Aston Data Explorer

Uren, Victoria

Joel-Edgar, Sian

Pan, Yu-Chun

Sociotechnical Talk - Survey design with literature sources

Table 1 Demographic and system use questions

Criterion	Question	Literature Source
Education: Level	How many full-time (or equivalent part-time) years at university have you completed? Less than 1/ 1 / 2 / 3 / 4 or more		[Filter question: minimum 1 year of study]	Based on (Junco, 2012a) (Pitafi, 2021)
Usage: 	Have you ever used a text chat / direct messaging app to collaborate with students at university on any task (e.g. assessed groupwork or organizing an event)? Yes/no		[Filter question exclude negative responses]	Self-designed
Usage: Platform General	Which text chat/direct message platforms do you currently use for any purpose? 	Blackberry Messenger (BBM)	FaceBook Messenger	Flock	Instagram Direct	Kik	LINE	MSTeams	Signal	Skype	Slack	Snapchat	Telegram Messenger	TikTok	Twitter Direct Message	Viber	Wechat	WhatsApp	Other (please name it)	[Filter question: at least 1 selected]	Self-designed 		List based on web searches of “n best apps” reviews to adapt the question to the current market. We excluded paid for business apps other than MSTeams and those that appeared on only one list or are used exclusively in non-English speaking countries.
Experience of group work using messaging	Have you ever used a text chat / direct messaging app to collaborate with students at university on any task (e.g. assessed groupwork or organizing an event). 		Yes / No 		[Filter out any responding No]	Self-designed
Education: Subject	Which or the following categories best describes your degree (choose the major subject if you are studying on a joint honours or liberal arts programme)?	Medical & Health Sciences	Engineering & Technology	Natural Sciences	Social Sciences	Business & Law	Arts	Humanities	Self-designed. Options based on (Dept. for Education, 2017)
Demographics	Gender: What is your gender identity? (Please enter “Prefer not to say” if you do not wish to answer this question)	Free text	(Slade et al., 2021)
Demographics: Age	What is your age?	18 to 20, 	21 to 24, 	25+	Prefer not to say		(Vaez & Laflamme, 2002)
Usage: frequency	When at university, on average, how many times per day do you check text chat/direct messaging apps?	less than once per day	between 1 and 3 times per day 	between 3 and 6 times per day 	between 6 and 9 times per day 	more than 9 times per day	Based on (Figueras-Maz, 2021) (Junco, 2012b) (Ellison et al. 2007)	
Usage: time	When at university, on average, how long per day do you spend on text chat/direct messaging apps?	less than 30 minutes 	30 minutes to 1 hour	1 to 2 hours 	2 to 3 hours 	more than 3 hours	Based on (Figueras-Maz, 2021) (Junco, 2012b) (Ellison et al. 2007)	
Usage: by activity	When at university, on average, how often per day do you use text chat / direct messaging apps for the following activities? 		Likert scale: (0 = never / 1 = less than once a day / 2 = once a day / 3 = 2 to 3 times a day / 4 = more than three times a day)		Socialising with family & friends	Entertainment	Monitoring announcements about your course	Sharing information about studies	Seeking help with studies	Collaborating on group tasks		Self designed with input from  (Figueras-Maz, 2021) 		
Usage: Platform Specific	Which text chat/direct message platforms have you used for to collaborate with students at university on any task (e.g. assessed groupwork or organizing an event)? 		Likert scale: 0 = never / 1 = for 1 task / 2 = for 2-3 tasks / 3 = for more than 3 tasks	Blackberry Messenger (BBM)	FaceBook Messenger	Flock	Instagram Direct	Kik	LINE	MSTeams	Signal	Skype	Slack	Snapchat	Telegram Messenger	TikTok	Twitter Direct Message	Viber	Wechat	WhatsApp	Other (please name it)	[Filter question: at least 1 selected]	Self designed 		List based on web searches of “n best apps” reviews to adapt the question to the current market We excluded paid for business apps other than MSTeams and those that appeared on only one list or are used exclusively in non-English speaking countries.



