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ISSN 1139-7241

 Abstract

  @- ?= @ @? @ -? @==@ @ ?@? ? 320 = @

=?: C>, C@> ?, "= ?, #@?,

E@?@>, !? ? !?, "?>?, ? @=@.

D=? G@? =@ >? V G == G@ > @

G ?G . @ ?= ? @> @@=. 16

-? ?@? @ == =? ?, @==@  

-? @==@ @ ?@?. FG =? @ @G

50% G? @> @G== =. C@?=@? ? ?

G= =? ?@> ? ? ? >?@=@ ? =? ? @> @ , >@, ? @?? @ 

@?. I =@ @?= @==@@? ? >@? @ 

>?? ? ?@? @ ?@?, ? G? @

=? ? ?=? >@? @ =?-

@==@@? .

Kewords: @==@@?, @ ?=, ?=? ?,

? ?=.

Resumen

Colocaciones muy frecuentes de sustantivos en artículos de investigaciónen ocho disciplinas académicas

E? @ ?=K = P? = @=@@? >M ?

?G@ @ ? ? @ 320 O=@ ?GP? ? @@

=? ?: O>, I?@>M, C? =@ "=,

#@?, E@?@>O, !? !?RO, A>?P? E>  @=@O. >? >N? = GP? Q? = ? =?, ?

H-? @==@@? @ ?@? ? = @ =?

Matthew Peacock

C6AF ';62?6AF

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Ibérica 23 (2012): 29-46

"A))HE, %EAC$C 

@ @@ @ ? ?G@? ?@. E? = ?M= = @

=KP @> @@=. ?@? =@ 16 ?G@ >M ? ?

? = @@ =? O @>@ = @=@@? >M ? ?

@. E? ?@ =? @ ? 50% GP? @ =@

=@ @>?@ @ = =? >?. @?= =

? ? ? =P? @? = O

=? N >N? M? =@? @? = >?@=@O M?

?@ =? ? =P? @? =@ > @, =@ >N@@

?GP? >=@ = @??@ =@ O=@. @?= >M

= @=@@? @?? ? >@? =@ ?@ =

?@? =@ ?G@, = ? > ?

=? = ? ?G = @=@@? ? = ?

=? N>.

Palabras clave: @=@@?, ?M= @, O=@ ?GP?? ? =?, ?M= N?@.

Introduction

  @- ?= @ -? 

@==@@? @ @>>@? ?@? ? 320 = ('A) @

=?. @==@? ?@? @ @==@@? @, W?@ @? ? @? ?

>= =? ?. F@ ? -? ?

? @ = 40 >==@? @ (>), @==@?

?@? @ B, C@? ? C@ (2004: 376): W >==@? @ @

?= ? ?=X.

 ==> (2002) @ @>>? G=@ @ @@>>?@? @ @ ? ? @, ?

?G= @, @? @ ?? . C@==@@?

@ >@? ?. H@G, @? ? @? @

@? @ -? ?@? >=G @? @ @ 

, @ @? @==@@?.

  'A @? @ @ ?? @

@

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@ @>>? @ @>>? (==>, 1998). =?

? @>>?.

$? @ = >?@? @ @ @==@@? F (1957), @

=@ @ ==-

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  == G@ > @ G ?G -? 

@==@@? @ @>>@? ?@?. (2007) =@@>@? ?@? ?

@==@@? ? C>= E??? @@

@==@@? ? @? ? @ @ ??? =?.

  >@ @>>@? ?@? WX, W=X, ? WX. = @==@@? ?= =? , ?@

@ ?G= @. H @G >@? ?@ WX

W +X ? @==@@? @= @ =? >>.

H@G, @ ?@ G = @ @>>@? ?@? @> ,

@ @>>@? ?@? ? @ =?, @ @ @==@@? (

@> = ?> @ W +X @==@@?). H@G, Y (2007)

=@@ G== ?@? @ @==@@? ?

@. g=== (2000) =? @ ? @

@ ??= >@ @? ? @? @ @ ? ?@,

@ ?@ @?? @ ?@?. H @ @==@@? ?

