Statistics portfolio

Requires SPSS

As this is statistics there should be no actual word limit, but a limit of 3 A4 pages done in APA style 12 point Times New Roman. Plots and tables can be placed in appendices that do not interfere with the word count.

There are three components one for each of the lectures in Weeks 10-12

These assignments (3 pages A4 each) are submitted as a portfolio at the end of the module.

PSY7065
Quantitative Data Analysis
Multiple Linear Regression
Gary McKeown
1
Overview
• Multiple linear regression (the basics)
• Multiple linear regression (advanced)
• SPSS practice in the Lab
2
Confidence Intervals
• A single number is a point estimate
– a midpoint (e.g. mean, median, beta values – today)
• We can also have interval estimates
– upper and lower limits
• How often, in the long run, does a given interval from
a sample contain the true (population) value of the
parameter
• Typically 95% confidence intervals
• Like a complement to the standard error
3
Confidence Intervals
• Work out the upper and lower
limits
• It is within limits we are interested
in this time not outside the limit
• As we are interested in sampling
we use SE and the mean to find the
limits.
• There are two equations
Confidence Intervals
• This is a long run probability so for any given confidence
interval the population mean (or other parameter) is
either within (probability of 1) or not within (probability of
0) the limits
• Therefore this does not mean there is a 95% probability that
the population mean (or other parameter) lies within the
limits
• It means if we take 100 samples from the population approx.
95 of those samples will contain the population mean (or
other parameter)
Confidence Intervals
https://shiny.rit.albany.edu/stat/confidence/
• Click the “Confidence Interval Graph Plus Sampling
Distribution of the Mean” radio button
• Change the sample size and see what happens
– remember larger sample sizes lead to smaller
standard errors
– Watch the sampling distribution of the mean
graph beneath too
• Put the sample size to 100 and repeatedly press
the resample button repeatedly — count the
number of red confidence intervals these are
where the mean is not within the limits
6
Quick recap: correlation
• Capturing relationship between two random variables
• Variables typically measured
• Any departure of non-dependence qualifies as
correlation
– Linearity not a necessity for the general concept, but a requirement for
the most frequently used correlation measure (Pearson’s)
• Correlation may be positive or negative
7
Correlation may indicate causation
• Causal relationships: A causally leads to B -> A
and B may be correlated as a result
– Example: practice makes perfect: amount of hours spent
practicing a skill is correlated with skill level
• However, this cannot be turned around:
correlation does not imply causation!
• Experimental design is needed to say
something about causality – not the statistics
http://www.tylervigen.com/spurious-correlations
8
From correlation to regression
• Concepts very strongly related
• Practical difference:
– Correlation: relationship between two random variables (typically
both measured), DV-DV
– Regression: relationship between random and fixed variable, DV-IV
• Example: practice makes perfect
– Measure skill levels at set times: apply regression to skill level as a
function of time
• Linear regression: model relationship between
explanatory variable (IV) and response variable (DV)
by fitting a linear equation/function
9
Some terminology
• Linear regression captures (linear) relationship
between measured variable and controlled variable.
Outcome variable
Dependent variable
– Measured variable = ‘dependent variable’ = ‘outcome
variable’
– Controlled variable = ‘independent variable’ = ‘predictor
variable’
Predictor variable
Independent variable
10
Think about
𝑦 = 𝑚𝑥 + 𝑐
11
Think about – Matrices
12
What is linear about linear regression?
