Lecture One Flashcards
Intro to Research and Overview of 1st Year Terminology
What is Simpson’s Paradox?
For quantitative data: a positive trend appears for two separate groups, but when the groups are combined there is a negative trend.
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- What phenomena was behind the UC Berkely gender bias?
- Describe what was happening analysis.
- Why was this occurring?
- Simpsons Paradox
- Men who were applying for positions were significantly more likely to be admitted than female applicants overall at the university. However, when looking at individual faculties it seems as though there was a bias in favour of women and not men.
- Rather than because of discrimination the statistics show that it was because women were applying for more competitive faculties with low rates of admission whereas men were applying to faculties that had a high rate of acceptance.
What is the difference between disaggregated data and aggregated data?
Disaggregated data is numerical or non-numerical information that has been collected from multiple sources and/or multiple measures, variables or individuals.
Aggregate data is a combination of the disaggregated data into a summary of the data so it can be reported or used for statistical analysis and then broken down into component parts or smaller units of data.
What is belief bias?
The tendency to judge the strength of arguments based on the plausibility of their conclusion rather than how strongly they support that conclusion.
Example:
No addictive things are inexpensive
Some cigarettes are inexpensive
Therefore, some addictive things are not cigarettes
The argument is invalid and implausible as cigarettes can encapsulate all addictive things, yet 71% endorsed it.
What does IV and DV mean?
What is the difference between the two?
Independent Variable (IV) and Dependent Variable (DV)
In an experiment, the IV is changed to see how it affects something else, whereas the DV is a variable that is being measured/observed.
The IV is something I change and the DV depends on the IV
What is the difference between Qualitative and Quantitative data?
Qualitative data is information about qualities; information that can’t actually be measured.
I.e Softness of your skin, the grace of which you run, colour of your eyes.
Quantitative data is information about quantities; that is, information that can be measured and written down with numbers.
I.e. Your height, your shoe size, length of fingernails.
What is psychological measurement
What are the steps you need to take when measuring?
What are the two processes involved?
Scientific Investigation
Step 1: Theoretical Constructs
Step 2: Measures
Step 3: Data (observations)
The two processes involved are Operationalisation and Measurement
What is a theoretical construct?
They are unobservable psychological entities.
E.g. Attitudes, beliefs, hopes
They are tools to help us make sense of ourselves and others
What is a measure?
A measure is a tool for getting people to produce data.
E.g. Survey items, reaction times, blood oxygenation level
Ideally, it elicits data that are informative about the (theoretical) construct
What is data and observations?
They are what you get when you use a measure.
E.g. Answers to questions, response speed, etc
These are things you can actually observe.
What is Operationalisation? What does it relate?
Operationalisation relates ‘constructs’ to ‘measures’
E.g. ‘Beck Depression inventory’ tries to measure ‘depression’
‘Inspection time’ tries to measure ‘processing speed’
Operationalisation is the process of finding a good measure
What does measurement relate to?
Measurement relates ‘measures’ to ‘observations’
E.g. Running an experiment, sending out the survey
It is the process of applying a measure to get the data (or observation)
How many scales of measurement are there?
What are their names?
What order do they go in?
There are four scales of measurement.
Their names are NOIR (or IRON for easy out-of-order remembering):
Nominal (1st)
Ordinal (2nd)
Interval (3rd)
Ratio (4th)
What is a ‘Nominal Scale’?
Nominal means ‘name’
This means the possible values have no particular relationship with each other.
There is no meaningful numbering scheme we can use.
E.g. 1. Blue eyes: 2. Brown eyes: 3. Green eyes;
VS 1. Green eyes: 2. Blue eyes: 3. Brown eyes
Neither scheme is any more meaningful than the other
1st order of measurement - named variables
What is an Ordinal scale?
Ordinal scales are scales that have order, but there is no standard of measurement of differences.
I.e. 1. A tennis ladder is an ordinal scale since one can say that one person is better than another, but not by how much (for example you can’t say that Djokovic is 3.2% better than Federer).
- Being Punched < Doing Statistics < Eating Pizza
The numbers are informative and ordered sequentially, but the magnitude of difference is unknown.
Ordinal means relating to the order of something in a series
2nd level of measurement - named + ordered variable
What is an Interval scale?
Interval scale is a quantitative measurement scale where the difference between 2 variables is meaningful.
The interval equates to the interval (or distance) between 2 variables.
The numbers have a natural ordering, differences between numbers are meaningful, ratios between them are not.
I.E. 1902-1932 is a 30-year difference. 1902-1992 is a 90-year difference. Meaning the 1992 gap is 3x the size of the 30-year gap.
3rd order of measurement - named + ordered + proportionate interval between variables
What is a Ratio Scale?
A ratio scale is a type of variable measurement scale which is quantitative in nature. Ratio scale allows any researcher to compare the intervals or differences.
The ratio scale possesses a zero point or character of origin. This is a unique feature of the ratio scale.
Numbers have a natural ordering and ‘zero means zero’ (0 is the character of origin). The differences between the numbers are meaningful. Ratios between them are also meaningful.
I.e. 20-40 vs 20-80. The graph starts at 0 years. 20-year vs 60-year difference. 3x the size of the small one. 40 is 50% of 80.
4th level or measurement - named + ordered + proportionate interval between variables + can accommodate absolute 0
- What are the two types of non-categorical variables?
- What is the difference between them?
- What scales of measurement can each use?
- Discrete Variable and Continuous Variable
- A continuous variable can take on any value between two specified values.
I.e. Weight measured between 70-100 kilos. Continuous because there are values in between (78.151, 91.7 etc.)
A discrete variable cannot take on any value between two specified values.
I.e. Counting number of heads on coin flips. It can be a number between 0 and infinite but it cannot be 4.5 flips.
- Discrete can use all 4 scales of measurement, continuous can only use interval and ratio.
What is the difference between Categorical and Non-Categorical variables?
Categorical variables are qualitative (names of labels, colours etc)
Non-Categorical variables are quantitative (numerical)
In regards to scales of measurements; which ones are categorical, which ones are non-categorical and which ones are continuous variables and which ones are discrete variables?
Nominal = Categorical and discrete
Ordinal = Non-categorical and discrete
Interval = Non-categorical, continuous and discrete
Ratio = Non-categorical, continuous and discrete
- What is a ‘variable used to explain other variables’?
- What is ‘variable to be explained in terms of other variables’?
- It is a predictor, independent variable, treatment etc.
- It is the outcome, dependent variable, response etc.
I.e. Smoking (predictor) causes Cancer (outcome)
Do explanatory predictors have to be causal?
Can variables play multiple roles?
- No. There can be factors in between.
E.g. Religious people live longer. Is it because of their religion? Not directly, but their religion makes them happier and then happiness gives them a longer life.
- Yes.
In the example above, religiosity is associated with happiness, and happiness makes you live longer.
In wide format; when analysing data what does the row and the column represent?
Each row represents a case (person)
Each column is a distinct variable
What does cleaning data mean?
It means when your data isn’t input correctly and you need to make a decision whether to enter in their data correctly or decide if you have to get rid of the data/individual/variable altogether.