>= C@ @ 150 'A @> 22 ? ? >@=@ 

@?=, @ ?@ @? @? ?@?. @> >= @==@@?

W? @ >@X ? W@ = ? =X.

Rationale for research

  -? @==@@? @ @>>@? ?@? > ? >@?

@ > E?= ?=? 'A, ? @ ?G? .

  G ? G= == @ ?@ @==@@?, @ >=

g@@> (2005) =? ? ?

G@ >?@=@ ? ?@@? == @ >?? @?

= =. g=== (2000) =@@@?-= ? "? (2008) >K

>@? @ @==@@?, ? @>>@? ?

> @ ? ? @

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I @==@@? >@?, > ?

 . B (2000) ?@ @? @? @ ?

?@> ? @ ?, == @ @

  @ @? > @ @>>?, = D? (2009)

=? ? @ -? @==@@?. =@ @ @> >? ?@?-?G >. (2002) @==@@?

@ ##, ## ? @ @ ? @? @?, ?

@@ G @==@@?. =@ => ## >@? @ @ @==@@? ? G?

  >, ? ## @= ? = =@? ?

?G

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= @ @G @? @ @==@@?. I @@

?@ G ?@? ? ?

?, @ =? G@? @ ?> @ =?.

= @= G= > >@ @ ? @ 'A, ? =

@ ? ?@> =? @ @ @==@@?.

Methodolog

  ?G @? ? ? @ -

? @==@@? @ ?@? ? 320 = @

=?.

Research Aims

  > @ , ? @, @:

(1) ? ? = -? @==@@? @ @>>@? ?@?;

(2) ?G ? @ @==@@?; ?

(3) ?G =? G@?.

 The RA Corpus

  @ 320 = 'A, 40 @> =?.

=? = ? ? @ ? =@

G = ?> @ , >@= ##, @? @=.

? =? @ ? @>>?@? @

?. F@ =? @?= = @> =?.

  > @ =G? >? ? @ @ @>

  ?= @?= @> =.

 ? 'A @> 2007/2008 ?@>= @? @> @?=  

G? ?> ? ? ?> @> @. $?= >= -

G? 'A I?@@?-"@-'=-D@? (I"'D)

@> @?, ? >@? ? (H=?, 1998). K

@ =? @@, ? @ =? @ @ @@

@?=, @@ ?= ?G.

"A))HE, %EAC$C 

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Investigating the Corpus

 A?= @? ? @==@? :

(1) H-? ?@? ? ? @! ?@?

@ @> @@= 4.0 (@, 2004). "? @ ?@? ( = 1), @ >= WX, W@X, ? WG=X,@>> ?@? G @ G. EG @? @  ?@? = @> @?: @ @ ? @ >?== >? @? ? C@?@?@?. A => @ 16 -? ?@? (=? G @ G), @ >@ >?=.

(2) H-? @==@@? ?, =@? =? G@?, ? C@?@ ?@? @ @> @@= = C=, ?, ? C@==@ -?@?.

'? 1, =, W?@?X >? W@X @ WX. @?=, @> @@= > @ @@? == W>= ?@>@?X("I) @ ? @==@, @ >@ = @ W@-@?X ? ? @ == W??X. "I > ? @ @==@@?, =>?? @ ?, ?

? = @ ? @ @@?. '? 2, @ = ?@ =? @@ > @ < =?  G@?. I?G= >?=

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 = 1 @ ?@ @ == -? @==@, @?= @ >@

@>>@?. E>= @==@: W?=X @==@ W@ LX,

W@? LX, WL / @>X, ? WL G=X. WEG?X

@==@ W@G//? LX, W?/@? LX, W>= LX, ?

W / ?@ LX. W@X @==@ W? LX, W=?? LX,

W?@>@? LX, ? W=??? LX. @=>? @ ?

@ == @? @ ?@? = @==@@? >=, @ @==@ @ W/X >@ @?

>@ @>>@? @==@ W=@?/X (38% @ @?),

WG=X (29%), WG?X (27%), WX (24%), ? W/X (19%).

H@G, W>@=X ? W>@X ?@ -? @==@, ?