Table 2 Vignette scenarios
 	VIGNETTE STATEMENT	Waziak (2010) Framework	Speech Acts (Searle, 1979)
Scenario and Instructions	Scenario: You have been assigned to a four-person team to complete an assessed task for your degree in engineering management. For the assessment, you are required to produce a business plan for the development of a new sensor device (the team call it "the widget") that will be used in the transport industry to collect wear information for predicting when vehicles need to be maintained. Your team is made up of yourself, Danny, and your team-mates, Ali, Bob and Cathy. Ali is a tech wizard who always aims for "straight A" grades. Bob loves his life at university and tries to balance fun and study. Cathy is a responsible person who works hard but sometimes has a complicated family life. The team have set up a group on a secure messaging app to help you all communicate during the project.	
In this task, you will be presented with fragments of chat and asked to provide your next response. Please respond as you normally would - you can assume that Danny is similar to you in gender, age, experience etc. There are 8 responses you need to make and it should take you 10-15 minutes to complete the task.	 	 
Vignette 1 	Start-up Your team have all provided their contact details and said hello in the chat and are trying to decide how to begin. What do you say?	 	 
Statement	Bob: has anyone ever done a business plan?	Task-related	Directive
Statement	Ali: not me!	 	Expressive
Statement	Cathy: me neither but I looked online. we need exec summary, product description, market analysis, strategy, financial plan, stuff like that	Information	Assertive
Statement	Ali: should we find out about the widget first?	Product	Directive
Vignette 2	Assigning Tasks A week or so later the team has a meeting in the morning to decide who does what. You were up late working on another assignment and overslept. You wake up to find the following message on the chat. You have no idea how to write a financial plan, what are you going to say now?	 	 
Time	    ----yesterday---	 	 
Statement	Cathy: the meeting is at in the library tomorrow morning, right?	Project	Directive
Statement	Ali: yes – meet in the library coffee shop though – people get hissy if you chat in the reading room!	Management	Assertive
Statement	You/Danny: sure – see you there – about to pull an all nighter for my stats report, lol!	Socio-emotional	Commisive
Time	  -----today-----	 	 
Statement	Cathy: @Danny are you on your way? We have been waiting twenty minutes here, and @Ali has a lecture at ten	Project & Management & Socio Emotional	Expressive
Statement	Bob: Hey, Ali and Cathy and me waited for you in the library but you didn’t turn up so we pooled our ideas on and decided to assign tasks like this: Product description – Ali, market analysis – Cathy, Strategy – Me and financial plan - Dani. We will meet next after class on Thursday, you will be there won’t you?	Project & Management & Task related	Commisive & Directive
Vignette 3	Making Progress It’s a couple of weeks later and you have found out what you need to do for the financial plan. It’s a bit intimidating and you need to coordinate with Cathy to make some financial projections based on her market analysis. You had arranged to meet in an hour’s time and are on your way to find her when you get these messages:	 	 
Statement	Cathy: Hey @Danny, my sister’s car has broken down and I gotta get me niece from the nursery. Sorry can’t make the meet today.	Socio emotional & Task related	Assertive & Expressive
Statement	You: No chance you could come and bring the kid? I really need your help!	Socio emotional	Directive & Expressive
Statement	Cathy: You would not want that!!! She is a maniac!!!!!! Also, the nursery is miles from uni I am not tracking that little loony about on the bus.	Socio emotional & Information	Expressive & Commisive (negative)
Vignette 4	Friend in Need Ali picks up your chat with Cathy and makes a helpful suggestion.	 	 
Statement	Ali: Hey @Danny, I was working with @Cathy on the competitors for the widget last week - just emailed you our shared doc. I’m in the electronics lab working on my circuits till 5 – drop by if you are in uni	Task related & Management	Assertive & Directive
Statement	You: Thanks mate J got it – looks nice - see you soon!	Socio emotional	Socio emotional & Commisive
Statement	Cathy: That version is pretty out of date now. I’ll send you the new one this evening. 	Task related & Information	Assertive & Commisive
Vignette 5	A Dispute Your group decide to set up a folder on a cloud service so you can all have access to each other’s latest work. Bob starts reading and realises there is a problem.	 	 
Statement	Bob: @Cathy my strategy is to sell to China, but your market analysis says the 3 biggest manufacturers are there. Can you change it to fit the strategy?	Product & Problem solving	Assertive & Directive
Statement	Cathy: its based on the facts, mate! Did a shed load of research. You need to change the strategy. If its just made up stuff we r gonna fry!	Information & Socio emotional	Expressive
Statement	Bob: yeah but I’ve done loads of work too L	Socio emotional	Expressive
Vignette 6	Deadlines Approach: Its a week to the submission day and your team is putting final touches to their plan.	 	 
 	Ali: I’ve just uploaded an updated version of the product description. How is it going with  your bits?	Product & Task related	Assertive 
 	Bob: Not bad, @Dani and I have a first draft of the Exec summary – everyone should check it – make sure it fits your stuff  	Product & Task related & Management	Assertive & Directive
 	Cathy: Could we have a final meeting? I am around Tuesday morning	Management	Directive 
 	Bob: I’ve got labs Tuesday, could do a call in the evening on Monday	Management	Assertive
 	Cathy: Baby sitting loony niece Monday	Information	Assertive
 	You: Can you do a call @Cathy – does she ever sleep lol!	Problem solving & Management	Directive & Expressive
 	Cathy: I can try to get her to bed by nine	Problem solving	Commisive
Vignette 7	Submission: It is deadline day and the business plan got submitted by Ali this morning	 	 
Time	  ----yesterday----	 	 
Statement	Ali: have combined all the files. let me know if you want any changes before I submit	Task related & Management	Assertive & Directive
Statement	Cathy: my bit looks fine, thanks Ali	Task-related & Socio emotional	Assertive & Expressive
Statement	You: had a couple of changes to the costings to do – submit v18 OK @Ali?	Task related & Management	Assertive & Directive
Statement	Ali: got it!	 	Assertive
Statement	Bob: v18 looks good to me	Task related	Assertive
Time	   ----today----	 	 
Statement	Ali: just submitted have forwarded the email receipt to you all	Information	Declaration
Statement	Bob: thanks Ali	Socio emotional	Expressive
Statement	Cathy: thanks guys. As it’s the last week, how about we meet for a drink Friday?	Socio emotional & Management	Expressive & Directive