When one variable is linearly related to
another variable, one can describe relationship
with what is called a ‘linear function’:
𝑦 = 𝑚𝑥 + 𝑐
𝑐 = constant/intercept/baseline (i.e., 𝑦 at 𝑥 = 0)
𝑚 = slope/regression coefficient
13
The model is a line
All of these capture the same relationship
c or b0 = intercept/baseline (i.e., DV at IV = 0)
m or b1 = slope/regression coefficient
error = residuals = part of data not captured by linear relation = the stuff left
over after the model has tried its best to explain the relationship
Residuals
– Residuals are the error, the bit left over after the model
15
Example
A participant trains to improve a skill and completes
a skill test multiple times prior to training. A
researcher predicts that skill score should increase
linearly (at least over days considered)
𝑆𝑘𝑖𝑙𝑙𝑠𝑐𝑜𝑟𝑒 = 𝑏0 + 𝑏1 × 𝐷𝑎𝑦 + 𝑒𝑟𝑟𝑜𝑟
b0: baseline Skill score (at Day 0)
b1: measure of learning rate (change in skill score
per day)
error: deviation scores ( = variation in performance
around regression line)
16
Skill score = Intercept + b1 ∙ Day
Different intercepts (b0) = different baseline skill levels
Different
slopes (b1)
=
Different
regression
coefficients
=
different
learning rates
17
Skill score = Intercept + b1 ∙ Day + error
Different intercepts (b0) = different baseline skill levels
Different
slopes (b1)
=
Different
regression
coefficients
=
different
learning rates
18
Skill score = Intercept + b1 ∙ Day + error
9 Datasets
Ideal line (population)
Data
Best fitting line (for data)
How well each
sample/dataset matches
population depends on
amount of error (=random
variation) and number of
data points.
19
Skill score = Intercept + b1 ∙ Day + error
9 Datasets
Ideal line (population)
Data
Best fitting line (for data)
9 sets of randomly selected values
→minor variations in ‘fitted’ line
(the linear equation), that is, in its
associated regression parameters
(intercept/slope)
20
Find a Fit
• https://shinyapps.org/showapp.php?app=https://tellmi.psy.
lmu.de/felix/lmfit&by=Felix%20Schönbrodt&title=Find-afit!&shorttitle=Find-a-fit!
• Be your own Ordinary Least Squares algorithm
•
(c) by Felix Schönbrodt
21
Hypothesis testing with linear regression
• There are a few different hypothesis test that occur
within linear models
– The hypothesis test of the model – the goodness of fit
– The hypothesis tests of the predictors and the
intercept
• The model hypothesis tests against the null that there is
no relationship
• In correlation
+1 = perfect positive relationship
– 1 = perfect negative relationship
0 = no relationship
22
Hypothesis testing with linear regression
Testing the goodness of fit
0 = no relationship is very important
This is the null hypothesis
no relationship is a flat line
This is just the intercept
The null model is then:
𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 = (𝑏0 ) + 𝑒𝑟𝑟𝑜𝑟
𝑦 = (𝑏0 ) + 𝜖
𝑦 = (𝑏0 ) + 0 × 𝑥 + 𝜖
Often called the baseline model
Field (2017)
23
Total Sum of Squares
SST
The sum of the squares
of the baseline model
when there is no
relationship
between x and y
“how good the mean is
as a model of the
observed outcome
scores”
Field (2017)
24
Total Sum of Squares
SST
The sum of the squares