W>=X @?= @. A = ?> @ =? ? @?.

  @? ? = 2 ? 3.

"A))HE, %EAC$C 

Ibérica 23 (2012): 29-4636

 

Noun CollocationsPercent of all

occurrences

study/ies present ~, previous ~, case ~, results (of) ~  19

result/s ~ show/ed, ~ indicate/d, ~ suggest/ed, ~ obtained 14

effect/s significant ~, main ~, no ~, positive ~ 24

model/s - 0information ~ management, ~ system/s, ~ technology, ~ processing 12data ~ (were) collected, ~ collection, ~ analysis, ~ were obtained 10analysis/es factor ~, regression ~, ~ was/were performed, ~ revealed 11

process/es business ~, learning ~, information ~, planning ~ 9research previous ~, future ~, further ~, ~ has shown  17sample/s ~ period, ~ size 6experiment/s results (of/in) ~ (1, 2, 3), present ~, participated in ~, previous ~ 8

relationship/s ~ between, customer ~, positive ~, causal ~ 38factor/s ~ analysis/es, (1st-, 2nd-, 3rd-, higher-) order ~, ~ structure, key ~ 14variable/s dependent ~, dummy ~, independent ~, explanatory ~ 29method/s - 0evidence provide/d/ing ~, find/found ~, empirical ~, there is/was no ~ 27

Table 1. High-frequency collocations, in order of frequency – All disciplines.

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C@>@? @ = 2 ? 3 = 1 G= @?= =?  G@?. $G == =?, ?@ ? 157 @==@@? @> @ ? = 1, @ 53%. F@ >=, C@> ? @@==@ W?@>@?X W@G LX, WL @?X, ? W= @ LX. #@? @ @==@ W?=/X WL @ / LX,W= LX, ? W>@= LX, ? W@/X W@?G LX.E@?@> @ @==@ W@/X WL >@=X, WL @GX,? W@?@==? LX, = @=@ @ @==@ W@X W?? LX ? W@?G LX. "?>? @ @==@ W>@=X

  W? LX, W@@=@ LX, W>>? LX, ? W= LX.

HIgH-F'E&*E#C. C$!!$CA)I$#( $F #$*#(

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Noun Chemistry Computer Science Materials Sci. Neuroscience

study/iespresent ~ previous ~, results (of) ~,

present ~, case ~

previous ~, present

~

present ~, previous ~,

current ~, recent ~

result/s~ obtained ~ indicated, ~ show/n,

experimental ~,

~ obtained

~ indicate, ~

show/n, similar ~, ~

suggest

~ suggest, ~ show/n/ed,

~ indicated, ~ (in/of)

experiment (1,2,3)

effect/s- significant ~,

positive ~, no ~- main ~, significant ~, no

~

model/s- user ~, ~ order, research

~- ~ analysis/es, direct ~,

memory ~

information

- ~ system/s, provides ~, ~

extraction,quality of ~

- ~ processing

data

~ collected,

~ collection,crystal ~

training ~, ~ collected,

consistent ~

~ (…) shown,

experimental ~,~ presented, ~

obtained

regression ~,

individual ~,~ were obtained

analysis/eselemental ~ data ~, factor ~ ,

~ results, further ~

thermal ~, ~ was/

were performed,reaction ~

~ of data/data ~,

statistical ~,~ revealed, model ~

process/es - software ~, business ~ corrosion ~ cognitive ~

research- previous ~, ~ model,

qualitative ~, future ~- future ~, previous ~

sample/s

- data ~ ~ is/as shown, ~tested, laboratory

~, observed (in all)~

-

experiment/s- ~ conducted ~ (…) performed participated in ~,

condition/s (in/of) ~,previous ~, present ~

relationship/s~ between ~ between, causal ~, ~

among~ between ~ between

factor/s- ~ analysis, key ~,

contextual ~

- -

variable/s

- controlled ~, value/s (ofthe) ~, dependent ~,independent ~

- independent ~

method/ssolved by direct~

(…)-based ~, evaluation~, clustering based ~,

common ~

sterilisation ~ -

evidence- - - there is/was no ~,

provide ~

% differing fromTable 1

38 52 59 39

Table 2. High-Frequency Collocations: Science Disciplines.

