Enrico Giai
BA in Translating and Interpreting
MA Student in Translation Studies
Turin University
Email: enrico.giai@gmail.com
Data Collection and
Analysis in Sociolinguistics
Practical elements for research methods in sociolinguistics
Turin, 07-08 April 2014
2
Monday, April 7th
Main topics
 Sociolinguistics: basic notions
 Collecting data
 Tool for data analysis
 Descriptive statistics: basic notions
 Example: using Excel for basic statistics
3
Sociolinguistics and sociology of
language
Sociolinguistics
Sociology of language
 Effects of society on language use
 Effects of language use on society
 Language variation & change
(variationist sociolinguistics)
 Who speaks what language to
whom and when
4
Quantitative and qualitative
approaches
Quantitative approach
Data
collection
Data
analysis
New
theory
Data
analysis
Theory
test
Qualitative approach
New
theory
5
Quantitative sociolinguistics
 Quantitative analyses are all about counting something […]. In order for
something to be counted, two conditions are normally considered to be
necessary: (a) what you want to count must itself be ‘countable’ (i.e.
quantifiable), and (b) what you want to count must have the potential to
be variable (i.e. be able to change).
Levon(2010:68)
6
Interviews: how to
 1st type: Structured
 Guided by set of questions on the topic
 Standardised & replicable
 Low degree of digression from the topic
 Yes/No, True/False, closed questions
 2nd type: Unstructured
 Free flowing
 Non standardised
 Higher degree of digression from the topic
 W-, open questions
 3rd type: Semi-structured
 Mix
7
Interviews: pro & con
Pros
Cons
 Immediate results
 Possible bias and manipulations
 Face-to-face
 Problems with generalisation
interviewees/society
 Prosodic features of the language
can be analysed
 Elements of culture and self
identity can be grasped
 Expensive in terms of time and
money
 Must be transcribed
The transcription process can be made easier by means of computer
programmes (e.g. ELAN)
8
Interviews: analysis
 Content analysis
 Discourse analysis
 Relational analysis
 Three main steps:
 Noticing concepts
 Collecting examples
 Analysing examples to find commonalities
9
Questionnaires and language surveys: how to
 1st type: desctiprive questionnaires
 How many people participate in a certain behaviour
 2nd type: analytical questionnaires
 Theory building and testing
 Questionnaire admininistration
 Self-administration
 Interview type situation
 Types of questions (level of measurement)
 Category type questions
 Ordinal type questions
 Continuous questions
10
Questionnaires and language surveys: pro&con
Pros
 Useful for surveying people from
different locations
 Not expensive
 Perfectly standardised &
replicable
 Easy to compare with other
questionnaires
Cons
 Not face-to-face
 Low response rate
 No certainty whether:
 Who filled it out
 When was filled out
 In what order was filled out
 How much time did it take to fill it
out
The analysis of questionnaires can be made easier by means of computer
programmes (e.g. Excel)
11
Questionnaires and language surveys: analysis
 Code the questions
 Transfer the information
 Establish strategy for analysis
 Summarise the responses
 Category and ordinal questions: frequency, charts
 Continuous questions: mean, median, mode, distribution
 Univariate vs bivariate vs multivariate analysis
12
Web: how to
 Using the Web as a source for corpora
 Tools:
 Google Advanced Search (e.g. http://www.google.com/advanced_search)
 WebCorp (e.g. http://www.webcorp.org.uk/live/)
 Twitter (e.g. https://twitter.com/search-advanced)
 Facebook (e.g. https://www.facebook.com/imatitikua.kokolet/about)
13
ELAN
 ELAN: freely downloadable (http://tla.mpi.nl/tools/tlatools/elan/download/)
 For audio and video interviews
 Helps the user to transcribe audio and/or video texts
 Transcription process in two steps
 Segmentation phase
 Transcription phase
14
ELAN: the basics (1)
Main page
New transcription
15
ELAN: the basics (2)
Grid selection and
main controls
Sound spectrum
Tier bar
16
ELAN for Interview Transcription –
An example
 Interview with a member of the Nigerian community in Turin, Italy
 Recorded file audio in .wav format
 Number of speakers
 1 interviewer
 1 interviewee
 Transcription work step-by-step
 Create a tier for each speaker
 Segment the utterances
 Annotate the utterances
 Save the transcription
17
ELAN for Interview Transcription –
Create New Tiers (1)
 “Tier” → “Add new tier”
 One for each participant to
the interview
 Add Tier Name
 Write your name in the
“Annotator” blank space
18
ELAN for Interview Transcription –
Create New Tiers (2)
 The tiers referred to the two participants should appear
19
ELAN for Interview Transcription –
Segment the Utterances (1)
 “Options” →
“Segmentation mode”
 Double click the tier to
select the speaker
 Hit the Enter key once to
open a new segment.