of the baseline model
when there is no
relationship
between x and y
“how good the mean is
as a model of the
observed outcome
scores”
Field (2017)
25
Mean Residuals
Squared
-5.78
33.4084
-4.88
23.8144
7.02
49.2804
-6.98
48.7204
-11.28
127.2384
-5.08
25.8064
-17.88
319.6944
-4.48
20.0704
-9.18
84.2724
3.42
11.6964
-10.18
103.6324
-10.68
114.0624
1.22
1.4884
-2.48
6.1504
-6.98
48.7204
3.12
9.7344
7.02
49.2804
7.62
58.0644
3.02
9.1204
5.02
25.2004
15.02
225.6004
0.92
0.8464
16.32
266.3424
13.62
185.5044
12.52
156.7504
sum =
2004.5
25
𝑆𝑆𝑇𝑜𝑡𝑎𝑙 = ∑ 𝑚𝑒𝑎𝑛𝑟𝑒𝑠𝑖𝑑𝑖2 = 2004.5
𝑖=1
26
Residual Sum of
Squares
SSR
The sum of the squares
of the residuals once
the model has been
fitted using Ordinary
Least Squares
(OLS) – typically
“represents the degree
of inaccuracy when the
best model is fitted to
the data”
Field (2017)
27
Residual Sum of
Squares
SSR
The sum of the squares
of the residuals once
the model has been
fitted using Ordinary
Least Squares
(OLS) – typically
“represents the degree
of inaccuracy when the
best model is fitted to
the data”
Field (2017)
28
Residuals
Squared
4.68
21.86
4.70
22.13
15.73
247.53
0.86
0.74
-4.31
18.57
1.02
1.04
-12.65
160.08
-0.12
0.02
-5.69
32.43
6.03
36.41
-8.44
71.19
-9.81
96.21
1.22
1.49
-3.35
11.23
-8.72
76.08
0.51
0.26
3.53
12.49
3.26
10.65
-2.21
4.87
-1.08
1.16
8.05
64.80
-6.92
47.91
7.61
57.87
4.04
16.29
2.06
4.26
sum =
1017.57
25
𝑆𝑆𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 = ∑ 𝑟𝑒𝑠𝑖𝑑𝑖2 = 1017.57
𝑖=1
29
Model Sum of Squares
SSM
The sum of the squares
of the differences
between the mean
value of y and the
model
(Sometimes called
regression or explained
sum of squares)
“This difference shows
us the reduction in the
inaccuracy of the model
resulting from fitting
the regression model to
the data”
Field (2017)
30
Model Sum of Squares
SSM
The sum of the squares
of the differences
between the mean
value of y and the
model
(Sometimes called
regression or explained
sum of squares)
“This difference shows
us the reduction in the
inaccuracy of the model
resulting from fitting
the regression model to
the data”
Field (2017)
31
Improvement
Squared
-10.46
109.32
-9.58
91.86
-8.71
75.92
-7.84
61.49
-6.97
48.59
-6.10
37.20
-5.23
27.33
-4.36
18.98
-3.49
12.15
-2.61
6.83
-1.74
3.04
-0.87
0.76
0.00
0.00
0.87
0.76
1.74
3.04
2.61
6.83
3.49
12.15
4.36
18.98
5.23
27.33
6.10
37.20
6.97
48.59
7.84
61.49
8.71
75.92
9.58
91.86
10.46
109.32
sum =
986.93
25
𝑆𝑆𝑀𝑜𝑑𝑒𝑙 = ∑ 𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑖2 = 986.93
𝑖=1
32
Goodness of fit – 𝑅
2
• 𝑅2 – the coefficient of determination
𝑅
2
𝑆𝑆𝑀𝑜𝑑𝑒𝑙
𝑆𝑆𝑀
2
=
=𝑅 =
𝑆𝑆𝑇𝑜𝑡𝑎𝑙
𝑆𝑆𝑇
2
𝑆𝑆𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙
𝑆𝑆𝑅
2
=1−
=𝑅 =1−
𝑆𝑆𝑇𝑜𝑡𝑎𝑙
𝑆𝑆𝑇
or 𝑅
• 𝑅2 tells you the proportion of variance explained by the
model.
• Multiply by 100 to get the percentage
• In a simple linear regression this is the correlation r squared
33
Goodness of fit – 𝑅
2
• 𝑅2 – the coefficient of determination
𝑅
2
𝑆𝑆𝑀𝑜𝑑𝑒𝑙
𝑆𝑆𝑀
986.93
2
=
=𝑅 =
=
= 0.492
𝑆𝑆𝑇𝑜𝑡𝑎𝑙
𝑆𝑆𝑇
2004.5
𝑆𝑆
1017.57
or 𝑅2 = 1 − 𝑆𝑆𝑆𝑆𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙
=
𝑅2 = 1 − 𝑅 = 1 −
= 1 − 0.508 = 0.492
𝑆𝑆𝑇
2004.5
𝑇𝑜𝑡𝑎𝑙
• 𝑅2 tells you the proportion of variance explained by the
model.