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  @@> @ ? = 2 ? 3 @ ? @ @==@@? ?

@=>? @> @ ? = 1. ? G  

=?, G =? @? @G 50% G?: C@>

?, "= ?, E@?@>, !? ? !?, ?

@=@.

"A))HE, %EAC$C 

Ibérica 23 (2012): 29-4638

 

Noun Economics Language Management Psychology

study/iesprevious ~,empirical ~, several

~

present ~, previous~, case ~

case ~, empirical ~,results (of) ~,

previous ~

present ~, previous ~,results (of) ~, current ~

result/s

regression ~,

~ suggest, ~reported, empirical

~

~ of this study, ~showed, ~ ofthe/this analysis, ~

reported

~ (of this) study,~ indicate/d, ~show,

~ suggest

~ (of this) experiment(1,2,3), ~ shows/ed, ~indicated, pattern of ~

effect/s

positive ~,significant ~

~ of(non)correction,positive ~, ~ for/offeedback,significant ~

positive ~,interaction ~,significant ~,negative ~

main ~, significant ~, ~of target, revealed(significant/main) ~

model/sregression ~,probit ~, structural~, theoretical ~

CARS ~ business ~, portfolio~, measurement ~,structural ~

~ fit, (1-,2-,3-,4-,5-)factor ~, parallel ~

information

~ available, obtain ~ - ~ systems, ~management, ~technology, ~

acquisition

location ~,~ processing,~ sources

data

~ available,~ source

~ analysis,~ collection

financial ~, ~collection,~ collected, ~

analysis

~ of/from experiment(1,2,3), ~ suggests,~ revealed

analysis/es

unit ~, regression ~,empirical ~,comparative ~

genre ~, data ~,needs ~, discourse~

data ~/~ of (the)data, empirical ~,factor ~,organizational ~

factor ~, regression ~,confirmatory ~,~ reveal/ed

process/es

production ~ writing ~, learning~, language ~

business ~,planning ~,information ~,management ~

inference ~,cognitive ~

researchfuture ~, previous ~ ~ question/s, further

~, ~ project,

second language ~

future ~, previous ~,further ~, prior ~

previous ~, future ~,present ~

sample/s~ period, ~ firms,during the ~ period,~ selection

representative ~ firms in the ~/~firms,~ size, ~ selection

(non-) clinical ~,present ~, ~ size,~ consisted of

experiment/scurrent ~, single ~,previous ~

controlled ~ - results (of) ~,identical ~, present ~,

data (of/from/in) ~

relationship/s

~ between, long-run~, positive ~

~ between,significant ~

~ between,customer ~,~ portfolio/s,

business ~

~ between, specific ~,current ~

factor/s~ model,~ productivity,controlling ~

learner ~, other ~ ~ analysis, success~

~ structure, (1st-, 2nd-,higher-) order ~,~ analysis, ~ loadings

variable/s

dummy ~,dependent ~,independent ~,control ~

independent ~,dependent ~

dependent ~,independent ~,control ~,explanatory ~

dependent ~,independent ~

method/s- research ~ - -

evidenceprovides ~,empirical ~, strong

~, ~ suggests

provide/d ~,anecdotal ~, further

~

empirical ~ provide/d/s ~,stronger ~,

empirical ~, further ~

% differing from

Table 164  59 46 57

Table 3. High-frequency collocations: Non-science disciplines.

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Discussion and Conclusions

  @==@@? ? = 1 > =@@< G >= @ , @ >

@ > @==@ >@ ?= @

  ?@?. H@G, ?@ , >?@? @ = 2? 3 @: = ?> @ =? ? > ?.

D? (2009) ? ? @ @ ? @ ?@?-

=?- = @ @==@@?, ? = = ??@

@? =@@= =

@==@@? =? .

I? @ @ @ ?? @? @ =? ?, =@ >?@? @ @ ? >. E>= @ >? @

=? ? @==@. D?@? @ ? > == G? @

???:

C>:

S W= X: >> @ ?>?= crystal data ? >?=

> @ >?@? G? ? = 1.