References

Dept. for Education (2017) Teaching Excellence Framework: subject level pilot specification.

Ellison, N.B., Steinfield, C. and Lampe, C. (2007) The benefits of Facebook “Friends:” Social capital and college students’ use of online social network sites, Journal of Computer-Mediated Communication, 12(4), pp. 1143-1168

Figueras Maz, M., Grandío Pérez, M. D. M., & Mateus, J. C. (2021). Students’ perceptions on social media teaching tools in higher education settings, Communication & Society, 34(1) 15-28.

Junco, R. (2012a) Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in human behavior, 28(1), 187-198.

Junco, R. (2012b) The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58(1), 162–171.

Pitafi, A. H., & Ren, M. (2021). Predicting the factors of employee agility using enterprise social media: moderating effects of enterprise social media-related strain. Internet Research. DOI 10.1108/INTR-11-2019-0469.

Searle, J.R., 1979. Expression and meaning: Studies in the theory of speech acts. Cambridge University Press.

Slade, T., Gross, D.P., Niwa, L., McKillop, A.B. and Guptill, C. (2021) Sex and gender demographic questions: improving methodological quality, inclusivity, and ethical administration, International Journal of Social Research Methodology, 24:6, 727-738.

Vaez, M. & Laflamme, L. (2002) First-year university students’ health status and socio-demographic determinants of their self-rated health, Work, 19, 71-80.

