APPROVED: Lawrence Williams, Major Professor Dorian Roehrs, Committee Member Donny A. Vigil, Committee Member Marie-Christine Koop, Chair of the

Department of Foreign Languages and Literatures

James D. Meernik, Acting Dean of the Robert B. Toulouse School of Graduate Studies

THE VARIABLE USE OF NE IN NEGATIVE STRUCTURES: AN APPARENT-TIME

VARIATIONIST STUDY OF SYNCHRONOUS ELECTRONIC

FRENCH DISCOURSE

Rebecca J. Gould

Thesis Prepared for the Degree of

MASTER OF ARTS

UNIVERSITY OF NORTH TEXAS

December 2010

Gould, Rebecca J. The variable use of ne in negative structures: An apparent-

time variationist study of synchronous electronic French discourse. Master of Arts

(French), December 2010, 71 pp., 13 tables, 1 figure, references, 33 titles.

This study of the variable use of ne in synchronous electronic French discourse

follows the methodological guidelines and the theoretical framework proposed and

subsequently elaborated by Labov for analyzing variable features of language. This

thesis provides a quantitative variable rule (i.e., VARBRUL) analysis including age as a

factor group (i.e., independent variable), thereby making a new contribution to this area

of inquiry. The data (50,000 words from the vingtaine 'twentysomething' channel and

50,000 words from the cinquantaine 'fiftysomething' channel) are a subset of 100,000

words from a corpus of one million words collected in 2008 by the thesis director from

the public chat server EuropNet.

This study aims to answer the following overarching question: To what extent

does age—compared to other factors—influence the variable use of ne in verbal

negation in synchronous electronic French discourse? In order to answer this question,

and possibly others, the VARBRUL analysis will include age, subject (e.g., noun vs.

pronoun), type of second negative particle (e.g., pas 'not', jamais 'never', personne 'no

one'/'nobody', and so forth), as well as verbal mood/tense.

ii

Copyright 2010

by

Rebecca J. Gould

iii

TABLE OF CONTENTS

Page

LIST OF TABLES............................................................................................................v

LIST OF FIGURES.........................................................................................................vi

1. INTRODUCTION.........................................................................................................1

1.1 Aim and Scope................................................................................................1

1.2 Chat Discourse................................................................................................2

1.3 The Variationist Tradition in Sociolinguistics ..................................................5

1.4 The Origin and Development of Ne Use.........................................................7

1.5 Negation in French..........................................................................................8

2. PREVIOUS STUDIES OF NE RETENTION..............................................................13

2.1 Synthesis......................................................................................................13

2.2 Factors that Influence Ne Retention..............................................................15

2.2.1 Linguistic factors..............................................................................16

2.2.2 Individual (Macrosociological) Factors.............................................17

3. METHODOLOGY.......................................................................................................20

3.1 Description of the Corpus..............................................................................20

3.2 Coding...........................................................................................................22

3.3 Examples of Ne from the Corpus..................................................................23

3.4 Variable Rule Analysis..................................................................................25

3.5 Levels of Analysis in GoldVarb X..................................................................27

4. RESULTS AND ANALYSIS.......................................................................................32

4.1 Residuals (Percentages): Distribution of Ne Absence/Presence.................32

iv

4.2 Cross Tabulation...........................................................................................34

4.3 One-Level Binomial Analysis........................................................................35

4.4 Step-up/step-down analysis..........................................................................37

4.5 Statistical significance..................................................................................39

4.6 Ranges.........................................................................................................40

5. CONCLUSION...........................................................................................................41

Appendices

A. GOLDVARB X DATA CODING KEY.........................................................43

B. BINOMIAL ONE-STEP ANALYSIS WITH ALL DATA...............................46

C. CROSS TABULATION WITH ALL DATA.................................................50

D. KNOCKOUTS AND SINGLETONS FROM BINOMIAL ONE-STEP.........56

E. BINOMIAL ONE-STEP ANALYSIS REDONE EXCLUDING KNOCKOUTS,

SINGLETONS, VARIABLE 1 (SECOND NEGATIVE PARTICLE),

AND VARIABLE 3 AND (VERB MOOD/TENSE).............................58

F. CROSS TABULATION WITH VARIABLES 2 AND 4................................61

G. BINOMIAL STEP-UP ANALYSIS.............................................................63

H. BINOMIAL STEP-DOWN ANALYSIS.......................................................65 REFERENCES.............................................................................................................67

v

LIST OF TABLES

Page

1 Vingtaine (Twentysomething) Channel Excerpts from May 3, 2008..............................4 2 The Cycle of Negation...................................................................................................8

3 French Negative Particles.............................................................................................9

4 Regional Variation in Spoken French..........................................................................14

5 Age of Speaker and Ne Retention...............................................................................15

6 Retention Rates of Ne in Previous Studies: Second Negative Particle Type..............15 7 Total Number of Words................................................................................................21

8 Data Collection (2008).................................................................................................21

9 Vingtaine (V) and Cinquantaine (C) Channel Excerpts: Ne Absence/Presence…......23

10 Vingtaine Channel Excerpts from May 3, 2008………………....................................25

11 Percentages of Ne Variation from the Original Coding..............................................33

12 Percentages of Ne Variation: Independent Variables 2 and 4...................................35 13 VARBRUL Analysis...................................................................................................39

vi

LIST OF FIGURES

Page

1. Scattergram of recoded data: Binomial one-step analysis.........................................37

1

CHAPTER 1

INTRODUCTION

1.1 Aim and Scope

This research treats the use of negative expressions in two non-moderated

Internet relay chat (IRC) rooms and compares the variable use of ne in two age groups

(one in the vingtaine 'twentysomething' channel and the other in the cinquantaine

'fiftysomething' channel). This study provides a unique contribution because ne

retention/deletion has not been extensively studied in computer-mediated

communication (CMC) and because age group has not been investigated in this type of

environment. The present study analyzes the variable use of ne in 1,015 tokens of

verbal negation in an attempt to comprehend the extent to which the communication in

two non-moderated chat rooms imitates informal/spoken language or formal/written

language, notably in the usage of negative expressions such as ne . . . pas 'not', ne . . .

rien 'nothing', ne . . . plus 'no more'/'no longer', ne . . . jamais 'never', ne . . . aucun(e)

'no . . . '/'no . . . (whatsoever)', ne personne 'no one', ne . . . ni . . . ni 'neither . . . nor', ne

point 'not (at all)', and the limiting expression ne . . . que 'only'. Communication in non-

moderated chat is a mix of forms commonly associated with both spoken and written

discourse since it is a type of written conversation. This thesis explores—to varying

degrees—some of the following issues and questions: To what extent and in what ways

do chat participants use the first negative particle ne? Does the age or age group of the

Internet user influence the retention of ne in any way? Is the retention of ne more often

associated with certain subject types (e.g., noun phrases vs. pronouns) or certain verb

tenses/moods? Does the language of nonmoderated chat rooms more closely resemble

2

formal written French (where the ne is generally retained) or everyday spoken French

(where the ne is in many cases not used)? Which factor(s) most significantly favors ne

retention in this context?

1.2 Chat Discourse

Nonmoderated chat rooms are one of the most popular forms of CMC (Pierozak,

2003; Werry, 1996; van Compernolle & Williams, 2007). Participants in IRC engage in

real-time discussions with other participants in chat channels without geographic

limitations. The participants self-identify by age or interest according to the choice of

chat channel (although nothing prohibits a 20 year old participating in a chat room for 50

year olds or vice versa). This is an environment that seems to be quite different from

more formal writing or speaking, where ne retention is more common. Since

synchronous chat is a text-based conversation, it is interesting to note to what extent

non-moderated chat is similar to and different from traditional spoken or written French.

Synchronous chat on public servers is almost always an environment with limited

information on the socioeconomic background of the participants, so these variations

cannot be easily studied (Paolillo, 2001, p. 181). It is possible that the participants base

their view of the level of formality of the chat room on their previous experience

communicating in a text-based synchronous chat context (Williams, 2009). However,

research indicates that since participants are anonymous to each other, they rely on

linguistic features in order to decide what level of formality is expected and, thereby,

appropriate (Williams, 2009).

Communication on IRC is a complex system. There is a general code of conduct,

3

or “netiquette” that is required of participants (Werry, 1996). IRC provides a place where

“intense personal relationships can be formed and maintained” (Werry, 1996, p. 50). It is

similar to written and oral communication in certain ways, but unique in its own way.

It is similar to written discourse in that users write their responses that are seen

on screen by all the participants, but the unusual sequencing (in which conversations

are interwoven) makes IRC unique (Werry, 1996). The rapid changes in discussion

topic are also unique and can lead to ambiguity, according to Werry. In order to avoid

confusion and to maintain the attention of the other participants, there is a high level of

addressivity used, more than is typical in written or spoken communication (Werry, pp.

52-53). Another factor that makes IRC unique as its own communication is the fact that

there are so many factors limiting the size of the communication submitted that do not

play a role in typical written and spoken communication, such as “screen size, average

typing speed, minimal response times, competition for attention, channel population and

the pace of channel conversations” (Werry, p. 53). This all leads to brevity through

various abbreviations and symbols. In fact, the average message length in Werry’s

study was six words, which appears to be an optimal length to communicate a

message, but remain brief and keep the attention of the audience. These are

considerations that are certainly not taken into account to such an extent in speaking or

writing.

In general, one can observe a tendency on IRC for words to be stripped down to

the fewest possible letters that will enable them to be meaningfully

recognized…In this incessant drive to reduce the number of required keystrokes

to the absolute minimum, some words get truncated especially often, especially

4

those that resemble the sound of a single letter when pronounced. (Werry, p. 55)

The pressure to be brief leads to subject pronoun deletion (Werry, 1996) or

abbreviation, which was certainly seen in my study, as seen below in Table 1.