Another time to close it
 Click on the play button to
start and stop the file
20
ELAN for Interview Transcription –
Segment the Utterances (2)
21
ELAN for Interview Transcription –
Annotate the segments (1)
 “Option”→ “Annotation mode”
 Double-click the segment you want to transcribe and write the utterance
down
22
ELAN for Interview Transcription –
Annotate the segments (2)
23
ELAN for Interview Transcription –
What now? (1)
 You can
 Export it as an interlinear text, to have a Word version of the transcription
24
ELAN for Interview Transcription –
What now? (2)
 You can
 Export it as a Tab-delimited
text, which can be
transferred to an Excel
document and analysed
25
PRAAT
 PRAAT: freely downloadable
(http://www.fon.hum.uva.nl/praat/download_win.html)
 For speech analysis
 Source: audio files (.wav and .mp3)
 Annotation:
 Phonetic
 Orthographic
26
PRAAT – Main page
27
PRAAT – An example (1)
Open > Read from file
Example: interview with a
Nigerian immigrant
28
PRAAT – An example (2)
Annotate > To TextGrid
29
PRAAT – An example (3)
Define number and name of tier(s) for each speaker(s)
30
PRAAT – An example (4)
Select both > View&Edit
31
PRAAT – An example (5)
32
PRAAT – An example (6)
Mouse on spectrogram > select passage (Tab=play)
33
PRAAT – An example (7)
Click on spectrogram>click on circle: delimitate tag
34
PRAAT – An example (8)
Enter key > annotate
35
WordSmith Tools
 Wordsmith Tools: freely downloadable
(http://www.lexically.net/wordsmith/)
 For concordances & word lists
 Source: corpus/linguistic database in .txt format
 Example: see next slide
36
WordSmith Tools – Excel to .txt
Source
.txt file
37
WordSmith Tools – Main page
38
WordSmith Tools – An example (1)
39
WordSmith Tools – An example (2)
40
WordSmith Tools – An example (3)
41
WordSmith Tools – An example (4)
42
WordSmith Tools – An example (5)
43
Basic statistics notions
 Average/aritmethic mean
 Median
 Mode
 Frequency
 Minimum values
 Maximum values
 Range/standard deviation
44
Microsoft Excel (1)
45
Font selection
and
customisation;
cell borders
Microsoft Excel (2)
Alignment
Merge cells
Insert/delete
rows/columns
Find
46
Microsoft Excel (3)
Columns
Rows
Cell: A1
47
Microsoft Excel (4)
Insert text: double click on cell
Right click>’Formato celle’
48
Microsoft Excel – Basic Notions
 Formulae are inserted clicking on the “Inserisci funzioni” button, or typing
them in a cell
 Rules
 Preceded by an equal sign;
 Use of coordinates;
 Types of formulae:
 Arithmetic
 Statistical
 Logical
49
Microsoft Excel – Entering data
 Collect data using questionnaires or language surveys
 Codify your questions and answers
 Arrange them
 Row 1: questions. Each column represents a question
 Row 2 to ∞: answers
50
Microsoft Excel – Coding a
questionnaire/language survey
 Coding your responses/data
 Allocating a number to the answers of each question
 Coding category type questions
 e.g. gender (M=1; F=2)
 Coding ordinal questions
 e.g. age brackets (1=15-20; 2=21-25; …)
 Coding continuous questions
 Already numbers
51
Using Excel for basic descriptive statistics
A language survey on the linguistic repertoire of Filipino
immigrants in Turin
 Features:
 122 questioned people so far (Dec 2013)
 Heterogeneous population
 Questionnaires collected in Microsoft Excel
 Linguistic and ethnographic data
52
Example of language survey
Linguistic data
Ethnographic data
 Mother tongue
 Age
 Known languages
 Gender
 Language use in different
contexts
 Education
 Code-switching
 Occupation
 Length of stay in Italy
53
Excel worksheet layout (1)
 Layout
 Column A to BB, row 1 (A1:BB1)
 Questions (“Factor Groups”)
 Column A, row 2 to 123 (A2:A123)
 Speakers’ ID
 Column B to BB, row 2 to 123 (B2:BB123)
 Answers
54
Excel worksheet layout (2)
55
Notations
 N/A: data not available
 #: empty cell
 BC: blue collars
 PC: pink collars
 WC: white collars
 Length of stay in Italy expressed in months
 Age and school attendance expressed in years
 N.B.
