1
Question#1;
what Chapter are (broadly speaking) the five stages of the research process?
There are five stages of rearch process.
Generating a research question: through an initial observation . From those observations
need data.
Generate a theory to explain your initial observation.
Generate hypotheses: break your theory down into a set of testable predictions.
Collect data to test the theory: decide on what variables you need to measure to
Test your predictions and how best to measure or manipulate those variables.
Analyze the data: look at the data visually and by fitting a statistical model to see
If it supports your predictions. At this point you
Should return to your theory and revise it if necessary.
Question # 2; what is the fundamental difference between experimental and
correlational research?
In a word, causality. In experimental research we manipulate a variable
(Predictor, independent variable) to see what affect it has on another variable
(Outcome, dependent variable). This manipulation, if done properly, allows us to
Compare situations where the causal factor is present to situations where it is
Absent. Therefore, if there are differences between these situations, we can
Attribute cause to the variable that we manipulated.
In correlational research, we measure things that naturally occur and so we cannot attribute
cause but instead look at natural covariation between variables.
Question # 3 ; What is the level of measurement of the following variables?
A)
The number of downloads of different bands songs on iTunes:
ANSWER- This is a discrete ratio measure. It is discrete because you can download
Only whole songs and it is ratio because it has a true value of 0. No
Downloads at all.
B ) The names of the bands downloaded.
A
nswer- This is a nominal variable. Bands can be identified by their name, but the
Names have no meaningful order. That fact that Norwegian black metal
Band 1349 called themselves 1349 does not make them better than British
Boy-band has-beens 911; the fact that 911 were a bunch of talentless idiots
Does, though.
C ) The position in the iTunes download chart.
Answer-- This is an ordinal variable. We know that the band at number 1 sold more
than the band at number 2 or 3 (and so on) but we dont know how many
more downloads they had. So, this variable tells us the order of magnitude
of downloads, but doesnt tell us how many downloads there actually were.
D)
The money earned by the bands from the downloads.
Answer- This variable is continuous and ratio. It is continuous because money
(Pounds, dollars, Euros or whatever) can be broken down into very small
Amounts (you can earn fractions of Euros even though there may not be an
Actual coin to represent these fractions).
E) The weight of drugs bought by the band with their royalties.
ANSWER - This variable is continuous and ratio. If the drummer buys 100 g of
Cocaine and the singer buy 1 kg, and then the singer has 10 times as much.
F) The type of drugs bought by the band with their royalties.
ANSWER
- This variable is categorical and nominal: the name of the drug tells us
Something meaningful (crack, cannabis, amphetamine, etc.) but has no
Meaningful order.
G)
The phone numbers that the bands obtained because of their fame.
ANSWE
R- This variable is categorical and nominal too: the phone numbers have no
Meaningful order; they might as well be letters. A bigger phone number
Did not mean that it was given by a better person.
H ) The gender of the people giving the bands their phone numbers.
ANSWER --This variable is categorical and binary: the people dishing out their phone
Numbers could fall into one of only two categories (male or female).
I) the instruments played by the band members.
ANSWER-- This variable is categorical and nominal too: the instruments have no
Meaningful order but their names tell us something useful (guitar, bass,
Drums etc.).
J ) The time, they had spent learning to play their instruments.
ANSWER-- This is a continuous and ratio variable. The amount of time could be split
into infinitely small divisions (nanoseconds even) and there is a
Meaningful true zero.
QUESTION # 4; Say I own 857 CDs. My friend has written a computer program
that uses a webcam to scan my shelves in my house where I keep my CDs and
measure how many I have. His program says that I have 863 CDs. Define
measurement errors. What is the measurement?
Error in my friends CD counting device?
ANSWER; Measurement error is the difference between the true value of something and the
Numbers used to represent that value. In this trivial example, the measurement
Error is 6 CDs. In this example we know the true value of what were measuring;
Usually we dont have this information so we have to estimate this error rather
Than knowing its actual value.
Question#5;
Sketch the shape of a normal distribution, a positively skewed distribution and a
Negatively skewed distribution.
Normal:
Chapter 2
Q
UESTION# 1 ; Why do we use samples?
We are usually interested in populations, but because we cannot collect data from every
Human being or whatever in the population, we collect data from a small subset of the
Population known as a sample and use these data to infer things about the population as
A whole.
QUESTION # 2 ; What is the mean and how do we tell if its representative of our data?