Table 1

Vingtaine (Twentysomething) Channel Excerpts from May 3, 2008 Time Excerpt Type of variation [07:31] <Mrs `Dan `Carter34> Nan jveux pas :p

(No, I don’t want) subject je is shortened to j

[07:36] <RockDancer> lol ya que moi qui en porte… (lol, there is only me who is wearing it…)

subject il is dropped

[09:07] <moi> c pas drole (that’s not funny)

subject ce is shortened to c

[14:01] <danseur . . . > faut pas le faire :p (shouldn’t do it)

subject il is dropped

IRC is more like speech in that the abbreviation allows the dialogue to pass very

quickly like a spontaneous, synchronous oral conversation. Abbreviations, omission of

pronouns, and lack of capitalization also make the chat more informal, as is everyday

spoken discourse (van Compernolle & Williams, 2007; Werry, 1996). Chat discourse

can resemble speech in other ways as well. There is "an almost manic tendency to

produce auditory and visual effects in writing, a straining to make written words simulate

speech" (Werry, p. 58). Yet IRC maintains a unique identity through various means,

including “simultaneous involvement of the ear and eye" (Werry, p. 59). It is also unique

in that a participant has a more distant connection to his or her words:

Through being embodied in electronic text, the speaker’s words are

5

depersonalized, stripped of all of the material qualities that individualize them and

connect them to a particular speaker…The process of self-consciously

constructing the paralinguistic dimensions of communication may heighten the

sense of their artificiality, and lead to an increased tendency to experiment and

play with them. (Werry, p. 59)

Although there is no data on participants, internal linguistic factors, such as

subject and verb tense, can still be studied. This method of data collection, wherein

participants communicate with one another rather than an interviewer may be more

authentic and more revealing (Labov, 1972, pp. 89-90). Of course, this conversation

took place in a public space and since participants are aware of that, they may have

modified what they might have said in private.

1.3 The Variationist Tradition in Sociolinguistics

As language does not live in a vacuum, but rather in an active community of

dynamic participants in which “social pressures are continually operating upon

language” (Labov, 1972, p. 3), observers of the language may notice variations in each

speaker’s choice of words and pronunciations based on context.

As the founder of variationist sociolinguistics, William Labov has focused on

using variation to explain change. This is unique from earlier (socio)linguistic studies

because the focus is limited to features, structures, and elements that can be spoken or

written in at least two different ways. The speaker makes a choice based on certain

aspects of context or any other number of factors. Variationist sociolinguistics can often

provide clear answers that cannot be uncovered through many types of discrete

6

linguistic analysis. Some previous research has studied ne retention in a variationist-

inspired framework (Labov, 1972), which takes into consideration, among other things,

the age, gender, educational background, and social class of participants. This

approach also allows for the consideration of the influence of subject type and second

negative particle, among other internal/linguistic factors. The Labovian framework is

primarily interested in variation as a way to explain change in progress or completed

change(s). The variationist methodology is considered to be valid and lends legitimacy

to the methods employed in this study.

[E]very-day speech involved a great deal of variation, which the standard theory

was not equipped to deal with. The tools for studying variation and change in

progress emerged from that situation. Eventually, it turned out that the study of

variation gave clear answers to many of the problems that were not resolved by a

discrete view of linguistic structure. (Labov, 2007)

Labov preferred the study of everyday language to more formal contexts because he

found that everyday speech is more effective in addressing the major questions of

(socio)linguistics.

The problem of explaining language change seems to resolve itself into three

separate problems: the origin of linguistic variations; the spread and propagation

of linguistic changes; and the regularity of linguistic change. The model which

underlies this three-way division requires as a starting point a variation in one or

several words in the speech of one or two individuals. (Labov, 1972, p. 1)

Negation in French is an excellent element for demonstrating variation in language

since it can be studied using, among other things, the variationist approach. Its

7

frequency of appearance in natural, undirected discourse; its structure; its integration

into the communicative system; and its highly-stratified distribution (based on age or

other societal divisions) make it ideal for study (pp. 7-8).

1.4 The Origin and Development of Ne Use

The use of the negative particle ne has its origins in Old French when ne was the

sole component of the negative expression. It was located structurally before the verb.

In the Middle Ages, a second negative particle was often added for emphasis after the

verb (Rickard, 2003). Instead of simply being able to say not, a range of negative

expressions allowed for more specificity or for providing additional information. During

the period of Classical (Modern) French (17th and 18th centuries), as the second

negative expression became more widely used, the emphasis that it had implied before

was gone and the second part of the negative expression became standard (Rickard,

1989). And in modern French, the second negative expression carries more of the

semantic weight of the negative expression, making the retention of ne less important

for comprehension (Williams, 2009).

The deletion of ne is a fairly recent phenomenon, appearing in some literature

produced during the 17th and 18th centuries (where ne deletion was associated with

lower socioeconomic status), but not appearing to be widespread until the 19th century

(Ayers-Bennett, 1996). It is impossible from written texts to determine the exact timing in

which ne deletion became standard (i.e., included in grammars, dictionaries, and

curricula). In any case, it is clear that ne deletion is now common among a large

majority of French speakers in an informal setting (and, in some instances, even in

8

formal settings), regardless of socioeconomic status or age. Ne deletion continues to be

an important sociolinguistic tool to signal the speaker’s perception of the level of

informality or formality of the context. Table 2 shows the history of the development of

ne in the French language.

Table 2

The Cycle of Negation (adapted from Ashby, 2001, and van Compernolle, 2007)

Period Negative expression Classic Latin non + verb Old French ne + verb Middle Ages French ne + verb (+ 2nd negative to add emphasis )

Classical French ne + verb + 2nd negative Modern spoken French (ne) + verb + 2nd negative French of the future? verb + 2nd negative

1.5 Negation in Modern French

In the French of today, a negative expression can be formed with two parts—a

pre-verbal particle (ne, or n’ before a vowel) and a post-verbal particle or combination of

particles (usually pas 'not') that normally surround the verb. Although the particles are

frequently adjacent to the conjugated verb in a given clause, they can also be remote. In

more formal contexts, such as writing or scripted spoken media, for example, the ne is

typically retained as in (1). In everyday spoken discourse, most speakers of French drop

the ne, which is shown in (2).

(1) Je ne comprends pas.

I do not understand.

(2) Je comprends pas.

9

I do not understand. (I don't understand.)

Both the first and second negative particles are placed before an infinitive.

(3) Tu essaies de ne pas oublier?

You are trying not to forget?

There are various second negative particles (Neg2s) besides pas 'not' (see Table 3).

Other Neg2s are used with ne to express more complex types of negation (Grevisse,

1993).

Table 3

French Negative Particles

French English Negative Adverbs

ne . . . plus Not anymore, no longer

ne . . . jamais Never

ne . . . nulle part Nowhere

ne . . . guère Hardly

ne . . . point Not (at all)

Negative pronouns

ne . . . rien Nothing

ne . . . personne no one

Others

ne . . . aucun(e) no/not any

ne . . . nul no/not any

ne . . . que Only

10

Selected examples of negative particles from Table 2 are provided below in (4), (5), (6),

(7), and (8).

(4) Il ne regarde plus.

He is not watching anymore.

(5) Elle n’a vu personne.

She saw no one.

(6) Nous n’avons rien mangé.

We did not eat anything./ We ate nothing.

(7) Je n’ai aucune idée.

I have no idea (whatsoever).

(8) Ils ne veulent que celui qui est grand.

They only want the big one.

Rien and personne can also be used as the subject of a sentence, as in (9) and (10).

(9) Rien n'est certain.

Nothing is certain.

(10) Personne ne sait.

No one knows.

Multiple negative words (other than pas) can be placed together as a simple negation,

as in (11).

11

(11) Elle n'a plus jamais rien dit à personne.

She never said anything else to anybody.

Double negative particles (i.e., pas 'not' followed by a second Neg2) form a double

negation, as shown in (12).

(12) Elle n'a pas vu personne.

She did not see nobody (i.e., she saw somebody).

In certain literary contexts, ne can express negation by itself without a second

negative particle. There are four verbs that may use this literary construction (pouvoir

'to be able to', savoir 'to know', oser 'to dare', and cesser 'to cease') as well as certain

proverbs and fixed expressions (Grevisse, 1993).

(13) (standard, ne + pas) Je ne sais pas. 'I do not know.'

(14) (casual, pas only) Je sais pas. 'I do not know.'/'I don't know.'

(15) (literary, ne only) Je ne sais. 'I do not know.'

The French expletive ne is used in certain cases without signifying any negation. It is

found in finite subordinate clauses (never before an infinitive) and is used only in the

following contexts (Grevisse, 1993):

• The complement clause of verbs expressing fear or avoidance: craindre (to fear), avoir peur (to be afraid), empêcher (to prevent), éviter (to avoid)

• The complement clause of verbs expressing doubt or denial: douter (to doubt), nier (to deny)

• Adverbial clauses introduced by the following expressions: avant que (before), à moins que (unless), de peur/crainte que (for fear that)

12

• Comparative constructions expressing inequality: autre (other), meilleur (better), plus fort (stronger), moins intelligent (less intelligent), etc.

(16) J'ai peur que cela ne se reproduise [Ø].

I am afraid that it might happen again.

(17) Il est arrivé avant que nous n'ayons [Ø] commencé.

He arrived before we started.

(18) Ils sont plus nombreux que tu ne le crois [Ø].

There are more of them than you think.

13

CHAPTER 2

PREVIOUS STUDIES OF NE DELETION/RETENTION

2.1 Synthesis

The negative particle ne is considered to be one of the primary indicators of

sociolinguistic variation in French (Coveney, 1996; Gadet, 1989; Williams, 2009) and is

thus an important area of study in linguistics. French negation was chosen for this

study because of its frequency of usage, its well-integrated structure, its possibility as a

variation between two choices (with or without the ne), its seemingly unpredictability,

and its distribution through social groups. The evolution of ne retention is incontestable

(Hansen & Malderez, 2004, p. 9). The retention of ne is generally associated with

written French or formal speech. Ne is normally dropped in casual conversation.

According to the sociolinguist Françoise Gadet, the ne has so often disappeared in

spoken French that this practice is no longer stigmatized (Fonseca-Greber, 2007) and

no longer is as closely associated with social class as it has been in the past (Hansen &

Malderez, p. 27). On the other hand, the frequency of the appearance of the ne in

written language remains at 33-75% (Hansen et Malderez). Previous studies of the use

of negative particles in e-mail, chat rooms, and forums (Panckhurst, 2007, p.121;

Williams, 2009) suggest that the use of pas without ne in negative structures is not

frequent for all speakers in all contexts (e.g., educational vs. noneducational contexts;

moderated chat vs. nonmoderated chat). However, the decreased use of the ne in

spoken French seems to be common for the majority of French speakers. The ne only

appeared in 10% of negative utterances in Fonseca-Greber's (2007) study of spoken

French, perhaps since it is the post-verbal negative expression that conveys the specific

14

nuance of negation in Modern French. Fonseca-Greber also found that the retention of

ne is more frequent when a person speaks with emphasis or adds an intensifying

adverb. Another study (Armstrong & Smith, 2002) that agrees with that of Greber

regarding the overall retention of ne suggests that the ne does not disappear if the

register of the language is at a high level or if the tone of the language is serious (p. 25).