 Each cell contains only 1 piece of information
Coding
Languages
56
Language use
Occupation
English varieties
Y/N
Gender
N/A
0
N/A
0
N/A
0 N/A
Arabic
1
English
1
BC
1 American English 1 Yes 1 M
1
Bicol
2
Italian
2
PC
2 British English
2
Bisaya
3
Tagalog
3
WC
3 Standard English 3
Cebuano
4
English/Italian
4
Unemployed 4
Chinese
Danish
English
5
6
7
English/Tagalog
Italian/Tagalog
No
5
6
7
Filipino dialect
8
Ita/Tag/English
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9 French
10 Ilocano
11 Other
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
Vietnamese
19
20
21
9
10
11
0 N/A 0 N/A
2 No
2 F
0
57
Language survey analysis with
Microsoft Excel
Descriptive statistics
 Most useful formulae for descriptive statistics in Excel
 Average: “=MEDIA()”
 Median: “=MEDIANA()”
 Mode: “=MODA()”
 Minimum point: “=MIN()”
 Maximum point: “=MAX()”
 Creation of charts and graphs
 Bar graphs
 Pie charts
58
What can we analyse using descriptive
statistics formulae?
 Age
 Length of stay in Italy
 School attendance
 Number of known languages
 Number of languages used at school (PH)
 Number of languages used at work (PH)
 Number of languages used at work (IT)
 Number of languages used with family (PH)
 Number of languages used with family (IT)
 Number of languages used with friends (PH)
 Number of languages used with friends (IT)
59
What can we draw graphs and charts
of? (1)
 Gender
 Occupation (PH)
 Occupation (IT)
 Nationality of friends (IT)
 Contacts with Filipinos
 Mother tongue
 Other known languages
60
What can we draw graphs and charts
of? (2)
 Languages used at school
 Languages used at school (PH)
 Languages used at work (PH)
 Languages used at work (IT)
 Languages used with family (PH)
 Languages used with family (IT)
 Languages used with friends (PH)
 Languages used with friends (IT)
 Code-switching
 Use of Italian in the Philippines
61
Layout of the analysis table (1)
 See analysis worksheet
 Column A, row 2 to 206 (A2:A206)
 Questions (“Factor Groups”)
 Column B, row 2 to 206 (B2:B206)
 Possible answers
 Type of calculus
 Column C, row 2 to 206 (C2:C206)
 Number of occurrences
 Results
 Column D, row 2 to 206 (D2:D206)
 Percentage values
 Column E, row 2 to 206 (E2:E206)
 Total amount of factor group
Layout of the analysis table (2)
62
63
Number of known languages (1)
Number of known languages: (L2:L123) in the “Questionnaire” worksheet ($: fixed
reference)
 Average number of known languages in C2
 =MEDIA(Questionnaire!$L$2:$L$123)
 Median number of known languages in C3
 =MEDIANA(Questionnaire!$L$2:$L$123)
 Mode number of known languages in C4
 =MODA(Questionnaire!$L$2:$L$123)
 Minimum value of number of known languages in C5
 =MIN(Questionnaire!$L$2:$L$123)
 Maximum value of number of known languages C6
 =MAX(Questionnaire!$L$2:$L$123)
64
Number of known languages (2)
65
Mother tongue (1)
Mother tongue: (M2:M123) in the “Questionnaire” worksheet ($: fixed reference)
 Number of N/A in C7
 =CONTA.SE(Questionnaire!$M$2:$M$123;”0”)
 Number of Italian mother tongue people in C8
 =CONTA.SE(Questionnaire!$M$2:$M$123;”13”)
 Number of English mother tongue people in C9
 =CONTA.SE(Questionnaire!$M$2:$M$123;”7”)
 Number of Tagalog mother tongue people in C10
 =CONTA.SE(Questionnaire!$M$2:$M$123;”20”)
 Number of Cebuano mother tongue people in C11
 =CONTA.SE(Questionnaire!$M$2:$M$123;”4”)
 Number of Ilocano mother tongue people in C12
 =CONTA.SE(Questionnaire!$M$2:$M$123;”11”)
66
Mother tongue (2)
Mother tongue: (M2:M123) in the “Questionnaire” worksheet ($: fixed reference)
 Number of Bisaya mother tongue people in C13
 =CONTA.SE(Questionnaire!$M$2:$M$123;”3”)
 Number of Kapampangan mother tongue people in C14
 =CONTA.SE(Questionnaire!$M$2:$M$123;”14”)
 Number of Pampango mother tongue people in C15
 =CONTA.SE(Questionnaire!$M$2:$M$123;”16”)
 Percentage of C7 to C15
 In D7: =(C7*100)/122
 click on the lower right corner of D7 and drag it down to D15
 N.B. Write cell name instead of language and drag it down