• Multiply by 100 to get the percentage
• In a simple linear regression this is the correlation r squared
34
Goodness of fit – R2
• How do we know if this is a significant difference
• Do an F-test
• Calculate the Mean Squares to compute
• One for each of SSModel and SSResiduals
• Divide them by the degrees of freedom
• F has a probability distribution we can use to get a p-value
Goodness of fit – R2
• How do we know if this is a significant difference
• Do an F-test
• Calculate the Mean Squares to compute
• One for each of SSModel and SSResiduals
• Divide them by the degrees of freedom
• F has a probability distribution we can use to get a p-value
Improvement
Step
-10.46
-9.58
0.87
-8.71
0.87
-7.84
0.87
-6.97
0.87
-6.10
0.87
-5.23
0.87
-4.36
0.87
-3.49
0.87
-2.61
0.87
-1.74
0.87
-0.87
0.87
0.00
0.87
0.87
0.87
1.74
0.87
2.61
0.87
3.49
0.87
4.36
0.87
5.23
0.87
6.10
0.87
6.97
0.87
7.84
0.87
8.71
Coefficients
𝑦𝑖 = (𝑏0 + 𝑏1 𝑥𝑖 ) + 𝜖𝑖
𝑦𝑖 = (𝑏0 + 𝑏1 𝑥𝑖 ) + 𝜖𝑖
0.87
𝑦5 = (𝑏0 + 𝑏1 𝑥5 ) + 𝜖5
𝑦7 = (𝑏0 + 𝑏1 𝑥7 ) + 𝜖7
9.58
0.87
𝑦7 = 45.053 + (.87 × 7) + −12.65 = 38.493
𝑦5 = 45.053 + (.87 × 5) + −4.31 = 45.013
10.46
0.87
37
Coefficient
• We interpret the coefficient as:
For every increase of 1 unit in the x variable
y increases by the coefficient
• In our model for every 1 increase in Time, Skill increase by 0.87
• If we continue our line to point where it crosses the y axis that
is the intercept, here 45.053 (b0 or c)
• The model part is equal to:
𝑦
𝑆𝑘𝑖𝑙𝑙
= 𝑚𝑥 + 𝑐
= 0.87 × 𝑇𝑖𝑚𝑒 + 45.053
• Add on the error/residuals to get back to our observed data
38
Hypothesis testing with linear regression
Testing individual predictors
0 = no relationship is very important again this is the null hypothesis for
each predictor
is each individual predictor a flat line = 0
or is it significantly different from 0
Tested using the t-statistic – how big b1 is compared to the standard error
𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑏𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑
𝑡=
=
𝑆𝐸𝑏
𝑆𝐸𝑏
𝑏𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 is 0 as the expected here is the null hypothesis and it drops out of the equation
Field (2017)
39
Hypothesis testing with linear regression
Testing individual predictors
0 = no relationship is very important again this is the null hypothesis for
each predictor
is each individual predictor a flat line = 0
or is it significantly different from 0
Tested using the t-statistic – how big b1 is compared to the standard error
𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑏𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 .871
𝑡=
=
=
= 4.723
𝑆𝐸𝑏
𝑆𝐸𝑏
.184
𝑏𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 is 0 as the expected here is the null hypothesis and it drops out of the equation
Field (2017)
40
Example
• Let’s see what this looks like for the data in SPSS
• PSY7065_LinearRegression_example1.sav
Key basic outputs
• ANOVA table
• F-value/p-value/dfs for null-hypothesis significance testing
• Basically: does the full regression model account for more variance than expected of
a model with regression predictor coefficients of 0 (i.e., DV = intercept + b1*IV versus
DV = intercept + 0*IV)
• Coefficients table
• Regression coefficients (B), their standard error, standardized coefficients, and
associated t-value and p-value
• t-value/p-value? Significance test for model DV = intercept + b1*IV versus H0 (→ DV =
intercept + 0*IV)
41
Example
• PSY7065_LinearRegression_example1.sav
Example
• PSY7065_LinearRegression_example1.sav
Example
• PSY7065_LinearRegression_example1.sav
𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑏𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑏𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 .871
𝑡=
=
=
= 4.723
𝑆𝐸𝑏
𝑆𝐸𝑏
.184
There is a rounding error in this one strictly speaking it is 0.871308/0.184479 = 4.723074
