Complete factorial designs

Session 5

MATH 80667A: Experimental Design and Statistical Methods
HEC Montréal

1 / 38

Outline

Factorial designs and interactions

Tests for two-way ANOVA

2 / 38

Factorial designs and interactions3 / 38

Complete factorial designs?

Factorial design
study with multiple factors (subgroups)

Complete
Gather observations for every subgroup

4 / 38

Motivating example

Response:
retention of information
two hours after reading a story

Population:
children aged four

experimental factor 1:
ending (happy or sad)

experimental factor 2:
complexity (easy, average or hard).

5 / 38

Setup of design

complexity	happy	sad
complicated
average
easy

6 / 38

These factors are crossed, meaning that you can have participants in each subconditions.

Efficiency of factorial design

Cast problem
as a series of one-way ANOVA
vs simultaneous estimation

Factorial designs requires
fewer overall observations

Can study interactions

7 / 38

To study each interaction (complexity, story book ending) we would need to make three group for each comparison in rows, and one in each column. So a total of 3 one-way ANOVA each with 2 groups and 2 one-way anova with 3 groups. The two-way ANOVA will lead to 6 groups instead of 12.

Interaction

Definition: when the effect of one factor
depends on the levels of another factor.

Effect together
$\neq$
sum of individual effects

8 / 38

Interaction or profile plot

Graphical display:
plot sample mean per category

with uncertainty measure
(1 std. error for mean
confidence interval, etc.)

9 / 38

Interaction plots and parallel lines

10 / 38

Interaction plots for 2 by 2 designs

11 / 38

Cell means for 2 by 2 designs

12 / 38

Line graph for example patterns for means for each of the possible kinds of general outcomes in a 2 by 2 design. Illustration adapted from Figure 10.2 of Crump, Navarro and Suzuki (2019) by Matthew Crump (CC BY-SA 4.0 license)

Example 1 : loans versus credit

Sharma, Tully, and Cryder (2021) Supplementary study 5 consists of a $2 \times 2$ between-subject ANOVA with factors

debt type (debttype), either "loan" or "credit"
purchase type, either discretionary or not (need)

No evidence of interaction

13 / 38

Example 2 - psychological distance

Maglio and Polman (2014) Study 1 uses a $4 \times 2$ between-subject ANOVA with factors

subway station, one of Spadina, St. George, Bloor-Yonge and Sherbourne
direction of travel, either east or west

Clear evidence of interaction (symmetry?)

14 / 38

Tests for two-way ANOVA15 / 38

Analysis of variance = regression

An analysis of variance model is simply a linear regression with categorical covariate(s).

Typically, the parametrization is chosen so that parameters reflect differences to the global mean (sum-to-zero parametrization).
The full model includes interactions between all combinations of factors
- one average for each subcategory
- one-way ANOVA!

16 / 38

Formulation of the two-way ANOVA

Two factors: $A$ (complexity) and $B$ (ending) with $n_{a} = 3$ and $n_{b} = 2$ levels, and their interaction.

Write the average response $Y_{i j r}$ of the $r$ th measurement in group $(a_{i}, b_{j})$ as $\begin{aligned} \underset{average response}{E (Y_{i j r})} = \underset{subgroup mean}{μ_{i j}} \end{aligned}$ where $Y_{i j r}$ are independent observations with a common std. deviation $σ$ .

We estimate $μ_{i j}$ by the sample mean of the subgroup $(i, j)$ , say ${\hat{μ}}_{i j}$ .
The fitted values are ${\hat{y}}_{i j r} = {\hat{μ}}_{i j}$ .