Numerous studies have been conducted by sociolinguists on the prevalence of

ne retention in different geographic (francophone) locations and in different contexts

(interviews, radio, online forums, etc.). See Tables 4, 5, and 6 for an overview of

selected studies on the variable use of ne.

Table 4

Regional Variation in Spoken French (Adapted from Armstrong & Smith, 2002, p. 28)

Source (Year published) Data Collection Place % ne Pohl (1968) In the 1950s Belgium 61.9% Sankoff & Vincent (1980) 1971 Montreal 50.0% Ashby (1976) 1967-8 Paris 55.8% Diller (1983) 1975 Bearn 65.7% Ashby (1981) 1976 Tours 36.6% Coveney (1996) 1980 Somme 18.8% Moreau (1986) 1982-3 Belgium 50.2% Pooley (1996) 1995 Rouge-Barres (Nord) 1.0% Ashby (2001) 1995 Tours 15.7%

15

Table 5

Age of Speaker and Ne Retention

Source Older

speakers Younger speakers

Overall retention rate

Ashby (1981) 52% 19% 37% Coveney (1996) 28.8% 8.4% 18.8% Ashby (2001) 25% 15% 18% Hansen & Malderez (2004) 22.3% 4.6% 8.2% Table 6 Retention Rates of Ne in Previous Studies: Second Negative Particle Type Ashby (1981) Coveney (1996) Armstrong &

Smith (2002) Hansen & Malderez (2004)

2nd neg. particle

N ne use N ne use N Ne use N ne use

Pas 2,330 33% 2,317 16.4% 1,748 70.5% 941 8.2%

Plus 127 51% 209 25.8% 85 77.6% 96 9.4%

Rien 104 34% 146 21.2% 57 80.7% 61 6.6%

Jamais 73 36% 84 26.2% 57 80.7% 35 11.4%

Que 115 59% 109 34.9% 60 95% 23 30.4%

Personne 20 75% 24 33.3% 3 33.3% 10 0.0%

Aucun N/A N/A 33 21.2% 50 82% N/A N/A

Multiple 24 41% N/A N/A N/A N/A N/A N/A 2.2 Influences on Ne Retention

Ne retention varies greatly according to the context. In general, the rate at which

the ne is dropped varies inversely with the degree of formality of the speech (Ashby,

2001). In certain contexts, ne is very often retained, particularly in scripted radio

broadcasts (Armstrong & Smith, 2002), in publicly issued government statements, in

16

French-language newspaper and magazine articles (nearly 100% ne retention), in

forums, and in moderated chat (Williams, 2009). However, in sociolinguistic interviews

with participants, ne varies greatly, and in nonmoderated chat, the retention of ne can

be as low as around 16% (van Compernolle, 2008; Williams, 2009). The prevalence of

ne retention is not simply a matter of age, gender or social class. This linguistic variable

is found in all different speakers, although at different rates (Ashby, 2001).

2.2.1 Linguistic Factors

The prevalence of ne retention generally relies on several factors, including

which second negative expression is used. More commonly used second negative

expressions, such as pas, are less likely to be accompanied by the ne (Armstrong &

Smith, 2002; Hansen & Malderez, 2004, p. 15; van Compernolle, 2008, p. 317;

Williams, 2009, p. 474).

Subject type is another factor that generally affects ne retention. For example,

when the subject is a common noun or proper noun, the ne is more often retained. But

the ne is generally dropped when there is a subject pronoun (Armstrong & Smith, 2002;

Hansen & Malderez, 2004, pp. 15 and 21).

A third factor that appears to affect the prevalence of ne retention is verb

tense/mood and the frequency of the verb. Imperative clauses in particular appear to

have a high ne retention rate. Armstrong and Smith (2002) reported a rate of 95%.

Commonly used verbs (e.g. être, avoir, savoir and pouvoir) retain the ne less frequently

than other verbs (Hansen & Malderez, 2004). There is also evidence that ne retention

can be influenced by the presence of an object pronoun (Hansen & Malderez, 2004, p.

17

15), although that was not studied here.

2.2.2 Individual (Macrosociological) Factors

A fourth factor that appears to affect the prevalence of ne retention is the age of

the speaker. There has been a marked difference in ne retention amongst different age

groups. In particular, young people drop the ne in everyday conversation much more

commonly than older speakers (Ashby, 2001). In Ashby’s research (conducted in 1976

and 1995), age was one of the most important influences on ne retention (Ashby, 2001).

He found approximately 25% ne retention for 51-64 year olds and approximately 13%

ne retention for 14-22 year olds for his 1995 data (as compared to 52% and 19%

respectively in 1976) (Ashby, 2001). Coveney also found that age had a significant

effect on ne retention. He found 29% ne retention for 50-60 year olds and 8% for 17-22

year olds (Coveney, 1996, p. 86). Interestingly enough, the retirees in Ashby’s research

had adopted a more casual form of speech and tended to drop the ne more readily than

those still in the workforce (Ashby, 2001, p. 19). Hansen and Malderez (2004) found

that age had a much more convincing effect on ne retention than social class. Since

both age and subject type have both been shown consistently to favor ne retention in

past studies, it is important to note that this is the only online chat conducted that

compares these two factors to determine which is more likely to favor ne retention.

There are two possible explanations for the difference between the age groups

according to Ashby: 1) that ne is disappearing from spoken language, just as it virtually

has from Quebec French (Sankoff & Vincent, 1981) and is just happening more quickly

with younger speakers than older speakers, or 2) that the older speakers are simply

18

choosing a more formal linguistic style (called la langue de dimanche 'the language of

Sunday') than the younger speakers who choose to use so-called "everyday" language

(Ashby, 2001; Blanche-Benveniste & Jeanjean, 1987). Does a speaker make linguistic

changes over time or make consistent choices throughout his life in regards to ne

retention? Does he become more formal as he ages or does he remain the same while

the following generation is less formal? If individuals change their linguistic behavior

throughout their lifetimes, but the community as a whole does not, the pattern can be

characterized as one of age grading" (Labov, 1994, p. 84).

For the present study, an apparent-time approach was adopted, whereas Ashby

(2001) conducted a real-time study by collecting data from the same participants at two

different points in time. The only sure way to determine if there has been a linguistic

change is to undertake a real-time study (Ashby, 2001). “If we confine our observations

to distributions in apparent time, we will detect those conditions that lead to a

differentiation of generations: that is, age grading and generational change. We will not

be able to distinguish between the two" (Labov, 1994, p. 84).

A fifth factor that may affect the prevalence of ne retention is seriousness of

subject, for example religion or education, among others (Hansen & Malderez, 2004, p.

21; Williams, 2009, p. 474). Subject matter of the participants was not examined in the

present study since the range of topics in synchronous chat tends to be vast and the

focus of participants almost always seems scattered.

A sixth factor that appears to affect the prevalence of ne retention is social class

(Ashby, 2001). In Ashby’s research conducted in 1976 and 1995, the lower

socioeconomic class had a much lower ne retention than the middle and upper classes

19

(Ashby, 2001). One of his young bourgeois participants had a 93% ne retention rate.

The research of Hansen and Malderez had less clear results, perhaps because they

based the definition of social class simply on level of education without considering

other factors (background, professional activities, network, etc.) (Hansen & Malderez,

2004, p. 19). Since the participants in my study were anonymous, their socioeconomic

status was not examined.

Interestingly enough, Ashby found no significant difference between ne retention

in gender (Ashby, 2001). Neither did other studies (Coveney, 1996, p. 87, Hansen &

Malderez, 2004, p. 18). Gender was not considered in the present study due to the

unavailability of information on this feature.

One final factor that may influence ne retention is the geographic origin of the

individual. Hansen and Malderez found that speakers who were born in the Paris region

or in the Oise region (6.4% ne retention) dropped the ne more commonly than speakers

who were raised in other parts of France (17% ne retention) (Hansen & Malderez, 2004,

p. 20). Since geographic origin of the participants was not known (unless they by

chance revealed it in the conversation), this factor was not part of the present study.

Ne retention appears to be a choice of register based on various factors of the

individual (e.g. age, gender, class, education, etc.) and others related to the context

(e.g., speaking to a peer vs. an employer). For example, in Ashby’s work, speakers

taped at home had lower ne retention rates than those taped at their office (Ashby,

2001, p. 16). Also see Bell (1981) for a summary of research based on speakers

changing degree of formality based on context and on their perception of the listener.

20

CHAPTER 3

METHODOLOGY

3.1 Description of the Corpus

The data (51,494 words from the vingtaine 'twentysomething' channel; 50,623

words from the cinquantaine 'fiftysomething' channel) are a subset of approximately

100,000 words from a corpus of one million words collected in 2008 from the public chat

server EuropNet. Data for this study was collected from two public non-moderated

French language general discussion chat rooms over a period of several days in May

2008. The two channels were chosen approximately one generation apart, which adds

to the conclusions that may be drawn (Labov, 1972, p. 4). Anyone with Internet access

was eligible to join the discussion. Very little is known about the participants, as it is an

anonymous chat room, except that they are likely twenty-something and fifty-something

year olds. The participants were likely about 30 years apart, which provides a

generational division between the two groups, as was done with Ashby’s research

(2001). Participants created a unique username and voluntarily gave little to no

information about themselves. There were a variety of topics discussed at all different

times of day and all days of the week. Enough data were analyzed from each channel to

have statistically viable results. Fewer days of data were collected from the

cinquantaine group because each day’s data was more extensive. Each contribution

added by a participant (i.e. the turn-taking) was longer in general than those in the

vingtaine group, which meant that there were more examples in each day of ne

retention or deletion.

21

Table 7

Total number of words

Date Channel Channel Vingtaine Cinquantaine

5/3/08 13,050 No data collected

5/7/08 4,206 4,688

5/8/08 11,308 No data collected

5/9/09 11,534 27,032

5/10/08 5,517 4,798

5/11/08 5,879 14,105

Total 51,494 50,623

Table 8

Data collection (2008)

Source Data Collection Dates

Vingtaine channel May 3, 7, 8, 9, 10, 11

Cinquantaine channel May 9, 10, 11

The chat discussions were saved as text-only files and imported into

Concordance software. The chat room corpus was chosen for the present study

because it represents spontaneous and unplanned discourse between people of nearly

the same age. I analyzed chat room exchanges unbeknownst to the participants. As the

participants did not know that the discourse would be analyzed, one can suppose that

their choice of vocabulary and register of language is the norm for the given context.