67
Mother tongue (3)
 Total amount of the Mother tongue factor group
 =SOMMA(C7:C15)
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
68
Other known languages (1)
Other known languages (apart from Mother tongue): (N2:S123) in the “Questionnaire”
worksheet ($: fixed reference)
 Number of Italian speakers in C16
 =CONTA.SE(Questionnaire!$N$2:$S$123;B16)
 Click on the lower right corner of C16 and drag it down to C36 to get all the results
 Percentage of C16 to C36
 click on the lower right corner of D15 and drag it down to D36
69
Other known languages (2)
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
70
Number of languages used at school PH (1)
Number of languages used at school: (T2:T123) in the “Questionnaire” worksheet ($: fixed
reference)
 Average number of languages used at school in C37
 =MEDIA(Questionnaire!T2:T123)
 Median number of languages used at school in C38
 =MEDIANA(Questionnaire!T2:T123)
 Mode number of languages used at school in C39
 =MODA(Questionnaire!T2:T123)
 Minimum value of number of languages used at school in C40
 =MIN(Questionnaire!T2:T123)
 Maximum value of number of languages used at school C41
 =MAX(Questionnaire!T2:T123)
71
Number of languages used at school PH (2)
72
Languages used at school PH (1)
Languages used at school PH: (U2:W123) in the “Questionnaire” worksheet ($: fixed
reference)
 Number of Italian in C42
 =CONTA.SE(Questionnaire!$U$2:$W$123;B48)
 Click on the lower right corner of C42 and drag it down to C53 to get all the results
 Percentage of C42 in D42
 =(C42*100)/122
 click on the lower right corner of D42 and drag it down to D53
73
Languages used at school PH (2)
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
74
Number of languages used at work PH/IT
Number of languages used at work PH: (X2:X123) in the “Questionnaire” worksheet
Number of languages used at work IT: (AB2:AB123) in the “Questionnaire” worksheet
Languages used at work PH/IT (1)
75
Languages used at work PH: (Y2:AA123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
Languages used at work PH/IT (2)
76
Languages used at work IT: (AC2:AF123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
Languages used at work PH/IT (3)
77
Bar graph
Code
N/A
0
Bisaya
3
English
Ilocano
Italian
Kapampangan
Tagalog
7
11
13
14
20
Languages used at work PH/IT (4)
78
Bar graph
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
79
Number of languages used with family PH/IT
Number of languages used with family PH: (AG2:AG123) in the “Questionnaire” worksheet
Number of languages used with family IT: (AK2:AK123) in the “Questionnaire” worksheet
Languages used with family PH/IT (1)
80
Languages used with family PH: (AH2:AJ123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
Languages used with family PH/IT (2)
81
Languages used with family IT: (AL2:AO123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
Languages used with family PH/IT (3)
82
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
83
Number of languages used with friends PH/IT
Number of languages used with friends PH: (AP2:AP123) in the “Questionnaire” worksheet
Number of languages used with friends IT: (AT2:AT123) in the “Questionnaire” worksheet
Languages used with friends PH/IT (1)
84
Languages used with friends PH: (AQ2:AS123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
Languages used with friends PH/IT (2)
85
Languages used with friends IT: (AU2:AW123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
86
Languages used with friends PH/IT (3)
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
87
Code-switching (1)
Code-switching: (AX2:AX123) in the “Questionnaire” worksheet ($: fixed reference)
 0 in C156
 =CONTA.SE(Questionnaire!$AX$2:$AX$103;B156)
 1 and 2 in C157 and C158
 Drag C156 down
Code
N/A
0
Yes
1
No
2
88
Code-switching (2)
Code
N/A
0
Yes
1
No
2
89
Language use – Television (1)
Code
Languages used to watch TV: (AY2:AY123) in the “Questionnaire” worksheet N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