44
Example
• PSY7065_LinearRegression_example1.sav
Design Matrix
Design Matrix
It is all Matrix Algebra – properly Linear Algebra
Example
• Try: PSY7065_LinearRegression_example2.sav
48
Multiple linear regression
• Same principles, but now with more than one
IV/predictor variable
Design Matrix Multiple Regression
Overview
• Multiple linear regression (the basics)
• Multiple linear regression (advanced)
• SPSS practice
51
Bias and Assumptions
The linear model has assumptions and we want to avoid
bias
• As all the statistical techniques are based on the linear
model the assumptions are the same across the
techniques
• Benefit of learning the linear model route
• Simple & Multiple linear regression are appropriate for
Outcome/DV: Continuous — interval ratio
Predictor(s)/IV: Continuous or Nominal
• but more than two categories can be dummy coded
as multiple predictors
52
Residuals
Squared
4.68
21.86
4.70
22.13
23.30
542.89
0.86
0.74
-4.31
18.57
1.02
1.04
-12.65
160.08
-0.12
0.02
-5.69
32.43
6.03
36.41
-8.44
71.19
-9.81
96.21
1.22
1.49
-3.35
11.23
-8.72
76.08
0.51
0.26
3.53
12.49
3.26
10.65
-2.21
4.87
-1.08
1.16
8.05
64.80
-6.92
47.91
7.61
57.87
4.04
16.29
2.06
4.26
sum =
1312.93
Outliers
• As with the mean outliers are a problem.
• But much more so as we use the sum of
squares
• Extreme values carry more weight
53
No extreme outliers
Outlier by
distance:
regression line
not greatly
affected
Outlier by
influence:
regression line
affected
54
Outliers
• What to do?
• In general, we can use histograms and box plots to spot
outliers
• In regression
– We can create standardised residuals and remove
those over 3.29 or under -3.29 (Field suggests 3)
– Remove influential cases
• Adjusted predicted value – leave one out
• Cook’s Distance (>1 is a concern)
• Leverage (investigate case with more than twice
the average leverage value)
55
Linear and Additive
• Relationship between DV and IV should be linear (a
straight line), not curvilinear (e.g. quadratic, sinusoid) or
some other wiggly non-linear function.
• There may be no relationship, but if there is one we are
assuming it will be a straight line.
• Linear relationship can be checked through visual
inspection of scatter plots
• We can use transformations to bring data back into shape
• Additive means that different predictor’s effect can be
combined by adding, The + in the linear model. They do
not interact (although we can extend to add interactions).
56
Independent Residuals
• There should be no correlation between residuals
• Assumption means that there is no correlation between any of
residuals – they are random
𝜖 ≈ 𝑖. 𝑖. 𝑑. 𝑁(0, 𝜎 2 )
• i.i.d. means every residual is independent and identically distributed
• identically distributed means there is equal likelihood they will be
drawn in a sample from the population distribution
• 𝑁(0, 𝜎 2 ) means normally distributed
• Typically arises with time (time series) or, with consecutive residuals
being related
• Also important in spatial (geographic data)
– imagine two postcodes or longitude and latitudes positions
• Can arise elsewhere
• Tested with the Durbin–Watson test
57
Normally Distributed Residuals
𝜖 ≈ 𝑖. 𝑖. 𝑑. 𝑁(0, 𝜎 2 )
• 𝑁(0, 𝜎 2 ) means normally distributed
• Often people make the mistake of thinking the data itself has to be
normally distributed — it is the residuals.
• The errors cancel out and are just noise and are mostly close to zero
with extreme instances being less common.
• Can be tested with histogram of the residuals.
• Formal tests of normality exist (Kolmogorov-Smirnov and ShapiroWilk)
58
Homoscedasticity vs
heteroscedasticity
Ideal line (population)
Data
Best fitting line (for data)
With a heteroscedasticity (here: error
increases as Time increases), estimate of
best fitting line becomes more variable.
This affects null-hypothesis significance
testing for both intercept and slope.