17 / 38

One average for each subgroup

BB ending
 AA complexity 
b1b1 (happy)
b2b2 (sad)
row mean


a1a1 (complicated)
μ11μ11
μ12μ12
μ1.μ1.

a2a2 (average)
μ21μ21
μ22μ22
μ2.μ2.

a3a3 (easy)
μ31μ31
μ32μ32
μ3.μ3.

column mean
μ.1μ.1
μ.2μ.2
μμ

18 / 38

$B$ `ending` $A$ `complexity`	$b_{1}$ (`happy`)	$b_{2}$ (`sad`)	row mean
$a_{1}$ (`complicated`)	$μ_{11}$	$μ_{12}$	$μ_{1.}$
$a_{2}$ (`average`)	$μ_{21}$	$μ_{22}$	$μ_{2.}$
$a_{3}$ (`easy`)	$μ_{31}$	$μ_{32}$	$μ_{3.}$
column mean	$μ_{.1}$	$μ_{.2}$	$μ$

Row, column and overall average

Mean of $A_{i}$ (average of row $i$ ): $μ_{i .} = \frac{μ_{i 1} + \dots + μ_{i n_{b}}}{n_{b}}$
Mean of $B_{j}$ (average of column $j$ ): $μ_{. j} = \frac{μ_{1 j} + \dots + μ_{n_{a} j}}{n_{a}}$

Overall average: $μ = \frac{\sum_{i = 1}^{n_{a}} \sum_{j = 1}^{n_{b}} μ_{i j}}{n_{a} n_{b}}$

Row, column and overall averages are equiweighted combinations of the cell means $μ_{i j}$ .
Estimates are obtained by replacing $μ_{i j}$ in formulas by subgroup sample mean.

19 / 38

Vocabulary of effectssimple effects: difference between levels of one in a fixed combination of others (change in difficulty for happy ending)
main effects: differences relative to average for each condition of a factor (happy vs sad ending)
interaction effects: when simple effects differ depending on levels of another factor
20 / 38

Main effects

Main effects are comparisons between row or column averages

Obtained by marginalization, i.e., averaging over the other dimension.

Main effects are not of interest if there is an interaction.

	happy	sad
column means	$μ_{.1}$	$μ_{.2}$

complexity	row means
complicated	$μ_{1.}$
average	$μ_{2.}$
easy	$μ_{3.}$

21 / 38

Simple effects

Simple effects are comparisons between cell averages within a given row or column

	happy	sad
means (easy)	$μ_{13}$	$μ_{23}$

complexity	mean (happy)
complicated	$μ_{11}$
average	$μ_{21}$
easy	$μ_{31}$

22 / 38

Contrasts

We collapse categories to obtain a one-way ANOVA with categories $A$ (complexity) and $B$ (ending).

Q: How would you write the weights for contrasts for testing the

main effect of $A$ : complicated vs average, or complicated vs easy.
main effect of $B$ : happy vs sad.
interaction $A$ and $B$ : difference between complicated and average, for happy versus sad?

The order of the categories is $(a_{1}, b_{1})$ , $(a_{1}, b_{2})$ , $\dots$ , $(a_{3}, b_{2})$ .

23 / 38

Contrasts

Suppose the order of the coefficients is factor $A$ (complexity, 3 levels, complicated/average/easy) and factor $B$ (ending, 2 levels, happy/sad).

test	$μ_{11}$	$μ_{12}$	$μ_{21}$	$μ_{22}$	$μ_{31}$	$μ_{32}$
main effect $A$ (complicated vs average)	$1$	$1$	$- 1$	$- 1$	$0$	$0$
main effect $A$ (complicated vs easy)	$1$	$1$	$0$	$0$	$- 1$	$- 1$
main effect $B$ (happy vs sad)	$1$	$- 1$	$1$	$- 1$	$1$	$- 1$
interaction $A B$ (comp. vs av, happy vs sad)	$1$	$- 1$	$- 1$	$1$	$0$	$0$
interaction $A B$ (comp. vs easy, happy vs sad)	$1$	$- 1$	$0$	$0$	$- 1$	$1$

24 / 38

Global hypothesis tests

Main effect of factor $A$

$H_{0}$ : $μ_{1.} = \dots = μ_{n_{a} .}$ vs $H_{a}$ : at least two marginal means of $A$ are different

Main effect of factor $B$

$H_{0}$ : $μ_{.1} = \dots = μ_{. n_{b}}$ vs $H_{a}$ : at least two marginal means of $B$ are different

Interaction

$H_{0}$ : $μ_{i j} = μ_{i \cdot} + μ_{\cdot j}$ (sum of main effects) vs $H_{a}$ : effect is not a combination of row/column effect.