Every qualified occurrence of negation in the corpus was counted and coded to

determine the frequency of ne retention, as well as the subject pronoun used, the verb

22

tense used, and the second negative particle used. Several categories of negation were

not counted as a part of this study, as they have been considered statistically

insignificant by other research and have not become standard in data collection of this

kind.

The chat corpus was studied in order to understand if participants use the ne

or pas—if they participate orthographically as if it is a written or spoken form of

communication. According to the study by Fonseca-Greber (2007), the age of

participants influenced the frequency of the usage of ne in spoken language. Older

people more often use the ne while speaking than younger. Two nonmoderated chat

rooms were chosen where the age of the participants is different. The analysis of the

quantitative method explores the context in order to understand how the ne functions in

nonmoderated chat.

It certainly would provide insightful to have a wide range of demographic

information on the participants, but this was not possible. It could be a beneficial study

in the future to have recruited participants interviewed beforehand. Demographic

information could be collected, yet participants could still be uninformed as to the exact

topic of the linguistic study so that they would not willingly (or without realizing it)

change their (socio)linguistic behavior, participation, or actions.

3.2 Coding

Each occurrence of negation was counted and coded according to the dependent

variable (absence or presence of ne) and according to four independent variables: 1)

second negative particle, 2) subject, 3) verb tense/mood, 4) vingtaine or cinquantaine

23

chat room. The variables were coded according to the following coding scheme to

prepare it for the GoldVarb X (Sankoff et al. 2005) so that VARBRUL analysis could be

done. The goal of using the GoldVarb X was to determine the influence that each

independent variable had on the dependent variable. The greater the weight of the

variable, the greater the probability that the variable had an influence on the absence or

presence of ne. The variables with a weaker weight have lower probability of having any

influence on ne retention. Appendix A shows the coding (including an example) used for

the dependent and independent variables.

3.3 Examples of Ne from the Corpus

As one can see in Table 9, the participants are treating the synchronous non-

moderated forum as both an opportunity to retain the ne and as an opportunity to drop

the ne. (Segments of verbal negation are in bold.)

Table 9

Vingtaine (V) and Cinquantaine (C) Channel Excerpts: Ne Absence/Presence

Date (Chan.) Time Excerpt May 3 (V) [07:15] <El-KiwiBiscuit> je ne te vois pas May 3 (V) [07:30] <BideRempliTendu59> cest clair que cest pas moi… :p May 7 (C) [01:58] <coralie> stixx> j’ai rien dis moi :) May 7 (C) [02:02] <stixx> canopee> jte signale qu’on ne dit pas vieux ….

For this study, whenever a negation was not associated with a subject and a

conjugated verb, it was not counted since within a variationist framework, tokens are

only included from contexts where the same thing can be expressed or realized in (at

least) two different ways. The use of pas + adjective/adverb (e.g., pas mal 'not

24

bad'/'quite a few'; pas vraiment 'not really') that is not part of verbal negation, [ne . . .

pas 'not' + infinitive] and fixed expressions (e.g. n’est-ce pas? 'isn't that so?') were all

excluded, as well as any instance where the ne appeared without a second particle.

Expressions with ne and no second particle are not variable, which is the reason for

their exclusion from the coding and analysis. Other times, the ne deletion appeared to

be accidental on the part of the participant. Partial clauses without either a conjugated

verb or a subject were also excluded, as were tokens where what normally serves as

the second particle of the negative expression now serves as the subject or a

determiner/quantifier (e.g. Personne ne sait 'No one knows'; Aucune femme ne me

répond 'No woman is responding to me').

In Table 10 (below), examples are provided of exclusions from the analysis, with

certain items underlined to draw attention to the variation.

25

Table 10

Vingtaine Channel Excerpts from May 3, 2008

Time Excerpt Reason for exclusion [07:10] <binouze> pas assez bavarde lol

(not chatty enough lol) pas + adverb

[07:31] <Mrs `Dan `Carter34> Nan jveux pas :p (No, I don’t want)

incomplete subject

[07:36] <RockDancer> lol ya que moi qui en porte . . . (lol, it’s only me who is wearing it . . .)

no overt subject

[09:07] <moi> c pas drôle (that’s not funny)

syllabogram's morphology excludes possibility of variation

[09:10] <photograve> désolé, je ne peu me retenir . . . (sorry, I can’t hold myself back . . .)

lexicalized expression with no second negative particle

[13:54] <beaudelair> liinza> j’escomptais ne pas avoir a arriver… (I was counting on not having to arrive . . .)

negative infinitive

[14:01] <danseur-sans-charentaises> faut pas le faire :p (shouldn’t do it)

no subject

Other examples of negation that were not included in the data were lexicalized

expressions (e.g. n’est-ce pas? ‘isn’t that right?’), present participle verbs, and where

the second negative particle served as the subject or the determiner.

3.4 Variable Rule Analysis The variable rule (VARBRUL) application is “one of the most appropriate

methods available for conducting statistical analysis on natural speech” because it

makes the data accessible and organized (Tagliamonte, 2006, p. 129). GoldVarb X, the

26

version of software that was used for this research has been available since October

2005 (Tagliamonte, 2006, p. 158).

The variationist method of sociolinguistics is attributed to William Labov (1969),

who observed “speakers make choices when they use language, and further, that this

choice is systematic. Due to the systemacity of the process, the relative frequency of

selection can be predicted” (Tagliamonte, 2006, p. 130). The variable rule was

“designed as an accountable, empirical model for this phenomenon, thus introducing a

probabilistic component into the model of language” (Tagliomonte, 2006, p. 130). The

choice process is affected by many internal and external factors (Labov 1969, p. 759).

There are three prerequisites for variable rule analysis: 1) choice between two or

more sounds or words, 2) unpredictability and 3) recurrence (Sankoff 1988, p. 984).

Linguistic choice is a key component to VARBRUL:

Whenever a choice among two (or more) discrete alternatives can be perceived

as having been made in the course of linguistic performance, and where this

choice may have been influenced by factors such as: features in the phonological

environment, the syntactic context, discursive function of the utterance, topic,

style, interactional situation, personal or socio-demographic characteristics of the

speaker, other participants, then it is appropriate to invoke the statistical notions

and methods known to students of linguistic variation as “variable rules” (Sankoff,

1988, p. 984).

Once these three conditions are met, statistical inference can be made

(Tagliamonte, 2006, p. 131). The linguistic choice of the speaker being measured is

called the dependent variable. The linguistic or external factors, which may affect the

27

dependent variable, are called independent variables, factors, or factor groups

(Tagliamonte, 2006, p. 131). The record of which choice was actually made is called

the variant. Each instance where the variable appears is called a token. (Tagliamonte,

2006, p. 165)

3.5 Levels of Analysis in GoldVarb X

The first step in conducting variable rule is to establish that there is indeed an

effect on the linguistic choice. The null hypothesis has to be falsified (Tagliamonte,

2006, p. 132). “The null hypothesis is that none of the factors has any systematic effect

on the choice process and that any differences in the choice outcome among the

various contexts is attributed to statistical fluctuation” (Tagliamonte, 2006, p. 132).

However, “if we can prove that random processes alone are unlikely to have resulted in

the pattern of proportions observed, we may be able to attribute this pattern to the effect

of one or more of the factors” (Sankoff, 1988, pp. 987-992).

The second step in variable rule is to determine which set of factors will likely

influence the dependent variable. Here, the “likelihood criterion” is used because it

accounts for the “extreme distributional imbalances, including contrasting full versus

near-empty cells, in corpus-based data” (Tagliamonte, 2006, p. 133). The likelihood

criterion is vital to determine which factors will be a best fit to the data (Tagliamonte,

2006, p. 133). In this particular study, the factors determined to be the most likely to

have an effect on the dependent variable (ne retention) based upon a review of current

relevant research were second negative particle, subject type, verb tense/mood, and

age of the speaker. Factor groups should be independent of each other. (Tagliamonte,

28

2006, p. 181)

The third step in variable rule is to begin to analyze the data. It is important to

keep in mind that although the variable rule program is a very useful tool, it has

limitations: “VARBRUL only performs mathematical manipulations on a set of data. It

does not tell us what the numbers mean, let alone do linguistics for us (Guy, 1988, p.

133).” “. . . statistical analysis must be informed by linguistic insights.” (Tagliamonte,

2006, p. 225) In this study, GoldVarb X software was used. There are two ways to

conduct analysis of data with GoldVarb: 1) binomial one-step and 2) binomial step-

up/step-down (Tagliamonte, 2006, p. 139).

In binomial one-step analysis, the computation is done one step at a time. All

cells and all combinations are analyzed at the same time. This allows the researcher to

examine each cell to see how the combination appears and to see which cells fit the

model the best and the least (Tagliamonte, 2006, pp. 139-140). The cells that fit the

model the least can be excluded as exceptions (Tagliamonte, 2006, p. 140). A

*KnockOut* is when there is a zero percent or a 100 percent value in one of the cells of

the analysis. A variable rule analysis cannot be done in this case because there is no

variation in data and thus, these should be eliminated. (Tagliamonte, 2006, p. 152)

Another instance is with a Singleton Group, wherein there is only one token in the cell.

“Highly infrequent lexical items” either have to be removed or collapsed into another

category to successfully continue with the analysis. (Tagliamonte, 2006, p. 223)

The binomial step-up/step-down method is more often employed than the one-

step analysis because it provides the researcher three lines of evidence, each of which

are instrumental to interpreting the data: 1) statistical significance, 2) relative strength

29

(measured by the range, calculated by subtracting the lowest factor weight from the

highest) and 3) constraint ranking of factors (i.e. the hierarchy of categories within a

factor group) (Tagliamonte, 2006, pp. 140, 237, 242). VARBRUL begins with the step

up and then does the step down. The factor groups selected during stepping up are

significant, whereas the factors eliminated during stepping down are not significant.