Tested by plotting Standardized
Residuals as a function of Standardized
Predicted Values (should not show
fanning in/out)
59
Homoscedasticity
Normal plot with residuals
Residuals plotted against the fitted values
Fanning out of the residuals evident in both plots
but clearer in the second
Solution use weighted least squares regression or robust regression
http://www.sthda.com/ 60
Little or no multicollinearity
Predictors should not be highly correlated
Correlation between predictors is called
multicollinearity
• Your predictors should not be strongly correlated
• Example:
Perfect correlation between b1 and b2
– 𝑦 = 𝑏0 + 𝑏1 × 𝑥1 + 𝑏2 × 𝑥2 = 𝑏0 + 𝑏1 × 𝑥2 + 𝑏2 × 𝑥1
– For any data point one cannot say whether its specific value is
due to x1 or x2: B1 and B2 cannot be uniquely determined
• Check VIF/Tolerance
• Solution: Ridge Regression
61
Hierarchical regression
• Determining whether an predictor should be included in
model
• Build your model from theory using hierarchical regression
– Enter known predictors in order of importance for
predicting the outcome
– After known predictors enter new ones (most important
first)
– Forced entry (Enter) includes them all at the same time
– Use model comparison techniques to let you know the
value of a model and the benefits of predictors.
62
Stepwise methods for models with
multiple predictors
• Determining whether an predictor should be included in model
• Avoid these. Build your model from theory using hierarchical
regression process rather than stepwise regression
– Forward: start with simplest model, without predictors
• add variable yielding the most significant improvement in model
• Rerun extended model
• Repeat
– Backward: start with full model
• exclude variables that least reduce model’s explanatory value
• Rerun reduced model
• Repeat
– Bidirectional
• Combination of above 2 methods
63
Reporting the Results
• APA 7 or APA 6 is fine
• APA 7 abbreviations
b, bi
in regression and multiple regression analyses, estimated values of raw (unstandardized) regression
coefficients
b*, bi*
estimated values of standardized regression coefficients in regression and multiple regression analyses
df
degrees of freedom
ES
effect size
F
F distribution
MS
mean square
r
estimate of the Pearson product–moment correlation coefficient
r2
coefficient of determination; measure of strength of relationship; estimate Pearson correlation squared
SE
standard error
SS
Sum of Squares
t
Student’s t distribution; a statistical test based on the Student t distribution; the sample value of the t-test
statistic
z
a standardized score; the value of a statistic divided by its standard error
64
Reporting the Results
• APA 7
• “The results of multiple regression, including
mediation and moderation analyses, can be presented
in a variety of ways depending on the purpose of the
table and need for detail. Clearly label the regression
type (e.g., hierarchical) and the type of regression
coefficients (raw or standardized) being reported.”
65
Reporting the Results
• 7.16 Confidence Intervals in Tables
• When a table includes point estimates—for example,
means, correlations, or regression slopes—it should also,
when possible, include confidence intervals. Report
confidence intervals in tables either by using square
brackets, as in the text (see Section 6.9) and in Table 7.16
in Section 7.21, or by giving lower and upper limits in
separate columns, as in Table 7.17 in Section 7.21. In every
table that includes confidence intervals, state the
confidence level (e.g., 95% or 99%). It is usually best to use
the same confidence level throughout a paper.
66
Reporting the Results
• Table 7.15 Sample Regression Table, Without Confidence Intervals
67
Reporting the Results
• Table 7.17 Sample Regression Table, With Confidence Intervals in Separate Columns
68
Reporting the Results
• Table 7.18 Sample Hierarchical Multiple Regression Table
69
Reporting the Results
SJPlot
in R
Papaja
in R
70
Reporting the Results
• Table 7.18 Sample Hierarchical Multiple Regression Table
APA 7
F ratios:
For immediate recognition, the omnibus test of the main effect of sentence format
was significant, F(2, 177) = 6.30, p = .002, est ω2 = .07.