25 / 38

Comparing nested models

Rather than present the specifics of ANOVA, we consider a general hypothesis testing framework which is more widely applicable.

We compare two competing models, $M_{a}$ and $M_{0}$ .

the alternative or full model $M_{a}$ under the alternative $H_{a}$ with $p$ parameters for the mean
the simpler null model $M_{0}$ under the null $H_{0}$ , which imposes $ν$ restrictions on the full model

26 / 38

The same holds for analysis of deviance for generalized linear models. The latter use likelihood ratio tests (which are equivalent to F-tests for linear models), with a $χ_{ν}^{2}$ null distribution.

Intuition behind $F$ -test for ANOVA

The residual sum of squares measures how much variability is leftover, ${R S S}_{a} = \sum_{i = 1}^{n} {(y_{i} - {\hat{y}}_{i}^{M_{a}})}^{2}$ where ${\hat{y}}_{i}$ is the estimated mean under model $M_{a}$ for the observation $y_{i}$ .

The more complex fits better (it is necessarily more flexible), but requires estimation of more parameters.

We wish to assess the improvement that would occur by chance, if the null model was correct.

27 / 38

Testing linear restrictions in linear models

If the alternative model has $p$ parameters for the mean, and we impose $ν$ linear restrictions under the null hypothesis to the model estimated based on $n$ independent observations, the test statistic is

$\begin{aligned} F = \frac{({R S S}_{0} - {R S S}_{a}) / ν}{{R S S}_{a} / (n - p)} \end{aligned}$

The numerator is the difference in residuals sum of squares, denoted $R S S$ , from models fitted under $H_{0}$ and $H_{a}$ , divided by degrees of freedom $ν$ .
The denominator is an estimator of the variance, obtained under $H_{a}$ (termed mean squared error of residuals)
The benchmark for tests in linear models is Fisher's $F (ν, n - p)$ .

28 / 38

Analysis of variance table

term	degrees of freedom	mean square	$F$
$A$	$n_{a} - 1$	${M S}_{A} = {S S}_{A} / (n_{a} - 1)$	${M S}_{A} / {M S}_{res}$
$B$	$n_{b} - 1$	${M S}_{B} = {S S}_{B} / (n_{b} - 1)$	${M S}_{B} / {M S}_{res}$
$A B$	$(n_{a} - 1) (n_{b} - 1)$	${M S}_{A B} = {S S}_{A B} / {(n_{a} - 1) (n_{b} - 1)}$	${M S}_{A B} / {M S}_{res}$
residuals	$n - n_{a} n_{b}$	${M S}_{resid} = {R S S}_{a} / (n - n_{a} n_{b})$
total	$n - 1$

Read the table backward (starting with the interaction).

If there is a significant interaction, the main effects are not of interest and potentially misleading.

29 / 38

Intuition behind degrees of freedom

The model always includes an overall average $μ$ . There are

$n_{a} - 1$ free row means since $n_{a} μ = μ_{1.} + \dots + μ_{n_{a} .}$
$n_{b} - 1$ free column means as $n_{b} μ = μ_{.1} + \dots + μ_{. n_{b}}$
$n_{a} n_{b} - (n_{a} - 1) - (n_{b} - 1) - 1$ interaction terms

$B$ `ending` $A$ `complexity`	$b_{1}$ (`happy`)	$b_{2}$ (`sad`)	row mean
$a_{1}$ (`complicated`)	$μ_{11}$	$X$	$μ_{1.}$
$a_{2}$ (`average`)	$μ_{21}$	$X$	$μ_{2.}$
$a_{3}$ (`easy`)	$X$	$X$	$X$
column mean	$μ_{.1}$	$X$	$μ$

Terms with $X$ are fully determined by row/column/total averages

30 / 38

Example 1

The interaction plot suggested that the two-way interaction wasn't significant. The $F$ test confirms this.