S W=>?= ?=/X (>?? =>? ? ?

>=): =, ?= elemental analysis   ?@ @??

  ?(#2)2 >?>= @>=.

S W@=G >@/X ( >>= @ @ >?? 

= ): A== solved by direct methods  ? 

HE!-97.

C@> ?:

S W>?= =/X: experimental result >@? @ ?

> ?? >@G>? @? @ >?.S W >@=/X (@? @ : = @ G@): A

$"D >@G @? @ user model  

? @ Y @= * ? @G >.

S W?@>@? @?X (G? ?@>@?): @

information extraction ? @ @>== ? ? @?

?> = @> ?.

S W= @ ?@>@?X: H ? @?? @ ??

? @ @?@? @ @ ? @ quality of 

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information @G @>, ? @?? @ ?=? @>

@>.

S W?? X (?@? @ ): I? @, training data 

? -@ @ ?Y ?@? ? G= ?G@?>?,

? >@= @ > ? @> =? .S W@ @/X ( @?K@? ? >?>? @ @

G=@>?): = = = software process 

= ? @ @.

S W=? >@X (= ?= >@ ? C@>

?): ? E! >@ @@> clustering 

based method  ? > @ G ? > >?

?.

"= ?:

S W>= ?=/X (? ? ? >= >

?): D?>= >?= thermal analysis  (D"A) @ >@

?? ?.

S W@? ?=/X: K@? ? @>K@? @

@=> G reaction analysis  >@?.

S W@@@? @/X: @G @G@? > ? @

?= ? @ G? @ @? >= corrosion 

 process G=@.

S W=@? >@/X: @ sterilisation method @?

@ @=? ? ? @>?@? @= @?

? ?@ G@= ? @.

#@?:

S W>>@ >@=/X (@? @ @ @ @ >>@ @

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E@?@>:

S W@? =/X (@>>@? >@ @ ?=): F@>

?? @ regression results @> @=/X (@> @= @ ? ): gG? @?=

? @ ?? G= ? @ probit model .

S W@ >@=/X (>>= >@= @ @< ?=): '?=, ? @ ?? = ?@@ >-? G@ ?

 factor models @ > .

S W@ @GX (@ @ @ @ ? @ =@ ? =):

  = ? ? @=  factor productivity  (F) ? @? @ 

@= ? > G @ ?= =>.

!? ? !?:S W? ?=/X: =? @ @  genre analysis  ? ?

?@@ @ @ =? G@ ? @ > ?

@@?= ?.

S W@ ?=/X: @? @ == ?G@=G

discourse analysis @ @ ? G@ @? @ gCAE >?.

"?>?:

S W?@? /X (= > >?? @ G= @? @): H@G, = ? ? @Y ? ?@

= ?@; ? interaction effect ? @ ? >? ?@ ??.

S W?@>@? @?X ( @==@? @ > ?@>@? @>

@?K@?= ? ? 

@?Y >?? @ information acquisition   @ @? 7-@?

!?X: results of Experiment 3 = @

@ IG?@ ? =?.

S W(1-,2-,3-,4-,5-) @ >@=/X: A CFA @> ? @

??? @ (@? @> @  four- ? five-factor >@=).

S W=== >@=/X (@? > >=?@= ? ==):

  - parallel models > >== G= @ ?

= ? == @?X ( =@@? @ >= @ @?

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>): > @?= @ ? @ ?@? ? @G? @ ? ===.

S W(?@?-) =?= >=/X: F @< =K? ?G,

clinical samples , ? >=->@ >? @@= G=.

G >==, non-clinical samples , ? G >@@=@==>@?.

S W@ X (= > = @ @ ?=): A?,

?= @ ?= @> (@=) @@? @

=?  factor structure .

S W@ =@?X (= > = @ @ ?=): >?? 

> @  factor loadings  @? @ @

?=.

C= ? ? ?= @ @G >= @> ?=?, ? @ = 2 ? 3, = @ @@= >? @ >@ @ 

@==@@? ? ? >?@=@ ? =?.