(Tagliamonte, 2006, p. 251)

The first step in fitting the model to the data is to find the group that makes the

most significant change to the model when it is added or subtracted from the

rest. All factor groups are tested, in order to determine which one increases the

likelihood most significantly. The program retains the most significant group and

tries to add a second group, which increases the likelihood as significantly as

possible. It continues in this way until no further additions result in a statistically

significant improvement. The collection of groups incorporated in the model this

way is referred to as the step-up solution. (Tagliamonte, 2006, p. 140)

Once the step-up is completed, the step-down is done using the same principle,

but in reverse order: “The program starts by calculating the likelihood of the model when

all the factor groups are included in the regression simultaneously. Thereafter, it

discards the group whose loss least significantly reduces the likelihood (using the chi-

square test)” (Tagliamonte, 2006, p. 143). When a functional category is removed and

the log likelihood (a measurement of how accurate the analysis is) improves (moves

closer to zero), this is a factor group that has less of an effect on the dependent variable

(Tagliamonte, 2006, pp. 144, 156). When the data run returns to level #1, each factor is

tested independently and the “best run” is determined by GoldVarb. (Tagliamonte, 2006,

30

p. 146) The results for the step-up and the step-down should be the same and should

have the same “best run”. (Tagliamonte, 2006, pp. 145, 228, 235) The error measures

the accuracy with which the predicted data match the observed data (Tagliamonte,

2006, p. 156).

Interaction between factor groups may be visible during the step-up/step-down

process (Tagliamonte, 2006, p. 151). If during the process of step-up/step-down, there

is a large change, a cross-tabulation of factor groups can be conducted to verify

whether or not the two factor groups interact. “Cross tabulation is a key element of

variation analysis…you may uncover some of the most important findings.”

(Tagliamonte, 2006, p. 151) Varbrul analysis is the only way to compare different factors

and their respective influence on the dependent factor. All factor groups are cross-

tabulated with each other so that any interaction, empty cell, coding error, or anomaly

can be observed. (Tagliamonte, 2006, pp. 182, 220) The cross-tabulation process

produces a grid that will show the resulting interaction of each of the factor groups when

they are combined with another factor group (Tagliamonte, 2006, p. 207). This

information can be used to help determine the likelihood that the factor group affects ne

retention.

Factor weights should be considered in the analysis and can be valued from 0 to

1. “When a factor weight is closer to 1, it is interpreted as ‘favouring’ the application

value, whereas if it is closer to zero it is interpreted as ‘disfavouring’ the application

value…it is the relative position of factor weights, vis-à-vis each other, that is the

relevant criterion for interpreting the results” (Tagliamonte, 2006, p. 145). In other

words, the factor weights are a relative measure (compared to other factors in the same

31

factor group) of the probability of ne retention.

Degrees of freedom should also be considered in the analysis. These are “the

number of independent pieces of information available or used in an analysis of the

observed data” (Paolillo 2002, p. 109). “The more factors that are involved in a variable

rule analysis, the greater the degrees of freedom” (Tagliamonte, 2006, pp. 148-149).

Convergence is the ideal at each run of the step-up/step-down analysis;

however, this does not always occur even after multiple runs “where variation is so

infrequent, an accurate statistical model is difficult” (Tagliamonte, 2006, pp. 153-154).

32

CHAPTER 4

RESULTS AND ANALYSIS

4.1 Residuals (Percentages): Distribution of Ne Absence/Presence

In all, 1,031 occurrences of verbal negation were found in the corpus, of which

1,015 were retained for the statistical analysis (after knockouts and singletons were

removed). This includes 405 occurrences with two-particle negation (39.9%) and 610

occurrences with single-particle negation (60.1%). There were 566 total tokens in the

vingtaine chat channel with 173 instances (30.6%) of second particle negation. There

were 449 total tokens collected in the cinquantaine chat room. Of these, there were 232

occurrences (51.7%) of ne retention. Table 11 shows the frequency of negative tokens

according to each independent variable in descending order. Pas has by far more

negative tokens (828) than all the other second negative particles combined. The same

is true in the verb tense/mood category—the present indicative has by far more

negative tokens (747) than all other verb moods/tenses combined. This is consistent

with previously conducted research (Hansen & Malderez, 2004, pp. 22-24) of

spontaneous discourse. The number of tokens of pas and the number of tokens of the

present indicative so exceeds the number of tokens for any other variant in their

respective categories that it is difficult to make a statistical comparison.

33

Table 11

Percentages of Ne Variation from the Original Coding

Factor Total Tokens Ne present Independent Variable 1: Negation Type

pas 828 323 (39.0%) rien 59 20 (33.9%) plus 53 22 (41.5%) jamais 36 17 (47.2%) que 33 22 (66.7%) personne 7 3 (42.9%) plus rien 6 3 (50.0%) point 4 4 (100.0%)* ni...ni 2 0 (0.0%) aucun(e) 2 1 (50.0%) jamais rien 1 1 (100.0%)*

Independent Variable 2: Subject Type je 421 154 (36.6%) tu 198 81 (40.9%) on 80 27 (33.8%) il, elle 79 38 (48.1%) ce 65 19 (29.2%) common noun 64 41 (64.1%) ça 56 16 (28.6%) proper noun 26 16 (61.5%) ils, elles 16 10 (62.5%) vous 10 3 (30.0%) nous 7 7 (100.0%)* qui 5 1 (20.0%) celui, celle, etc. 4 3 (75.0%)

Independent Variable 3: Verb Mood/Tense pres. indicative 747 302 (40.4%) present perfect 84 31 (36.9%) imperative 64 26 (40.6%) imperfect 44 18 (40.9%) near future 25 5 (20.0%) conditional 24 13 (54.2%) simple future 23 10 (43.5%) pres. subjunctive 9 4 (44.4%) pluperfect 7 4 (57.1%) past conditional 4 3 (75%) * indicates a Knock-Out

(table continues)

34

Table 11 (continued).

Independent Variable 4: Age Group vingtaine 573 178 (31.1%) cinquantaine 458 238 (52.0%) TOTAL 1,031 416 (40.3%)

4.2 Cross Tabulation

Appendix C shows the cross tabulations with all data. It is evident that there are

many empty cells because of the domination of the second negative particle pas and

the present indicative tense in their respective categories. Independent Variable 1

(second negation type) and Independent Variable 3 (verb mood/tense) could not be

used in the statistical analysis since there were such a small number of tokens for all

negation types (except pas) and for verb moods/tenses (except present indicative).

Since pas and present indicative dominated in their respective factor groups, their

categories were eliminated. This allowed for the data to be represented without

Variable 1 and 3 in Table 12 (again shown in descending order of negative tokens). In

addition, for Variables 2 (subject type) and 4 (age group), factors that had fewer than

ten tokens of negation were eliminated because analysis could not proceed with so

many empty cells. The data was re-coded and re-run after eliminating Variables 1 and

3, as well as knockouts and singletons (see also Appendix E).

35

Table 12 Percentages of Ne Variation: Independent Variables 2 and 4

Factor Total Tokens Ne presence

Independent Variable 1 (Negation Type): Eliminated

Independent Variable 2 (Subject Type)

Je 421 154 (36.6%)

Tu 198 81 (40.9%)

Noun 90 57 (63.3%)

On 80 27 (33.8%)

il, elle 79 38 (48.1%)

Ce 65 19 (29.2%)

Ça 56 16 (28.6%)

proper noun [Combined with "common noun" to form "noun"]

ils, elles 16 10 (62.5%)

Vous 10 3 (30.0%)

Nous Eliminated (knockout: no variation)

Qui Eliminated (only 5 tokens produced)

celui, celle, etc. Eliminated (only 4 tokens produced)

Independent Variable 3 (Verb Mood/Tense): Eliminated

Independent Variable 4: Age Group

Vingtaine 566 173 (30.6%)

cinquantaine 449 232 (51.7%)

TOTAL 1,015 405 (39.9%)

4.3 One-Level Binomial Analysis

There are three indicators that must be present before the data analysis can be

continued: 1) crosstabs with no empty cells, 2) a good chi-square number, and 3) a

scattergram of data that are mostly along the regression line. After running the data

36

without the first variable (second negative particle type) and third variable (verb

mood/tense), I looked at the crosstabs and saw that there were no empty cells (see

Appendix F).

With no empty cells in the crosstabs, the chi-square number was then checked

(16.0173; see Appendix E). For the current study, this should ideally be below 16.919

(see Preston, 1996, for criteria; see also Paolillo, 2001). The lower the chi-square

number (based on data patterns and significance value), the better the scattergram

results (i.e., the closer the data appear to the regression line).

Since the chi-square value was acceptable, the scattergram was then checked.

Figure 1 is a scattergram of the data along a regression line. This provides a visual

representation of the data (Tagliamonte, 2006, p. 224). Ideally, everything groups

around the regression line, as it does here. "The size of the point is proportional to the

number of tokens in the corresponding cell(s)" (Rand & Sankoff, 1990, p. 24). This is the

third indication that it is acceptable to continue to the step-up/step-down analysis.

37

Figure 1 Scattergram of recoded data along a regression line.

There are now three indicators that the data analysis can proceed: 1) no empty

cells in the output; 2) an acceptable chi-square number; and 3) a scattergram of data

that congregate along the regression line.

4.4 Step-Up/Step-Down Analysis

I conducted the step-up/step-down analysis, the actual statistical test (see

Appendix G for the step-up analysis and Appendix H for the step-down analysis). The

stepping up process and stepping down process check for the most statistically

significant dependent variable (i.e. the one that has the highest probability of affecting

the independent variable ne). In the stepping up process, the Goldvarb X software runs

the variables first individually and then as a group to see if it becomes more statistically

significant with each additional variable.

During the stepping up analysis, Run 4 (in which independent variables subject

38

type and age were combined) was found to be the most statistically significant. During

the stepping down analysis, the most statistically significant run was 5 (which is the

same run—the last in step-up and the first in step-down, since step-down is done in the

reverse order). In addition, there were no groups eliminated while stepping down. In

other words, neither of the variables (subject type and age) was found to be statistically

insignificant, therefore indicating that both variables are statistically significant. The

prevalence of ne retention was more statistically significant when these variables were

run together than when they were run separately. The log likelihood is the closest to

zero in Run 4/5 (-643.277) compared with the other data runs.

Table 13 shows the VARBRUL analysis of subject type and age group. The

differences between factor weights within each group are statistically significant. A

higher number indicates a higher probability of influencing ne retention relative to other

factors in the same group.