There is a significant main effect of both purchase and debttype.

term	SS	df	F stat	p-value
purchase	752.3	1	98.21	< .001
debttype	92.2	1	12.04	< .001
purchase:debttype	13.7	1	1.79	.182
Residuals	11467.4	1497

31 / 38

Example 2

There is a significant interaction between station and direction, so follow-up by looking at simple effects or contrasts.

The tests for the main effects are not of interest! Disregard other entries of the ANOVA table

term	SS	df	F stat	p-value
station	75.2	3	23.35	< .001
direction	0.4	1	0.38	.541
station:direction	52.4	3	16.28	< .001
Residuals	208.2	194

32 / 38

Main effects for Example 1

We consider differences between debt type labels.

Participants are more likely to consider the offer if it is branded as credit than loan. The difference is roughly 0.5 (on a Likert scale from 1 to 9).

## $emmeans
##  debttype emmean    SE   df lower.CL upper.CL
##  credit     5.12 0.101 1497     4.93     5.32
##  loan       4.63 0.101 1497     4.43     4.83
## 
## Results are averaged over the levels of: purchase 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast      estimate    SE   df t.ratio p.value
##  credit - loan    0.496 0.143 1497   3.469  0.0005
## 
## Results are averaged over the levels of: purchase

33 / 38

Toronto subway station

Simplified depiction of the Toronto metro stations used in the experiment, based on work by Craftwerker on Wikipedia, distributed under CC-BY-SA 4.0.

34 / 38

Reparametrization for Example 2

Set stdist as $- 2$ , $- 1$ , $+ 1$ , $+ 2$ to indicate station distance, with negative signs indicating stations in opposite direction of travel

The ANOVA table for the reparametrized models shows no evidence against the null of symmetry (interaction).

term	SS	df	F stat	p-value
stdist	121.9	3	37.86	< .001
direction	0.4	1	0.35	.554
stdist:direction	5.7	3	1.77	.154
Residuals	208.2	194

35 / 38

Interaction plot for reformated data

36 / 38

Custom contrasts for Example 2

We are interested in testing the perception of distance, by looking at $H_{0} : μ_{- 1} = μ_{+ 1}, μ_{- 2} = μ_{+ 2}$ .

mod3 <- lm(distance ~ stdist * direction, data = MP14_S1) 
(emm <- emmeans(mod3, specs = "stdist"))
  # order is -2, -1, 1, 2
contrasts <- emm |> contrast(
  list("two dist" = c(-1, 0, 0, 1), 
        "one dist" = c(0, -1, 1, 0)))
contrasts # print pairwise contrasts
test(contrasts, joint = TRUE)

37 / 38

Estimated marginal means and contrasts

Strong evidence of differences in perceived distance depending on orientation.

##  stdist emmean    SE  df lower.CL upper.CL
##  -2       3.83 0.145 194     3.54     4.11
##  -1       2.48 0.144 194     2.20     2.76
##  +1       1.62 0.150 194     1.33     1.92
##  +2       2.70 0.145 194     2.42     2.99
## 
## Results are averaged over the levels of: direction 
## Confidence level used: 0.95

##  contrast estimate    SE  df t.ratio p.value
##  two dist   -1.122 0.205 194  -5.470  <.0001
##  one dist   -0.856 0.207 194  -4.129  0.0001
## 
## Results are averaged over the levels of: direction

##  df1 df2 F.ratio p.value
##    2 194  23.485  <.0001

38 / 38

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Tile View: Overview of Slides

Complete factorial designs

Session 5

MATH 80667A: Experimental Design and Statistical Methods
HEC Montréal

1 / 38

Outline

Factorial designs and interactions

Tests for two-way ANOVA

2 / 38

Factorial designs and interactions3 / 38

Complete factorial designs?