The mean is a simple statistical model of the centre of a distribution of scores. A
Hypothetical estimate of the typical score. We use the variance, standard or deviation, to
tell us whether it is representative of our data. The standard deviation is a measure of how
Much error there is associated with the mean: a small standard deviation indicates that the
Mean is a good representation of our data.
QUESTION # 3; whats the difference between the standard deviation and the standard error?
The standard deviation tells us how much observations in our sample differ from the
Mean value within our sample. The standard error tells us not about how the sample mean
Represents the sample itself, but how well the sample mean represents the population
Mean. The standard error is the standard deviation of the sampling distribution of a
Statistic. For a given statistic (e.g. the mean) it tells us how much variability there is in
This statistic across samples from the same population. Large values, therefore, indicate
That a statistic from a given sample may not be an accurate reflection of the population
From which the sample came.
Question# 4; In Chapter 1 we used an example of the time taken for 21 heavy smokers to fall
Off of a treadmill at the fastest setting (18, 16, 18, 24, 23, 22, 22, 23, 26, 29, 32, 34, 34,
36, 36, 43, 42, 49, 46, 46, 57). Calculate the sums of squares, variance, and standard
Deviation and standard error of these data.
To calculate the sum of squares, take the mean from each value, and then square this
Difference. Finally, add up these squared values:
So, the sum of squared errors is a massive 2685.24.
The variance is the sum of squared errors divided by the degrees of freedom (N1). There
Were 21 scores and so the degrees of freedom were 10. The variance is, therefore,
2685.24/20 = 134.26
The standard deviation is the square root of the variance: 134.26 = 11.59.
The standard error will be: =11.5921 =2.53
The sample is small, so to calculate the confidence interval we need to find the
Appropriate value of t. First we need to calculate the degrees of freedom, N 1. With 21
Data points, the degrees of freedom are 20. For a 95% confidence interval we can look up
The value in the column labeled Two-Tailed Test, 0.05 in the table of critical values
Of the t-distribution (Appendix). The corresponding value is 2.09. The confidence
intervals are therefore:
Lower Boundary of Confidence Interval = X
26.90
(2.09
SE) = 32.19 (2.09 2.53) =
Upper Boundary of Confidence Interval = X
(2.09
SE) = 32.19 + (2.90 2.53) = 37.48
Question#5; what do the sum of squares, variance and standard deviation represent? How
Do they differ?
All of these measures tell us something about how well the mean fits the observed sample
Data. Large values suggest the mean is a poor fit of the observed scores, and small values
suggest a good fit. They are also, therefore, measures of dispersion with large values indicating a
spread-out distribution of scores and small values showing a more tightly packed distribution.
These measures all represent the same thing, but differ in how they express it. The sum of
squared errors is a total and is, therefore, affected by the number of data points. The variance is
the
Average variability but units squared. The standard deviation is the average variation but
Converted back to the original units of measurement. As such, the size of the standard
Deviation can be compared to the mean.
Question #6; what is a test statistic and what does it tell us?
A test statistic is a statistic for which we know how frequently different values occur. The
Observed value of such a statistic is typically used to test hypotheses, or to establish
Whether a model is a reasonable representation of whats happening in the population.
Question # 7: What are Type I and Type II errors?
A Type I error occurs when we believe that there is a genuine effect in our population,
When in fact there isnt. A Type II error occurs when we believe that there is no effect in
The population when, in reality,
Task 8: What is an effect size and how is it measured?
An effect size is an objective and standardized measure of the magnitude of an observed
Effect. Measures include Cohens d, the odds ratio and Pearsons correlations coefficient,
Question#9: What is statistical power?
Power is the ability of a test to detect an effect of a particular size (a value of 0.8 is a
Good example of statistical power).
Chapter 3
#2 your second task is to enter the data that I used to create Figure 3.10. These data
these data
Show the score (out of 20) for 20 different students some of whom are male and
Some female, and some of whom were taught using positive reinforcement (being
Nice) and others who were taught using punishment (electric shock). Just to make
It hard, the data should not be entered in the same way that they are laid out
QUESTION#3; Research has looked at emotional reactions to infidelity and found that men get
Homicidal and suicidal and women feel undesirable and insecure (Shackelford,
LeBlanc, and Dress, 2000). Lets imagine we did some similar research: we took
Some men and women and got their partners to tell them they had slept with
Someone else. We then took each person to two shooting galleries and each time
Gave them a gun and 100 bullets. In one gallery was a human-shaped target with a
Picture of their own face on it, and in the other was a target with their partners
Face on it. They were left alone with each target for 5 minutes and the number of
Bullets used was measured. The data are below, enter them into SPSS.
Or
with the value labels off, like this