39

Table 13 VARBRUL Analysis __________________________________________________________________ Variable Double-Particle Negation Total FW __________________________________________________________________ Subject Type common/proper noun 57 (63.3%) 90 .718 ils, elles 10 (62.5%) 16 .692 il, elle 38 (48.1%) 79 .596 tu 81 (40.9%) 198 .520 je 154 (36.6%) 421 .458 on 27 (33.8%) 80 .437 vous 3 (30.0%) 10 .421 ce, c' 19 (29.2%) 65 .400 ça 16 (28.6%) 56 .399 (Range: 319) Age Group cinquantaine 232 (51.7%) 449 .619 vingtaine 173 (30.6%) 566 .405 (Range: 214) __________________________________________________________________ Total 405 (39.9%) 1,015 __________________________________________________________________ Log Likelihood = -643.277; Input (corrected mean) = 0.393; Total Chi-Square = 16.0173

4.5 Statistical Significance

Table 9 shows several variants have factor weights above 0.5, indicating that ne

retention is favored relative to each other factor in the same group. Factor weights at or

above 0.5 in each group are in boldface type in order to draw attention to the division

between factors that favor or disfavor application of the rule (within each factor group).

In the Subject Type category, common/proper noun, ils/elles, il/elle, and tu each have

factor weight values approximately 0.5 and so would have a higher probability of

influencing ne retention than je, on, vous, ce/c’ and ça. In the Age factor group, the

cinquantaine group (factor weight .619) favors ne retention more than the vingtaine

40

group (factor weight .405). Goldvarb X shows subject type to be more statistically

significant than age group because it has a greater range (319 versus 214).

To find the degrees of freedom, simply subtract the number of groups (2) from

the number of factors (11). There are 9 degrees of freedom. That is relatively low since

there are only two factor groups.

While age is influential, its likelihood to affect ne retention is lower than subject

type. This significant influence on negation is consistent with van Compernolle’s

analysis of synchronous non-moderated chat in 2007 and the study by Williams in 2009.

4.6 Ranges

In order to find the range of a statistically significant factor group in the binomial

analysis, subtract the lowest factor weight from the highest. The group of data with the

highest range more greatly influences the variation. In Appendix G, in Run 4 (the most

statistically significant group where subject type and age were combined), the range can

be found by looking at both Group 1 (subject type) and Group 2 (age). Then a

comparison can be made between the two ranges to determine which has a higher

probability of favoring ne retention. As you can see in Table 9, in Group 1, subtract the

lowest factor weight (399) from the highest factor weight (718) for a range of 319. In

Group 2, subtract the lowest factor weight (405) from the highest factor weight (619) for

a range of 214. Group 1’s range (319) is greater than Group 2’s range (214). This

means that subject type has a relatively higher probability of favoring ne retention than

age. One explanation for this would be that high frequency subjects (e.g. je or I) tend to

be used more casually and so the ne is dropped more frequently.

41

CHAPTER 5

CONCLUSION

The results of the analysis have shown that age group was a significant factor

in the variable use of ne, but subject type had an even greater influence on ne absence

or presence. The ne in verbal negation is more often retained by the cinquantaine group

than by the vingtaine group, as might be expected based on previous research. A

common or proper noun was the most likely variable in this study to affect ne retention.

The results of this study conform to what could have been expected for this

context. The results show us that non-moderated chat conforms to the informality of

spoken French in terms of ne retention. There is also a clear difference in ne retention

between the two age groups. This was a context of casual written conversation, where

negative second particles were regularly typed incorrectly and the ne was regularly

dropped. This corresponds to the non-moderated chat room findings done previously by

van Compernolle (2007). As is typical with other research, the ne was dropped more

commonly with pas than it was with other negative expressions.

For further research, it would be interesting to continue the work of investigation

of ne retention in synchronous non-moderated chat rooms and consider the influence of

inversion, object pronouns, and seriousness of topic. Other sources for analysis may

include blogs, curriculum vitaes, job application cover letters, general business

correspondence (online and traditional) and complaint correspondence (online and

traditional). Some data (particularly those related to professional networking) may

provide significant demographic data for further analysis.

Another possibility would be to conduct the same research in a controlled

42

environment where participants are interviewed beforehand, but not given information

about the specifics of the study. Then other sociological factors could be considered.

43

APPENDIX A

DATA CODING

44

Dependent variable 0 Absence of ne 1 Presence of ne

Independent variable 1 (second negative particle) R Pas W Rien H Jamais B Personne Q Que X Plus G Plus rien Y Aucun(e) Z Point = Ni ni * Jamais rien

Independent variable 2 (subject) C Common noun P Proper noun E Ce/c’ A ça D Celui/celle/ceux/celles J Je T Tu N Nous V Vous I Il/elle S Ils/elles O On K Qui

Independent variable 3 (verb tense/mood) F Present indicative L Imperfect M Present perfect U Simple future 3 Pluperfect 4 Conditional 6 Conditional past 7 Near future 8 Imperative

45

9 Subjunctive present

Independent variable 4 (age group) 2 Vingtaine chat channel 5 Cinquantaine chat channel For example, a sentence like Je n’aime pas (I don’t like) from the fifty something chat

room would be coded the following:

1RJF5 1 = ne present R = pas was the second negative particle J = je was the subject pronoun F = verb aime is in the present indicative 5 = data is from the fifty something chat room

46

APPENDIX B

BINOMAIL ONE-STEP ANALYSIS WITH ALL DATA

47

Number of cells: 191; Application value(s): 1; Total no. of factors: 36 Non- Group Apps apps Total % -------------------------------------- 1 (2) r N 323 505 828 80.3 % 39.0 61.0 w N 20 39 59 5.7 % 33.9 66.1 h N 17 19 36 3.5 % 47.2 52.8 q N 22 11 33 3.2 % 66.7 33.3 x N 22 31 53 5.1 % 41.5 58.5 g N 3 3 6 0.6 % 50.0 50.0 b N 3 4 7 0.7 % 42.9 57.1 z N 4 0 4 0.4 % 100.0 0.0 * KnockOut * = N 0 2 2 0.2 % 0.0 100.0 * KnockOut * y N 1 1 2 0.2 % 50.0 50.0 * N 1 0 1 0.1 % 100.0 0.0 * KnockOut * Total N 416 615 1031 % 40.3 59.7 -------------------------------------- 2 (3) t N 81 117 198 19.2 % 40.9 59.1 j N 154 267 421 40.8

48

% 36.6 63.4 c N 41 23 64 6.2 % 64.1 35.9 e N 19 46 65 6.3 % 29.2 70.8 o N 27 53 80 7.8 % 33.8 66.2 i N 38 41 79 7.7 % 48.1 51.9 p N 16 10 26 2.5 % 61.5 38.5 a N 16 40 56 5.4 % 28.6 71.4 d N 3 1 4 0.4 % 75.0 25.0 s N 10 6 16 1.6 % 62.5 37.5 n N 7 0 7 0.7 % 100.0 0.0 * KnockOut * k N 1 4 5 0.5 % 20.0 80.0 v N 3 7 10 1.0 % 30.0 70.0 Total N 416 615 1031 % 40.3 59.7 -------------------------------------- 3 (4) l N 18 26 44 4.3 % 40.9 59.1 8 N 26 38 64 6.2 % 40.6 59.4 f N 302 445 747 72.5

49

% 40.4 59.6 u N 10 13 23 2.2 % 43.5 56.5 m N 31 53 84 8.1 % 36.9 63.1 4 N 13 11 24 2.3 % 54.2 45.8 7 N 5 20 25 2.4 % 20.0 80.0 9 N 4 5 9 0.9 % 44.4 55.6 3 N 4 3 7 0.7 % 57.1 42.9 6 N 3 1 4 0.4 % 75.0 25.0 Total N 416 615 1031 % 40.3 59.7 -------------------------------------- 4 (5) 2 N 178 395 573 55.6 % 31.1 68.9 5 N 238 220 458 44.4 % 52.0 48.0 Total N 416 615 1031 % 40.3 59.7 -------------------------------------- TOTAL N 416 615 1031 % 40.3 59.7 Name of new cell file: .cel

50

APPENDIX C

CROSS TABULATIONS

51

Group #1 – horizontally Group #2 -- vertically r % w % h % q % x % g % b % z % = % y % * % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - t 1: 72 42: 3 30: 1 17: 0 0: 4 57: 0 --: 0 0: 0 --: 0 --: 0 --: 1 100| 81 41 -: 100 58: 7 70: 5 83: 1 100: 3 43: 0 --: 1 100: 0 --: 0 --: 0 --: 0 0| 117 59 ∑: 172 : 10 : 6 : 1 : 7 : 0 : 1 : 0 : 0 : 0 : 1 | 198 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - j 1: 118 37: 9 26: 7 50: 7 58: 8 30: 1 33: 1 25: 2 100: 0 0: 1 50: 0 --| 154 37 -: 203 63: 26 74: 7 50: 5 42: 19 70: 2 67: 3 75: 0 0: 1 100: 1 50: 0 --| 267 63 ∑: 321 : 35 : 14 : 12 : 27 : 3 : 4 : 2 : 1 : 2 : 0 | 421 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - c 1: 37 54: 4 100: 2 100: 9 90: 3 75: 0 --: 0 --: 2 100: 0 --: 0 --: 0 --| 57 63 -: 31 46: 0 0: 0 0: 1 10: 1 25: 0 --: 0 --: 0 0: 0 --: 0 --: 0 --| 33 37 ∑: 68 : 4 : 2 : 10 : 4 : 0 : 0 : 2 : 0 : 0 : 0 | 90 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - e 1: 17 28: 0 0: 0 --: 2 67: 0 --: 0 --: 0 --: 0 --: 0 0: 0 --: 0 --| 19 29 -: 43 72: 1 100: 0 --: 1 33: 0 --: 0 --: 0 --: 0 --: 1 100: 0 --: 0 --| 46 71 ∑: 60 : 1 : 0 : 3 : 0 : 0 : 0 : 0 : 1 : 0 : 0 | 65 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - o 1: 16 29: 1 25: 3 33: 2 50: 4 67: 1 50: 0 --: 0 --: 0 --: 0 --: 0 --| 27 34 -: 39 71: 3 75: 6 67: 2 50: 2 33: 1 50: 0 --: 0 --: 0 --: 0 --: 0 --| 53 66 ∑: 55 : 4 : 9 : 4 : 6 : 2 : 0 : 0 : 0 : 0 : 0 | 80 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - i 1: 26 43: 3 100: 2 67: 2 67: 2 29: 1 100: 2 100: 0 --: 0 --: 0 --: 0 --| 38 48 -: 34 57: 0 0: 1 33: 1 33: 5 71: 0 0: 0 0: 0 --: 0 --: 0 --: 0 --| 41 52 ∑: 60 : 3 : 3 : 3 : 7 : 1 : 2 : 0 : 0 : 0 : 0 | 79 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - a 1: 14 27: 0 0: 1 100: 0 --: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 16 29 -: 38 73: 2 100: 0 0: 0 --: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 40 71 ∑: 52 : 2 : 1 : 0 : 1 : 0 : 0 : 0 : 0 : 0 : 0 | 56 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - s 1: 10 67: 0 --: 0 --: 0 --: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 10 62 -: 5 33: 0 --: 0 --: 0 --: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 6 38 ∑: 15 : 0 : 0 : 0 : 1 : 0 : 0 : 0 : 0 : 0 : 0 | 16 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - -

