Transcript History of the English Language - uni

Research design
Variables
•
Independent variable
•
Dependent variable
•
Levels
•
Responses
Variables and conditions
Subjects are given two types of constructions and are
asked to decide whether the given sentence is
grammatical:
(1)
(2)
a.
b.
c.
a.
b.
c.
I gave him it. Construction 1
I gave her the book.
…
I gave to him it.
Construction 2
I gave to her the note you sent me.
…
Variables and conditions
IV (two conditions)
DV (forced choice task)
Construction 1
Construction 2
a. grammatical
b. ungrammatical
Variables and conditions
Subjects are asked to complete copular sentences with
a relative clause. The predicate nominals of the copular
clauses belong to three different semantic types: (1)
animate/human (2) inanimate/object (3) place.
(1)
a.
b.
c.
This is the man __
This is the ball __
This is the place __
Variables and conditions
IV
DV
1. This is the man __
2. This is the ball __
3. This is the place __
a. SUBJ relative clause
b. DO relative clause
c. IO relative clause
d. OBL relative clause
e. GEN relative clause
Variables and conditions
IV
DV
1. This is the man __
2. This is the thing __
3. This is the place __
a. SUBJ relative clause
b. DO relative clause
c. IO relative clause
d. OBL relative clause
e. GEN relative clause
1. I saw the man __
2. I saw the thing __
3. I saw the place __
Interaction
Condition 1
Condition 2
SUBJ
DO
IO
OBL
GEN
SUBJ
DO
IO
OBL
GEN
3.5
3.2
2.7
2.2
0.6
2.5
3.8
3.2
0.5
0.5
Types of data
•
Nominal/categorical data
•
Ordinal data
•
Interval data
Type of analysis
•
Correlational analysis
•
Difference test
Type of analysis
Correlational test
Difference test
Pearson‘s r
Kendall‘s tau
T-test
ANOVA
Confound variable
(1)
(2)
(3)
(4)
… the man who talked to Mary.
… the car that caused the accident.
… the man who Mary talked to.
… the car that Peter bought.
Confound variable
(1)
(2)
(3)
(4)
… the man who talked to Mary.
… the car that caused the accident.
… the man who Mary talked to.
… the car that Peter bought.
Confound variables
•
Control
•
Randomization
Sampling
•
Simple random sampling
•
Stratified random sampling
•
Systematic sampling
•
Cluster sampling
Related and independent design
•
Within subjects design – related design –
repeated measures design
•
Between subjects design – unrelated
design – independent design
Advantages of within subjects design
•
Reduction of inter-individual differences
•
Fewer subjects
Disdvantages of within subjects design
Responses to IVs/conditions can influence each
other:
•
Subjects recognize the purpose of the study.
•
Subjects get tired, frustrated, excited.
•
Subjects get habituated to the task.
Counterbalancing
Counterbalancing serves to eliminate the
ordering effect.
•
ABBA
•
AB - BA
Counterbalancing
1. ABC
2. ACB
3. BAC
4. CAB
5. BCA
6. CBA
Experimental design
A child language researcher wants to find out if the
meaning of the head of a relative clause influences the
interpretation of the acquisition of relative clauses in
early child speech. Specifically, he wants to know if
animate and inanimate head nouns affect children’s
interpretation of relative clauses. In this study, he
concentrates on the two most frequent types of relative
clauses in which the subject and object are relativized
(i.e. expressed by the relative pronoun).
Experimental design
(1)
(2)
(3)
(4)
Das ist der Mann, der das Mädchen gestern
gesehen hat.
Das ist der Mann, den das Mädchen gestern
gesehen hat.
Das ist der Ball, der das Mädchen am Kopf
getroffen hat.
Das ist der Ball, den das Mädchen mit dem Kopf
getroffen hat.
Experimental design
(1)
(2)
(3)
(4)
Das ist der Mann, der das Mädchen gestern
gesehen hat.
Das ist der Mann, den das Mädchen gestern
gesehen hat.
Das ist der Ball, der das Mädchen am Kopf
getroffen hat.
Das ist der Ball, den das Mädchen mit dem Kopf
getroffen hat.
Experimental design
Animate head
Subject
Object
Inanimate head
Central tendency
Data:
2, 3, 3, 3, 4, 6, 6, 9, 12, 13, 13
Mean:
2+3+3+3+4+6+6+9+12+13+13
11
= 6.72
Median:
2, 3, 3, 3, 4, 6, 6, 9, 12, 13, 13
=6
Mode:
2, 3, 3, 3, 4, 6, 6, 9, 12, 13, 13
=3
Variance and SD
(x1 – x)2
N- 1
Variance
S
words
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
Variance
S
words
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
 59 / 8
= 7.4 (mean)
Variance
S
words
(=X1 – Xmean)
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
3 – 7.4
7 – 7.4
4 – 7.4
9 – 7.4
12 – 7.4
9 – 7.4
11 – 7.4
4 – 7.4
 59 / 8
= 7.4 (mean)
Variance
S
words
(=X1 – Xmean)
d1
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
3 – 7.4
7 – 7.4
4 – 7.4
9 – 7.4
12 – 7.4
9 – 7.4
11 – 7.4
4 – 7.4
–4.4
–0.4
–3.4
1.6
4.6
1.6
3.6
–3.4
 59 / 8
= 7.4 (mean)
Variance
S
words
(=X1 – Xmean)
d1
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
3 – 7.4
7 – 7.4
4 – 7.4
9 – 7.4
12 – 7.4
9 – 7.4
11 – 7.4
4 – 7.4
–4.4
–0.4
–3.4
1.6
4.6
1.6
3.6
–3.4
 59 / 8
= 7.4 (mean)
0/8=0
Variance
S
words
(=X1 – Xmean)
d1
d12 (residuals)
1
2
3
4
5
6
7
8
3
7
4
9
12
9
11
4
3 – 7.4
7 – 7.4
4 – 7.4
9 – 7.4
12 – 7.4
9 – 7.4
11 – 7.4
4 – 7.4
–4.4
–0.4
–3.4
1.6
4.6
1.6
3.6
–3.4
19.36
0.16
11.56
2.56
21.16
2.56
12.96
11.56
0/8=0
 81.87
 59 / 8
= 7.4 (mean)
Variance
81.87
8-1
= 11.7
Standard Deviation
81.87
8-1
= 3.42
Standard Deviation
70% of all data points fall within 1 SD.
Mean +/- SD = range of 70% of the data
7.4 +/- 3.42 = 3.98 – 10.82 words