significantly do better than the other group)
Two tailed experimental hypothesis (non-directional) - just states that there will be a difference but doesn’t state the direction
Describe Descriptive Statistics, give examples and state what it cannot do
- it analyse data to help describe, show or summarise it.
Examples:
- *central tendency
- *measures of dispersion
- summary tables
- pie charts/ bar charts/ graphs
Weaknesses:
- if practical is repeated measures = unable to predict if the results are similar
- can’t explain what caused the results
Give advantages and disadvantages for the measure of central tendency (basically mean median and mode)
Mean:
advantages - it uses all of the scores
disadvantages - it is influenced by extreme scores
Median:
advantages - not as influenced by extreme scores as is the mean
disadvantages - didn’t not use the arithmetic value of all scores = can’t be used for further calculation
Mode:
advantages - easy to spot
disadvantages - there maybe more than one
What is measures of dispersion and what are the common measures?
it is used to notify us if our scores are clustered closely around the mean or widely scattered (lower = clustered/ higher = scattered)
common measures:
- range
- standard deviation (SD)
- Variance
How do you work out SD?
this isn’t the same as A-level Statistics (can’t use the calculator as you will get different answer)
- work out the mean
- each number is minus from the mean and then squared
- for those squared numbers, find the mean
- square root it
What is Inferential Statistics and why is it important?
Needs to know if the results are accurate and valid enough to make predictions about future occurrences
its important as we can make predictions on behaviour, such as diagnosing and treating mental disorders effectively
What is your aim for Inferential Statistics?
How do we know which hypothesis to take?
- To discover the likelihood that the results are due to chance factors.
1. (does land in CV) if the effects of the DV are unlikely due to chance factors but more to do with the IV = reject the null hypothesis and carry forward the experimental
2. (doesn’t land in CV) if the results was due to chance factors = reject experimental hypothesis then carry out the null hypothesis
- How accurate do we need to be?
2. When are the levels of significance used?
like what type of test do they use on
1.To make safe predictions most of the results needs to be at least 95%/99% accurate = called levels of significance (the remaining % are chance factors)
- 1%(p=0.01) = needs certainty such as drug test, prototypes of vaccine etc
5%(p=0.05) = mostly common test such as memory test
Describe type 1/2 errors
Type 1 error - A false positive is where the null hypothesis may be falsely rejected (when researchers falsely claim an effect exist). likely to happen when a P value is too lenient such as P<0.5 or P<0.3
(Reject null hypothesis when it was true)
Type 2 error - A false negative is where a null hypothesis may be falsely accepted (when the researchers may falsely claim an effect that doesn’t exist). likely to happen when a P value is too stringent such as P<0.01
(Accept null hypothesis but was false)
- What is a Statistical test
- How do you know which test to choose?
THE TABLE TO TELL WHICH ONE TO CHOOSE IS IN THE BOOKLET “MORE METHODOLOGY”
*IMPORTANT TO REMEMBER AS YOU WONT BE GETTING ONE IN THE TEST
- meant to calculate the likelihood that our results are due to chance factor but can’t choose the same test for every set of results
2:
- test of the difference or relationship
- types of data (level of measurement)
- design of the study (independent measures, repeated measures, matches pairs or correlation)
What are the Types of data in Statistical test?
- Nominal (categories)
- Ordinal (ordering)
- Interval (no. goes into minuses) and Ratio (starts at 0 and onward)
(the numbers for interval and ratio are the measurements of those like temperature, amount of cash, volume of water etc)