52

v 1: 3 30: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 3 30 -: 7 70: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 7 70 ∑: 10 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 | 10 +---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 313 38: 20 34: 16 46: 22 67: 22 42: 3 50: 3 43: 4 100: 0 0: 1 50: 1 100| 405 40 -: 500 62: 39 66: 19 54: 11 33: 31 58: 3 50: 4 57: 0 0: 2 100: 1 50: 0 0| 610 60 ∑: 813 : 59 : 35 : 33 : 53 : 6 : 7 : 4 : 2 : 2 : 1 | 1015 • CROSS TABULATION Group #1 -- horizontally. Group #3 -- vertically. r % w % h % q % x % g % b % z % = % y % * % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - l 1: 15 39: 0 --: 0 --: 2 100: 1 25: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 18 41 -: 23 61: 0 --: 0 --: 0 0: 3 75: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 26 59 ∑: 38 : 0 : 0 : 2 : 4 : 0 : 0 : 0 : 0 : 0 : 0 | 44 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 8 1: 22 37: 0 --: 0 --: 0 --: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 23 38 -: 38 63: 0 --: 0 --: 0 --: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 38 62 ∑: 60 : 0 : 0 : 0 : 1 : 0 : 0 : 0 : 0 : 0 : 0 | 61 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - f 1: 234 39: 16 39: 6 40: 16 62: 16 40: 2 40: 2 33: 3 100: 0 0: 1 50: 0 --| 296 40 -: 363 61: 25 61: 9 60: 10 38: 24 60: 3 60: 4 67: 0 0: 1 100: 1 50: 0 --| 440 60 ∑: 597 : 41 : 15 : 26 : 40 : 5 : 6 : 3 : 1 : 2 : 0 | 736 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - u 1: 4 36: 0 --: 2 50: 1 50: 3 50: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 10 43 -: 7 64: 0 --: 2 50: 1 50: 3 50: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 13 57 ∑: 11 : 0 : 4 : 2 : 6 : 0 : 0 : 0 : 0 : 0 : 0 | 23 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - m 1: 17 35: 3 19: 8 53: 0 --: 0 --: 0 --: 1 100: 0 --: 0 0: 0 --: 1 100| 30 36 -: 32 65: 13 81: 7 47: 0 --: 0 --: 0 --: 0 0: 0 --: 1 100: 0 --: 0 0| 53 64 ∑: 49 : 16 : 15 : 0 : 0 : 0 : 1 : 0 : 1 : 0 : 1 | 83 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 4 1: 8 44: 1 100: 0 --: 2 100: 1 50: 0 --: 0 --: 1 100: 0 --: 0 --: 0 --| 13 54 -: 10 56: 0 0: 0 --: 0 0: 1 50: 0 --: 0 --: 0 0: 0 --: 0 --: 0 --| 11 46

53

∑: 18 : 1 : 0 : 2 : 2 : 0 : 0 : 1 : 0 : 0 : 0 | 24 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 7 1: 3 14: 0 0: 0 --: 1 100: 0 --: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --| 5 20 -: 19 86: 1 100: 0 --: 0 0: 0 --: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --| 20 80 ∑: 22 : 1 : 0 : 1 : 0 : 1 : 0 : 0 : 0 : 0 : 0 | 25 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 9 1: 3 38: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 3 38 -: 5 62: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 5 62 ∑: 8 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 | 8 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 3 1: 4 67: 0 --: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 4 57 -: 2 33: 0 --: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 3 43 ∑: 6 : 0 : 1 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 | 7 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 6 1: 3 75: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 3 75 -: 1 25: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 1 25 ∑: 4 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 0 | 4 +---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 313 38: 20 34: 16 46: 22 67: 22 42: 3 50: 3 43: 4 100: 0 0: 1 50: 1 100| 405 40 -: 500 62: 39 66: 19 54: 11 33: 31 58: 3 50: 4 57: 0 0: 2 100: 1 50: 0 0| 610 60 ∑: 813 : 59 : 35 : 33 : 53 : 6 : 7 : 4 : 2 : 2 : 1 | 1015 • CROSS TABULATION Group #1 -- horizontally. Group #4 -- vertically. r % w % h % q % x % g % b % z % = % y % * % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 2 1: 134 29: 8 23: 7 30: 10 59: 9 35: 0 0: 2 50: 3 100: 0 0: 0 --: 0 --| 173 31 -: 321 71: 27 77: 16 70: 7 41: 17 65: 1 100: 2 50: 0 0: 2 100: 0 --: 0 --| 393 69 ∑: 455 : 35 : 23 : 17 : 26 : 1 : 4 : 3 : 2 : 0 : 0 | 566 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 5 1: 179 50: 12 50: 9 75: 12 75: 13 48: 3 60: 1 33: 1 100: 0 --: 1 50: 1 100| 232 52 -: 179 50: 12 50: 3 25: 4 25: 14 52: 2 40: 2 67: 0 0: 0 --: 1 50: 0 0| 217 48 ∑: 358 : 24 : 12 : 16 : 27 : 5 : 3 : 1 : 0 : 2 : 1 | 449 +---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 313 38: 20 34: 16 46: 22 67: 22 42: 3 50: 3 43: 4 100: 0 0: 1 50: 1 100| 405 40

54

-: 500 62: 39 66: 19 54: 11 33: 31 58: 3 50: 4 57: 0 0: 2 100: 1 50: 0 0| 610 60 ∑: 813 : 59 : 35 : 33 : 53 : 6 : 7 : 4 : 2 : 2 : 1 | 1015 • CROSS TABULATION Group #2 -- horizontally. Group #3 -- vertically. t % j % c % e % o % i % a % s % v % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - l 1: 1 33: 11 52: 1 17: 0 0: 2 40: 2 33: 1 100: 0 --: 0 --| 18 41 -: 2 67: 10 48: 5 83: 2 100: 3 60: 4 67: 0 0: 0 --: 0 --| 26 59 ∑: 3 : 21 : 6 : 2 : 5 : 6 : 1 : 0 : 0 | 44 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 8 1: 21 37: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 2 50| 23 38 -: 36 63: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --: 2 50| 38 62 ∑: 57 : 0 : 0 : 0 : 0 : 0 : 0 : 0 : 4 | 61 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - f 1: 42 40: 117 37: 45 63: 19 31: 22 37: 30 53: 12 24: 8 62: 1 25| 296 40 -: 63 60: 199 63: 26 37: 42 69: 38 63: 27 47: 37 76: 5 38: 3 75| 440 60 ∑: 105 : 316 : 71 : 61 : 60 : 57 : 49 : 13 : 4 | 736 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - u 1: 2 100: 2 25: 2 100: 0 --: 2 40: 0 0: 2 100: 0 --: 0 0| 10 43 -: 0 0: 6 75: 0 0: 0 --: 3 60: 3 100: 0 0: 0 --: 1 100| 13 57 ∑: 2 : 8 : 2 : 0 : 5 : 3 : 2 : 0 : 1 | 23 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - m 1: 9 56: 14 29: 3 75: 0 --: 0 0: 3 38: 0 --: 1 100: 0 --| 30 36 -: 7 44: 34 71: 1 25: 0 --: 6 100: 5 62: 0 --: 0 0: 0 --| 53 64 ∑: 16 : 48 : 4 : 0 : 6 : 8 : 0 : 1 : 0 | 83 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 4 1: 2 67: 5 45: 2 100: 0 0: 1 100: 2 67: 1 50: 0 --: 0 --| 13 54 -: 1 33: 6 55: 0 0: 2 100: 0 0: 1 33: 1 50: 0 --: 0 --| 11 46 ∑: 3 : 11 : 2 : 2 : 1 : 3 : 2 : 0 : 0 | 24 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - -

55

7 1: 1 11: 3 38: 1 50: 0 --: 0 0: 0 0: 0 0: 0 --: 0 0| 5 20 -: 8 89: 5 62: 1 50: 0 --: 2 100: 1 100: 2 100: 0 --: 1 100| 20 80 ∑: 9 : 8 : 2 : 0 : 2 : 1 : 2 : 0 : 1 | 25 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 9 1: 1 100: 0 0: 1 100: 0 --: 0 0: 0 --: 0 --: 1 50: 0 --| 3 38 -: 0 0: 3 100: 0 0: 0 --: 1 100: 0 --: 0 --: 1 50: 0 --| 5 62 ∑: 1 : 3 : 1 : 0 : 1 : 0 : 0 : 2 : 0 | 8 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 3 1: 1 100: 2 40: 1 100: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 4 57 -: 0 0: 3 60: 0 0: 0 --: 0 --: 0 --: 0 --: 0 --: 0 --| 3 43 ∑: 1 : 5 : 1 : 0 : 0 : 0 : 0 : 0 : 0 | 7 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 6 1: 1 100: 0 0: 1 100: 0 --: 0 --: 1 100: 0 --: 0 --: 0 --| 3 75 -: 0 0: 1 100: 0 0: 0 --: 0 --: 0 0: 0 --: 0 --: 0 --| 1 25 ∑: 1 : 1 : 1 : 0 : 0 : 1 : 0 : 0 : 0 | 4 +---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 81 41: 154 37: 57 63: 19 29: 27 34: 38 48: 16 29: 10 62: 3 30| 405 40 -: 117 59: 267 63: 33 37: 46 71: 53 66: 41 52: 40 71: 6 38: 7 70| 610 60 ∑: 198 : 421 : 90 : 65 : 80 : 79 : 56 : 16 : 10 | 1015 • CROSS TABULATION Group #2 -- horizontally. Group #4 -- vertically. t % j % c % e % o % i % a % s % v % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 2 1: 40 34: 60 27: 27 63: 4 10: 12 26: 19 40: 8 21: 2 33: 1 14| 173 31 -: 77 66: 160 73: 16 37: 38 90: 34 74: 28 60: 30 79: 4 67: 6 86| 393 69 ∑: 117 : 220 : 43 : 42 : 46 : 47 : 38 : 6 : 7 | 566 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 5 1: 41 51: 94 47: 30 64: 15 65: 15 44: 19 59: 8 44: 8 80: 2 67| 232 52 -: 40 49: 107 53: 17 36: 8 35: 19 56: 13 41: 10 56: 2 20: 1 33| 217 48