 A>@? >? >= @ =?- >?@=@

W= X ? W@=G >@/X (C>), W@

@X, W=? >@X, ? W >@=X (C@> ?),

W>= ?=X, W@? ?=X, ? W@@@? @X ("=

?), W>>@ >@=X ? W@?G @X (#@?), W@

>@=X ? W@ @GX (E@?@>), W? ?=X ?W@ ?=X (!? ? !?), W?@>@? @?X

("?>?), ? W=== >@=X ? W(?@?-) =?= >=X

(@=@).

E>?@? @ ? @==@@? ? >= ? ?

 = 2 ? 3 @ @ @> @ : @

>@ ==, ? @==@@? ? 

>?@=@, ? @, ? >@, ? ? 

@?? @ @? @ =?. ? , @==@@? == G >@? @ >??, ? @

@ ?@?, @ ?@?. I =@ G? >?? ?

?@? @? =?, ? >?? ? ?@?

@==@@?.

  @==@@? >@ @>>@? ? @ ? ? = 1, =? @

@? G? @ =? ? ?=?

>@? @ =?- @==@@? . F>@,

=? ? ? ? -? 

"A))HE, %EAC$C 

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@==@@? @ @>>@? ?@? @ G@ >?@=@ 

(g@@>, 2005) =? ? ?. A=@, ?= @ 

@ = @ @? -? @==@@?

? >@? @ 'A ? ?= @ ?? @ (==>,

2002) @ 'A. @ ? =? ?@>, ? ? ? ? ? ?

=? @?K @ @ ?

. A? H=? (2000: 78) ?@, ? @ W@ ? ?

@X. H =@ @@ (1999) =? ? = @=

@?? ? =?. > ? C (2004)

? ? ? @, ? @?G?@?= ?

@ @>>?. G= @> @ @?G?@?=

@> ? G@ =? @@.E>?@? @ = 2 ? 3 G= ? ?

?G= =? ? >@ @= ? @ ?

=? @= ? @ ?@?-? =?. H@G,

-? @==@@? ? C> ? "= ?.

  ? =?, @ ? @ ?@? ( ?@ @ ?@?, @

@= @), =@ ? V ?@? ? @

@@ =@ @ ==@ @? @ ? -? @==@@?.

C> ?@? W?@>@?X (220 >), WX (60 >),W>?X (210 >), WG=X (110 >), ? WG?X (220 >).

I? "= ? W?@>@?X (120 >), WX (90

>), WG=X (50 >), ? WG?X (160 >). F?==,

@? ? "?>?, W>?X (80 >). A ?@ @G,

@? ? @? @ @? @ @ -? 

?@? >=G @? @ @ .

Implications for teaching

C@==@@? ? >@? @ =?

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 A@=, 2004) ? ? =?, ? , = > @ 

. A ?@ @G, ? @ =? @==@@? @= (E==,

>@?-= & "?, 2008); D? (2009) @@ =?

? @ =? -? @==@@?. @ >=@? @ ?

?? @ ? ? ? @ =? G@? ? >@? @ ?, == @ ?

@ ?, ? =?- ? @

@==@@? ?= G=. > == >@? @

##, @ > ? @ ? @?G?@? ? ? = ? @?? 

@ @>>? @ ??@?= (=, 1993), ? =

 ? @< ? ? ? ?@ @= = (A>, 1997).

  @==@@? >@? ? > E?=, ? ## > @ ##. ? ?@> ? 

@ ?, ? @ =? @ G@?.

 A (2007) G @ @? =?, @>= @

. ? ?? ? >? Y @?

@==@@? G =? . A?= @ @ @?

?> @ =? ? ? @==@ @ -? 

?@?.

I @ ??? @ 

=? @?G?@?, ?=? =? ?, ? @ @==@@?.  ? ?? @= >@G

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Matthew Peacock  ? D>? @ E?= C *?G @ H@? @?. H ? ?= E?= @ @, @ ?=, ?, ? ?=, ? EF!>@@=@. I? 2001, @- ( J@? F=@) @==@? @>C> *?G , Research Perspectives on English for Academic Purposes .

"A))HE, %EAC$C 

A