Factorial design
study with multiple factors (subgroups)

Complete
Gather observations for every subgroup

4 / 38

Motivating example

Response:
retention of information
two hours after reading a story

Population:
children aged four

experimental factor 1:
ending (happy or sad)

experimental factor 2:
complexity (easy, average or hard).

5 / 38

Setup of design

complexity	happy	sad
complicated
average
easy

6 / 38

These factors are crossed, meaning that you can have participants in each subconditions.

Efficiency of factorial design

Cast problem
as a series of one-way ANOVA
vs simultaneous estimation

Factorial designs requires
fewer overall observations

Can study interactions

7 / 38

Interaction

Definition: when the effect of one factor
depends on the levels of another factor.

Effect together
$\neq$
sum of individual effects

8 / 38

Interaction or profile plot

Graphical display:
plot sample mean per category

with uncertainty measure
(1 std. error for mean
confidence interval, etc.)

9 / 38

Interaction plots and parallel lines

10 / 38

Interaction plots for 2 by 2 designs

11 / 38

Cell means for 2 by 2 designs

12 / 38

Example 1 : loans versus credit

Sharma, Tully, and Cryder (2021) Supplementary study 5 consists of a $2 \times 2$ between-subject ANOVA with factors

debt type (debttype), either "loan" or "credit"
purchase type, either discretionary or not (need)

No evidence of interaction

13 / 38

Example 2 - psychological distance

Maglio and Polman (2014) Study 1 uses a $4 \times 2$ between-subject ANOVA with factors

subway station, one of Spadina, St. George, Bloor-Yonge and Sherbourne
direction of travel, either east or west

Clear evidence of interaction (symmetry?)

14 / 38

Tests for two-way ANOVA15 / 38

Analysis of variance = regression

An analysis of variance model is simply a linear regression with categorical covariate(s).

Typically, the parametrization is chosen so that parameters reflect differences to the global mean (sum-to-zero parametrization).
The full model includes interactions between all combinations of factors
- one average for each subcategory
- one-way ANOVA!

16 / 38

Formulation of the two-way ANOVA

Two factors: $A$ (complexity) and $B$ (ending) with $n_{a} = 3$ and $n_{b} = 2$ levels, and their interaction.

We estimate $μ_{i j}$ by the sample mean of the subgroup $(i, j)$ , say ${\hat{μ}}_{i j}$ .
The fitted values are ${\hat{y}}_{i j r} = {\hat{μ}}_{i j}$ .

17 / 38

One average for each subgroup

BB ending
 AA complexity 
b1b1 (happy)
b2b2 (sad)
row mean


a1a1 (complicated)
μ11μ11
μ12μ12
μ1.μ1.

a2a2 (average)
μ21μ21
μ22μ22
μ2.μ2.

a3a3 (easy)
μ31μ31
μ32μ32
μ3.μ3.

column mean
μ.1μ.1
μ.2μ.2
μμ

18 / 38

$B$ `ending` $A$ `complexity`	$b_{1}$ (`happy`)	$b_{2}$ (`sad`)	row mean
$a_{1}$ (`complicated`)	$μ_{11}$	$μ_{12}$	$μ_{1.}$
$a_{2}$ (`average`)	$μ_{21}$	$μ_{22}$	$μ_{2.}$
$a_{3}$ (`easy`)	$μ_{31}$	$μ_{32}$	$μ_{3.}$
column mean	$μ_{.1}$	$μ_{.2}$	$μ$

Row, column and overall average

Mean of $A_{i}$ (average of row $i$ ): $μ_{i .} = \frac{μ_{i 1} + \dots + μ_{i n_{b}}}{n_{b}}$
Mean of $B_{j}$ (average of column $j$ ): $μ_{. j} = \frac{μ_{1 j} + \dots + μ_{n_{a} j}}{n_{a}}$

Overall average: $μ = \frac{\sum_{i = 1}^{n_{a}} \sum_{j = 1}^{n_{b}} μ_{i j}}{n_{a} n_{b}}$

Row, column and overall averages are equiweighted combinations of the cell means $μ_{i j}$ .
Estimates are obtained by replacing $μ_{i j}$ in formulas by subgroup sample mean.