56

∑: 81 : 201 : 47 : 23 : 34 : 32 : 18 : 10 : 3 | 449 +---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 81 41: 154 37: 57 63: 19 29: 27 34: 38 48: 16 29: 10 62: 3 30| 405 40 -: 117 59: 267 63: 33 37: 46 71: 53 66: 41 52: 40 71: 6 38: 7 70| 610 60 ∑: 198 : 421 : 90 : 65 : 80 : 79 : 56 : 16 : 10 | 1015 • CROSS TABULATION Group #3 -- horizontally. Group #4 -- vertically. l % 8 % f % u % m % 4 % 7 % 9 % 3 % 6 % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 2 1: 5 28: 12 34: 131 32: 4 33: 10 20: 5 33: 3 17: 1 20: 0 0: 2 67| 173 31 -: 13 72: 23 66: 279 68: 8 67: 39 80: 10 67: 15 83: 4 80: 1 100: 1 33| 393 69 ∑: 18 : 35 : 410 : 12 : 49 : 15 : 18 : 5 : 1 : 3 | 566 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 5 1: 13 50: 11 42: 165 51: 6 55: 20 59: 8 89: 2 29: 2 67: 4 67: 1 100| 232 52 -: 13 50: 15 58: 161 49: 5 45: 14 41: 1 11: 5 71: 1 33: 2 33: 0 0| 217 48 ∑: 26 : 26 : 326 : 11 : 34 : 9 : 7 : 3 : 6 : 1 | 449 +---------+---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 18 41: 23 38: 296 40: 10 43: 30 36: 13 54: 5 20: 3 38: 4 57: 3 75| 405 40 -: 26 59: 38 62: 440 60: 13 57: 53 64: 11 46: 20 80: 5 62: 3 43: 1 25| 610 60 ∑: 44 : 61 : 736 : 23 : 83 : 24 : 25 : 8 : 7 : 4 | 1015

57

APPENDIX D

CONDITION FILE EXCLUDING KNOCKOUTS AND SINGLETONS FROM BINOMIAL

ONE -STEP ANALYSIS

58

• CELL CREATION • 8/3/2010 ••••••••••••••••••••••••••••••••••••••••• Name of token file: Gould_all_thesis.tkn Name of condition file: Untitled.cnd ( (1 (NIL (COL 3 k)) (NIL (COL 3 n)) (NIL (COL 3 d))) (3 (t (COL 3 t)) (j (COL 3 j)) (c (COL 3 c)) (e (COL 3 e)) (o (COL 3 o)) (i (COL 3 i)) (c (COL 3 p)) (a (COL 3 a)) (d (COL 3 d)) (s (COL 3 s)) (n (COL 3 n)) (k (COL 3 k)) (v (COL 3 v))) (5) ) Number of cells: 18 Application value(s): 1 Total no. of factors: 11

59

APPENDIX E

BINOMIAL ONE-STEP ANALYSIS EXCLUDING KNOCKOUTS AND SINGLETONS

60

Non-Group Apps apps Total % 1 (3) t N 81 117 198 19.5 % 40.9 59.1 j N 154 267 421 41.5 % 36.6 63.4 c N 57 33 90 8.9 % 63.3 36.7 e N 19 46 65 6.4 % 29.2 70.8 o N 27 53 80 7.9 % 33.8 66.2 I N 38 41 79 7.8 % 48.1 51.9 a N 16 40 56 5.5 % 28.6 71.4 s N 10 6 16 1.6 % 62.5 37.5 v N 3 7 10 1.0 % 30.0 70.0 Total N 405 610 1015 % 39.9 60.1 2 (5) 2 N 173 393 566 55.8 % 30.6 69.4 5 N 232 217 449 44.2 % 51.7 48.3 Total N 405 610 1015 % 39.9 60.1 TOTAL N 405 610 1015 % 39.9 60.1

61

• BINOMIAL VARBRUL, 1 step • 8/3/2010 •••••••••••••••••••••••••••••• Name of cell file: .cel Averaging by weighting factors. One-level binomial analysis... Run 1, 18 cells: Convergence at Iteration 5 Input 0.393 Group Factor Weight App/Total Input&Weight 1: t 0.520 0.41 0.41 j 0.458 0.37 0.35 c 0.718 0.63 0.62 e 0.400 0.29 0.30 o 0.437 0.34 0.33 i 0.596 0.48 0.49 a 0.399 0.29 0.30 s 0.692 0.62 0.59 v 0.421 0.30 0.32 2: 2 0.405 0.31 0.31 5 0.619 0.52 0.51 Cell Total App'ns Expected Error v5 3 2 1.300 0.666 v2 7 1 1.697 0.378 t5 81 41 43.160 0.231 t2 117 40 37.805 0.188 s5 10 8 7.023 0.457 s2 6 2 2.981 0.641 o5 34 15 15.287 0.010 o2 46 12 11.720 0.009 j5 201 94 94.512 0.005 j2 220 60 59.586 0.004 i5 32 19 19.461 0.028 i2 47 19 18.508 0.022 e5 23 15 9.474 5.482 e2 42 4 9.520 4.139 c5 47 30 34.236 1.930 c2 43 27 22.741 1.693 a5 18 8 7.399 0.083 a2 38 8 8.590 0.052 Total Chi-square = 16.0173 Chi-square/cell = 0.8899 Log likelihood = -643.277

62

APPENDIX F

CROSS TABULATIONS WITH INDEPENDENT VARIABLES 2 (SUBJECT TYPE) AND 4 (AGE GROUP)

63

• CROSS TABULATION • 10/4/2010 10:50:13 PM ••••••••••••••••••••••••••••••••••••• • Cell file: .cel • 10/4/2010 10:50:07 PM • Token file: Gould_all_thesis.tkn • Conditions: Untitled.cnd Group #1 -- horizontally. Group #2 -- vertically. • CROSS TABULATION • 10/4/2010 10:50:13 PM ••••••••••••••••••••••••••••••••••••• • Cell file: .cel • 10/4/2010 10:50:07 PM • Token file: Gould_all_thesis.tkn • Conditions: Untitled.cnd Group #1 -- horizontally. Group #2 -- vertically. t % j % c % e % o % i % a % s % v % ∑ % + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 2 1: 40 34: 60 27: 27 63: 4 10: 12 26: 19 40: 8 21: 2 33: 1 14| 173 31 -: 77 66: 160 73: 16 37: 38 90: 34 74: 28 60: 30 79: 4 67: 6 86| 393 69 ∑: 117 : 220 : 43 : 42 : 46 : 47 : 38 : 6 : 7 | 566 + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - + - - - - 5 1: 41 51: 94 47: 30 64: 15 65: 15 44: 19 59: 8 44: 8 80: 2 67| 232 52 -: 40 49: 107 53: 17 36: 8 35: 19 56: 13 41: 10 56: 2 20: 1 33| 217 48 ∑: 81 : 201 : 47 : 23 : 34 : 32 : 18 : 10 : 3 | 449 +---------+---------+---------+---------+---------+---------+---------+---------+---------+--------- ∑ 1: 81 41: 154 37: 57 63: 19 29: 27 34: 38 48: 16 29: 10 62: 3 30| 405 40 -: 117 59: 267 63: 33 37: 46 71: 53 66: 41 52: 40 71: 6 38: 7 70| 610 60 ∑: 198 : 421 : 90 : 65 : 80 : 79 : 56 : 16 : 10 | 1015

64

APPENDIX G

BINOMIAL STEP-UP ANALYSIS

65

• BINOMIAL VARBRUL • 8/3/2010 •••••••••••••••••••••••••••••••••••••• Name of cell file: .cel Averaging by weighting factors. Threshold, step-up/down: 0.050001 Stepping up... ---------- Level # 0 ---------- Run # 1, 1 cells: Convergence at Iteration 2 Input 0.399 Log likelihood = -682.699 ---------- Level # 1 ---------- Run # 2, 9 cells: Convergence at Iteration 5 Input 0.397 Group # 1 -- t: 0.513, j: 0.467, c: 0.724, e: 0.386, o: 0.436, i: 0.585, a: 0.378, s: 0.717, v: 0.394 Log likelihood = -664.879 Significance = 0.000 Run # 3, 2 cells: Convergence at Iteration 4 Input 0.395 Group # 2 -- 2: 0.403, 5: 0.621 Log likelihood = -659.390 Significance = 0.000 Add Group # 2 with factors 25 ---------- Level # 2 ---------- Run # 4, 18 cells: Convergence at Iteration 5 Input 0.393 Group # 1 -- t: 0.520, j: 0.458, c: 0.718, e: 0.400, o: 0.437, i: 0.596, a: 0.399, s: 0.692, v: 0.421 Group # 2 -- 2: 0.405, 5: 0.619 Log likelihood = -643.277 Significance = 0.000 Add Group # 1 with factors tjceoiasv Best stepping up run: #4 ---------------------------------------------

66

APPENDIX H

BINOMIAL STEP-DOWN ANALYSIS

67

Stepping down... ---------- Level # 2 ---------- Run # 5, 18 cells: Convergence at Iteration 5 Input 0.393 Group # 1 -- t: 0.520, j: 0.458, c: 0.718, e: 0.400, o: 0.437, i: 0.596, a: 0.399, s: 0.692, v: 0.421 Group # 2 -- 2: 0.405, 5: 0.619 Log likelihood = -643.277 ---------- Level # 1 ---------- Run # 6, 2 cells: Convergence at Iteration 4 Input 0.395 Group # 2 -- 2: 0.403, 5: 0.621 Log likelihood = -659.390 Significance = 0.000 Run # 7, 9 cells: Convergence at Iteration 5 Input 0.397 Group # 1 -- t: 0.513, j: 0.467, c: 0.724, e: 0.386, o: 0.436, i: 0.585, a: 0.378, s: 0.717, v: 0.394 Log likelihood = -664.879 Significance = 0.000 All remaining groups significant Groups eliminated while stepping down: None Best stepping up run: #4 Best stepping down run: #5

68

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