90
Language use – Television (2)
Code
N/A
0 Danish
6
Arabic
1 English
7
Bicol
2
Filipino
dialect
8
Bisaya
3 French
9
Cebuano
4 German
10
Chinese
5 Ilocano
11
91
Language use – Books (1)
Languages used to read books: (AZ2:AZ123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
92
Language use – Books (2)
Code
N/A
0 Danish
6
Arabic
1 English
7
Bicol
2
Filipino
dialect
8
Bisaya
3 French
9
Cebuano
4 German
10
Chinese
5 Ilocano
11
93
Language use – Dreams (1)
Languages used to dream: (AA2:AA123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
94
Language use – Dreams (2)
Code
N/A
0 Danish
6
Arabic
1 English
7
Bicol
2
Filipino
dialect
8
Bisaya
3 French
9
Cebuano
4 German
10
Chinese
5 Ilocano
11
95
Language use – Thoughts(1)
Languages used to think: (AB2:AB123) in the “Questionnaire” worksheet
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
Ilonggo
Italian
Kapampangan
Norsk
Pampango
Pangasinan
9
10
11
12
13
14
15
16
17
Piedmontese dialect 18
Spanish
Tagalog
19
20
96
Language use – Thoughts (2)
Code
N/A
0 Danish
6
Arabic
1 English
7
Bicol
2
Filipino
dialect
8
Bisaya
3 French
9
Cebuano
4 German
10
Chinese
5 Ilocano
11
97
Language use: to sum up (1)
Code
0
1
2
3
4
5
6
7
8
9
10
11
TV
23,00
6,00
62,00
5,00
17,00
1,00
4,00
0,00
4,00
0,00
0,00
0,00
Books
42,00
10,00
56,00
4,00
8,00
1,00
1,00
0,00
0,00
0,00
0,00
0,00
Dreams
Thoughts
48,00
46,00
4,00
6,00
23,00
30,00
16,00
0,00
11,00
15,00
2,00
0,00
7,00
0,00
0,00
12,00
0,00
0,00
2,00
1,00
0,00
3,00
9,00
9,00
Code
N/A
0
Arabic
1
Bicol
2
Bisaya
3
Cebuano
4
Chinese
Danish
English
5
6
7
Filipino dialect
8
French
German
Ilocano
9
10
11
Language use: to sum up (2)
98
Language use
70
62
60
50
56
48
46
42
40
30
30
23
23
20
17
16
10
10
6
4
6
8
5 4
15
11
1 1 2 0
0
0
0
1
2
3
12
4
TV
4
7
1
5
Books
6
Dreams
9 9
4
0
0 0 0
7
Thoughts
0 0 0
8
2
0 0 1
9
0 0 0
10
3
0 0
11
99
Results (1)
Ethnographic data
 Men and women are equally partitioned
 Average age: ca 33 yrs
 Average length of stay: ca 15 months
 Average education: ca 12 years (medium/high)
 Occupation: more PC than BC WC once in Italy; same unemployment rate
 Almost all friends in Italy are Filipinos
 Frequent contacts with Filipinos living in the PH once in Italy
100
Results (2)
Linguistic data
 Average known languages: ca 4
 Most common mother tongue: Tagalog
 Most common known languages: English and Italian
 Most common language at work PH: English and Tagalog
 Most common language at work IT: Italian and English
 Most common language with family PH: Tagalog and English
 Most common language with family IT: Tagalog and Italian
 Most common language used with friends PH: Tagalog and English
 Most common language used with friends IT: Tagalog and Italian
 Code switching: Yes in more than ¾ of cases
101
Results (3)
Linguistic data
 Most commonly used languages to watch TV: Italian and English
 Most commonly used languages to read books: Italian and English
 Most commonly used languages to dream: Italian and Tagalog
 Most commonly used languages to think: Italian and Tagalog
102
What now?
Data analysis and hypothesis
Can we say that the high rate of code-switching is influenced by ethnographic and/or
linguistic factors?
 Multivariate analysis and Rbrul
103
References
BLOOMER A. & WRAY A., 2006, Projects in Linguistics 2nd Edition, Hodder Arnold, London
and New York.
EDLEY N. & LITOSSELITI L., 2010, "Contemplating Interviews and Focus Groups", in Litosseliti L.
(ed.), Research Methods in Linguistics, Continuum, London and New York: 155-179
HON, K., 2013, “An Introduction to Statistics”, retrievable from the World Wide Web:
http://www.artofproblemsolving.com/LaTeX/Examples/statistics_firstfive.pdf
LEVON, E., 2010, "Organizing and Processing Your Data: The Nuts and Bolts of Quantitative
Analyses", in Litosseliti L. (ed.), Research Methods in Linguistics, Continuum, London and
New York: 68-92.
See the e-book in my blog
104
Thank you