19 / 38

Vocabulary of effectssimple effects: difference between levels of one in a fixed combination of others (change in difficulty for happy ending)
main effects: differences relative to average for each condition of a factor (happy vs sad ending)
interaction effects: when simple effects differ depending on levels of another factor
20 / 38

Main effects

Main effects are comparisons between row or column averages

Obtained by marginalization, i.e., averaging over the other dimension.

Main effects are not of interest if there is an interaction.

	happy	sad
column means	$μ_{.1}$	$μ_{.2}$

complexity	row means
complicated	$μ_{1.}$
average	$μ_{2.}$
easy	$μ_{3.}$

21 / 38

Simple effects

Simple effects are comparisons between cell averages within a given row or column

	happy	sad
means (easy)	$μ_{13}$	$μ_{23}$

complexity	mean (happy)
complicated	$μ_{11}$
average	$μ_{21}$
easy	$μ_{31}$

22 / 38

Contrasts

We collapse categories to obtain a one-way ANOVA with categories $A$ (complexity) and $B$ (ending).

Q: How would you write the weights for contrasts for testing the

main effect of $A$ : complicated vs average, or complicated vs easy.
main effect of $B$ : happy vs sad.
interaction $A$ and $B$ : difference between complicated and average, for happy versus sad?

The order of the categories is $(a_{1}, b_{1})$ , $(a_{1}, b_{2})$ , $\dots$ , $(a_{3}, b_{2})$ .

23 / 38

Contrasts

Suppose the order of the coefficients is factor $A$ (complexity, 3 levels, complicated/average/easy) and factor $B$ (ending, 2 levels, happy/sad).

test	$μ_{11}$	$μ_{12}$	$μ_{21}$	$μ_{22}$	$μ_{31}$	$μ_{32}$
main effect $A$ (complicated vs average)	$1$	$1$	$- 1$	$- 1$	$0$	$0$
main effect $A$ (complicated vs easy)	$1$	$1$	$0$	$0$	$- 1$	$- 1$
main effect $B$ (happy vs sad)	$1$	$- 1$	$1$	$- 1$	$1$	$- 1$
interaction $A B$ (comp. vs av, happy vs sad)	$1$	$- 1$	$- 1$	$1$	$0$	$0$
interaction $A B$ (comp. vs easy, happy vs sad)	$1$	$- 1$	$0$	$0$	$- 1$	$1$

24 / 38

Global hypothesis tests

Main effect of factor $A$

$H_{0}$ : $μ_{1.} = \dots = μ_{n_{a} .}$ vs $H_{a}$ : at least two marginal means of $A$ are different

Main effect of factor $B$

$H_{0}$ : $μ_{.1} = \dots = μ_{. n_{b}}$ vs $H_{a}$ : at least two marginal means of $B$ are different

Interaction

$H_{0}$ : $μ_{i j} = μ_{i \cdot} + μ_{\cdot j}$ (sum of main effects) vs $H_{a}$ : effect is not a combination of row/column effect.

25 / 38

Comparing nested models

Rather than present the specifics of ANOVA, we consider a general hypothesis testing framework which is more widely applicable.

We compare two competing models, $M_{a}$ and $M_{0}$ .

the alternative or full model $M_{a}$ under the alternative $H_{a}$ with $p$ parameters for the mean
the simpler null model $M_{0}$ under the null $H_{0}$ , which imposes $ν$ restrictions on the full model

26 / 38

Intuition behind $F$ -test for ANOVA

The more complex fits better (it is necessarily more flexible), but requires estimation of more parameters.

We wish to assess the improvement that would occur by chance, if the null model was correct.

