W2 survey approaches

Question 1

Construct

Answer 1

the idea of the thing, does not exist by itself

not directly observable - construct
e.g., attitude, parenting skill, anxiety, personality traits

Question 2

Measures

Answer 2

data collection methods that are believed to tap the construct

• self report measures, explicit attitude scale, psychophysiological measures
• these are our variables

Question 3

Creating scores from measures

Answer 3

For example, total score on a perfectionism scale, the averaged output of psychophysiology

• measures are all operations or operational definitions
• operation = where do the actual numbers (in SPSS) come from?

Question 4

How good are the measures?

Answer 4

• Need to be able to trust our measurements, or our results can be brushed aside immediately
• This involves matters of degree and it is formalised as reliability and validity
• > it’s not a black and white judgement
• > reliability based on random error
• > validity based on coverage of correct underlying construct
• All we have are the observed scores

Question 5

True score

Answer 5

the systematic effect of the underlying construct
e.g., how much I actually know stats
E.g., should be unchanging across occasions without some treatment to increase (or decrease) it

Question 6

Systematic error

Answer 6

unrelated but systematic factors
e.g., how well I perform in testing situations in general
People with low test anxiety systematically better than high test anxiety people

Question 7

Random error

Answer 7

chance, changeable factors

Whether or not I happened to get little sleep one night at random

Question 8

Components of observed scores

Answer 8

• the systematic effect of the underlying construct [true score]
• also, unrelated but systematic factors [systematic error]
• chance, changeable factors [random error]

Question 9

Inferential statistics

Answer 9

• using sample data to make inferences about population parameters
• if nothing else is known, the statistics of a sample (e.g., the mean) are the best estimates of the population parameters (e.g., height of Griffith students based on this class).

Question 10

Sampling bias

Answer 10

• due to faulty sampling methods, some important subgroups of the population may be over- or under-represented in our sample
• > Systematic variation

Avoidable through random sampling: Ideally, every member of target population should be equally likely to be selected for the sample

Question 11

Example of sampling bias

Answer 11

A classic example…
-> American election in the 1948
Polls predicted the Republican candidate, Thomas Dewey would win

However, the Democrat, Harry S. Truman, won by a landslide, sample was biased as it was done via phone calls, this sample was more affluent than the general public and therefore was not generalisable

Question 12

Sampling error

Answer 12

• the term ‘sampling error’ implies a mistake but this is misleading … it’s a natural thing and can’t be helped.
• > Think of this as sampling fluctuation

so the question is not whether the sample mean differs from the population mean (it almost always will) but
-> how likely is it that the difference we observed could have occurred by chance

Question 13

Sampling distributions

Answer 13

• the distribution of a statistic that we would expect if we drew an infinite number of samples (of a given size) from the population
• sampling distributions have means and SDs
• can have a sampling distribution for any statistic, but the most common is the sampling distribution of the mean

Question 14

Standard error

Answer 14

the standard error of the mean is the standard deviation of a distribution of sample means

• it represents the typical or average distance between a sample mean and the mean of the population
• used to define and accurately measure sampling error