t values:
The one-degree-of-freedom contrast of primary interest was significant at the
specified p < .05 level, t(177) = 3.51, p < .001, d = 0.65, 95% CI [0.35, 0.95]. Hierarchical and other sequential regression statistics: High school GPA predicted college mathematics performance, R2 = .12, F(1, 148) = 20.18, p < .001, 95% CI [.02, .22]. 71 PSY7065 Quantitative Data Analysis 1 ANOVA Coursework A researcher studied the effectiveness of a new virtual reality-based treatment for Agoraphobia (fear of open spaces). She asked people who suffered from this phobia to participate in her study. The researcher was interested in the direct effect of the treatment as well as its longer-term effects; she thus measured agoraphobia prior to (PreTest), directly after (PostTest) and 1 week after the virtual reality treatment (OneWeek). The degree of experienced agoraphobia was quantified using the average heart rate measured in a real-life test on the university main square (higher means more fear). Two variants of the virtual reality treatment were considered, and participants were divided into two groups to explore the effects of these variants: one involving participants standing in different types of open spaces (level called Standing) and one in which they were moving around in these spaces (level called Moving). In the data file 1 = Standing and 2 = Moving. The data for 20 participants is presented in the accompanying file PSY7065_ANOVA_Portfolio2022.sav 1) Conduct and report the appropriate analysis, including post-hoc analyses, after confirming that its assumptions have not been violated (i.e., also report evidence of the associated checks). • • • • • • • • The data has been created for assessment purposes; it may not reflect reality. In your submitted document, avoid simply cutting and pasting prolifically from SPSS output, choose your evidence to submit carefully. The only output that can be included concerns the data summary and/or the assumption checks (although, when possible, reporting statistical values within the text is preferred); for any figures, include appropriate captions. Submit this coursework together with the other two assignments in a single portfolio (i.e., a single file). This section should be word processed, written in full paragraphs, and should not be longer than 3 sides of A4. Report findings in the appropriate statistical format. Cite references to support your answers where relevant. This only concerns references about statistics, not about the fictive topic. Interpret and explain your results; display understanding of the statistical principles underlying the test. Do not copy out lecture notes or material from the internet! All coursework will be checked for plagiarism. PSY7065 Quantitative Data Analysis 1 Logistic Regression Coursework A researcher studied decision making in childhood using a variant of the marshmallow test. In the regular marshmallow test (Mischel & Ebbesen, 1970), children are given a marshmallow on a plate and promised two marshmallows in reward for not eating the marshmallow while the experimenter is out of the room (For an example, see: https://www.youtube.com/watch?v=QX_oy9614HQ.) The variant studied here concerned a shared reward. In this ‘Shared-Reward’ variant children were told beforehand that one of the two promised marshmallows had to be shared with a friend. They thus only got to keep one marshmallow. The researcher registered participants’ decisions (Wait=0 or Eat=1) in this scenario and also recorded several other factors thought to be relevant: • • • Gender (Male=0, Female=1) Self-control (0-100, measured through a validated questionnaire; higher means more self-control) Ability to have fun (0-30, measured through a questionnaire, completed by the child’s carers; higher means more ability to have fun). The data for 194 participants is presented in the accompanying file PSY7065_LogisticRegression_Portfolio2022.sav. Conduct a logistic regression analyses to examine the relationship between decisions as a function Gender, Self-control, and Ability to have fun. Provide the motivation that you are allowed to conduct the analyses (i.e., ensure the assumptions are met) and conduct the analyses. Report information on the model fit, amount of variance explained, which individual variables act as significant predictors and the relationship between the individual variables and whether or not they increase the odds of the child deciding to eat the marshmallow. • • • • • • • The data has been created for assessment purposes; it may not reflect reality. In your submitted document, avoid simply cutting and pasting prolifically from SPSS output, choose your evidence to submit carefully. The only output that can be included concerns the data summary and/or the assumption checks (although, when possible, reporting statistical values within the text is preferred); for any figures, include appropriate captions. Submit this coursework together with the other two assignments in a single portfolio (i.e., a single file). This section should be word processed, written in full paragraphs, and should not be longer than 3 sides of A4. Report findings in the appropriate statistical format. Cite references to support your answers where relevant. This only concerns references about statistics, not about the fictive topic. Interpret and explain your results; display understanding of the statistical principles underlying the test. Do not copy out lecture notes or material from the internet! PSY7065 Quantitative Data Analysis 1 • All coursework will be checked for plagiarism. References Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality and Social Psychology, 16(2), 329–337. https://doi.org/10.1037/h0029815