27 / 38

Testing linear restrictions in linear models

$\begin{aligned} F = \frac{({R S S}_{0} - {R S S}_{a}) / ν}{{R S S}_{a} / (n - p)} \end{aligned}$

The numerator is the difference in residuals sum of squares, denoted $R S S$ , from models fitted under $H_{0}$ and $H_{a}$ , divided by degrees of freedom $ν$ .
The denominator is an estimator of the variance, obtained under $H_{a}$ (termed mean squared error of residuals)
The benchmark for tests in linear models is Fisher's $F (ν, n - p)$ .

28 / 38

Analysis of variance table

term	degrees of freedom	mean square	$F$
$A$	$n_{a} - 1$	${M S}_{A} = {S S}_{A} / (n_{a} - 1)$	${M S}_{A} / {M S}_{res}$
$B$	$n_{b} - 1$	${M S}_{B} = {S S}_{B} / (n_{b} - 1)$	${M S}_{B} / {M S}_{res}$
$A B$	$(n_{a} - 1) (n_{b} - 1)$	${M S}_{A B} = {S S}_{A B} / {(n_{a} - 1) (n_{b} - 1)}$	${M S}_{A B} / {M S}_{res}$
residuals	$n - n_{a} n_{b}$	${M S}_{resid} = {R S S}_{a} / (n - n_{a} n_{b})$
total	$n - 1$

Read the table backward (starting with the interaction).

If there is a significant interaction, the main effects are not of interest and potentially misleading.

29 / 38

Intuition behind degrees of freedom

The model always includes an overall average $μ$ . There are

$n_{a} - 1$ free row means since $n_{a} μ = μ_{1.} + \dots + μ_{n_{a} .}$
$n_{b} - 1$ free column means as $n_{b} μ = μ_{.1} + \dots + μ_{. n_{b}}$
$n_{a} n_{b} - (n_{a} - 1) - (n_{b} - 1) - 1$ interaction terms

$B$ `ending` $A$ `complexity`	$b_{1}$ (`happy`)	$b_{2}$ (`sad`)	row mean
$a_{1}$ (`complicated`)	$μ_{11}$	$X$	$μ_{1.}$
$a_{2}$ (`average`)	$μ_{21}$	$X$	$μ_{2.}$
$a_{3}$ (`easy`)	$X$	$X$	$X$
column mean	$μ_{.1}$	$X$	$μ$

Terms with $X$ are fully determined by row/column/total averages

30 / 38

Example 1

The interaction plot suggested that the two-way interaction wasn't significant. The $F$ test confirms this.

There is a significant main effect of both purchase and debttype.

term	SS	df	F stat	p-value
purchase	752.3	1	98.21	< .001
debttype	92.2	1	12.04	< .001
purchase:debttype	13.7	1	1.79	.182
Residuals	11467.4	1497

31 / 38

Example 2

There is a significant interaction between station and direction, so follow-up by looking at simple effects or contrasts.

The tests for the main effects are not of interest! Disregard other entries of the ANOVA table

term	SS	df	F stat	p-value
station	75.2	3	23.35	< .001
direction	0.4	1	0.38	.541
station:direction	52.4	3	16.28	< .001
Residuals	208.2	194

32 / 38

Main effects for Example 1

We consider differences between debt type labels.

Participants are more likely to consider the offer if it is branded as credit than loan. The difference is roughly 0.5 (on a Likert scale from 1 to 9).

## $emmeans
##  debttype emmean    SE   df lower.CL upper.CL
##  credit     5.12 0.101 1497     4.93     5.32
##  loan       4.63 0.101 1497     4.43     4.83
## 
## Results are averaged over the levels of: purchase 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast      estimate    SE   df t.ratio p.value
##  credit - loan    0.496 0.143 1497   3.469  0.0005
## 
## Results are averaged over the levels of: purchase

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Toronto subway station

Simplified depiction of the Toronto metro stations used in the experiment, based on work by Craftwerker on Wikipedia, distributed under CC-BY-SA 4.0.

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Reparametrization for Example 2

Set stdist as $- 2$ , $- 1$ , $+ 1$ , $+ 2$ to indicate station distance, with negative signs indicating stations in opposite direction of travel

The ANOVA table for the reparametrized models shows no evidence against the null of symmetry (interaction).

term	SS	df	F stat	p-value
stdist	121.9	3	37.86	< .001
direction	0.4	1	0.35	.554
stdist:direction	5.7	3	1.77	.154
Residuals	208.2	194

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Interaction plot for reformated data

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Custom contrasts for Example 2

We are interested in testing the perception of distance, by looking at $H_{0} : μ_{- 1} = μ_{+ 1}, μ_{- 2} = μ_{+ 2}$ .

mod3 <- lm(distance ~ stdist * direction, data = MP14_S1) 
(emm <- emmeans(mod3, specs = "stdist"))
  # order is -2, -1, 1, 2
contrasts <- emm |> contrast(
  list("two dist" = c(-1, 0, 0, 1), 
        "one dist" = c(0, -1, 1, 0)))
contrasts # print pairwise contrasts
test(contrasts, joint = TRUE)

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Estimated marginal means and contrasts

Strong evidence of differences in perceived distance depending on orientation.

##  stdist emmean    SE  df lower.CL upper.CL
##  -2       3.83 0.145 194     3.54     4.11
##  -1       2.48 0.144 194     2.20     2.76
##  +1       1.62 0.150 194     1.33     1.92
##  +2       2.70 0.145 194     2.42     2.99
## 
## Results are averaged over the levels of: direction 
## Confidence level used: 0.95

##  contrast estimate    SE  df t.ratio p.value
##  two dist   -1.122 0.205 194  -5.470  <.0001
##  one dist   -0.856 0.207 194  -4.129  0.0001
## 
## Results are averaged over the levels of: direction

##  df1 df2 F.ratio p.value
##    2 194  23.485  <.0001

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Complete factorial designs

Outline

Factorial designs and interactions

Complete factorial designs?

Motivating example

Setup of design

Efficiency of factorial design

Interaction

Interaction or profile plot

Interaction plots and parallel lines

Interaction plots for 2 by 2 designs

Cell means for 2 by 2 designs

Example 1 : loans versus credit

Example 2 - psychological distance

Tests for two-way ANOVA

Analysis of variance = regression

Formulation of the two-way ANOVA

One average for each subgroup

Row, column and overall average

Vocabulary of effects

Main effects

Simple effects

Contrasts

Contrasts

Global hypothesis tests

Comparing nested models

Intuition behind FF-test for ANOVA

Testing linear restrictions in linear models

Analysis of variance table

Intuition behind degrees of freedom

Example 1

Example 2

Main effects for Example 1

Toronto subway station

Reparametrization for Example 2

Interaction plot for reformated data

Custom contrasts for Example 2

Estimated marginal means and contrasts

Outline

Help

Complete factorial designs

Complete factorial designs

Outline

Factorial designs and interactions

Complete factorial designs?

Motivating example

Setup of design

Efficiency of factorial design

Interaction

Interaction or profile plot

Interaction plots and parallel lines

Interaction plots for 2 by 2 designs

Cell means for 2 by 2 designs

Example 1 : loans versus credit

Example 2 - psychological distance

Tests for two-way ANOVA

Analysis of variance = regression

Formulation of the two-way ANOVA

One average for each subgroup

Row, column and overall average

Vocabulary of effects

Main effects

Simple effects

Contrasts

Contrasts

Global hypothesis tests

Comparing nested models

Intuition behind FF-test for ANOVA

Testing linear restrictions in linear models

Analysis of variance table

Intuition behind degrees of freedom

Example 1

Example 2

Main effects for Example 1

Toronto subway station

Reparametrization for Example 2

Interaction plot for reformated data

Custom contrasts for Example 2

Estimated marginal means and contrasts

Intuition behind $F$ -test for ANOVA

Intuition behind $F$ -test for ANOVA