Wasiak, J., Hicks, B., Newnes, L., Dong, A. and Burrow, L., 2010. Understanding engineering email: the development of a taxonomy for identifying and classifying engineering work. Research in Engineering Design, 21(1), p.43-64.  


Socio-Technical Talk: How Speech Acts Coordinate Student Project Work in Digital Group Messaging

Rebollo-Neira, Laura

Simultaneous Optimized Orthogonal Matching Pursuit with Application to ECG Compression

Skog, Emil

Schofield, Andrew J.

Meese, Tim

Visual expertise for aerial- and ground-views of houses: No evidence for mental rotation, but experts are more incisive than novices

Tan, Mingming

Pratiwi, Dini

High Gain U-Band Discrete Raman Amplifier for Multi-Band Optical Transmission Systems

Po, Ho Fai

Houben, Akke Mats

Haeb, Anna-Christina

Raykov, Yordan

Tornero, Daniel

Soriano, Jordi

Saad, David

Research Data - Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics

Love, Robbie

Põldvere, Nele

1 
Appendices for Love & Põldvere (in press), Lexico-grammatical variation in spoken British English corpora 
Appendix 7.1. Statistical comparison (one-way independent ANOVA) of Factor 1 features for the Spoken BNC2014 and LLC-2 registers (p-values 
below the significance threshold p < 0.05 underlined). 
Factor 1 features Mean normalised frequency (per 100) i ANOVA 
Feature Loading BNC 2014 F2F 
Phone / 
CMC Broadcast Parliament Commentary Legal 
Prepared 
speech F p η²p Effect 
private verbs 0.96 2.6 3.11 2.63 2.75 1.32 1.56 1.8 1.69 38.77 < 0.001 0.153 large 
THAT deletion 0.91 1 1.19 0.93 0.93 0.3 0.38 0.5 0.3 101.34 < 0.001 0.242 large 
contractions 0.9 5.87 5.36 5.01 4.12 1.6 4.14 2.73 2.01 152.61 < 0.001 0.409 large 
2nd person pronouns 0.86 3.14 2.97 3.4 3.11 0.26 2.1 0.94 1.5 665.39 < 0.001 0.175 large 
present tense verbs 0.86 10.24 10.11 9.97 9.44 6.71 9.31 7.57 6.52 30.77 < 0.001 0.183 large 
DO as pro-verb 0.82 0.48 0.44 0.41 0.35 0.16 0.25 0.12 0.23 73.03 < 0.001 0.104 medium 
analytic negation 0.78 2.22 1.92 1.46 1.03 0.68 0.73 1.35 0.79 112.83 < 0.001 0.351 large 
demonstrative pronouns 0.76 1.06 1.01 0.98 0.97 0.73 1.09 0.8 0.71 5.91 < 0.001 0.037 small 
1st person pronouns 0.74 5.63 5.59 5.8 5.07 4.7 3.58 3.83 3.8 19.02 < 0.001 0.1 medium 
general emphatics 0.74 1.8 1.77 1.74 1.59 0.7 1.55 0.58 1.02 56.52 < 0.001 0.099 medium 
BE as main verb 0.71 3.56 3.3 3.6 3.17 1.9 2.29 2.75 2.18 76.44 < 0.001 0.27 large 
pronoun IT 0.71 3.25 2.88 2.6 2.59 0.96 2.6 2.33 1.41 61.16 < 0.001 0.216 large 
causative subordination 0.66 0.24 0.29 0.29 0.24 0.13 0.22 0.31 0.23 3.55 0.004 0.01 small 
discourse particles 0.66 0.07 0 0 0 0 0 0 0 NaN NaN 0.143 large 
indefinite pronouns 0.62 0.05 0.04 0.01 0.04 0.01 0.02 0.06 0.04 12.64 < 0.001 0.024 small 
general hedges 0.58 0.26 0.4 0.32 0.26 0.03 0.19 0.04 0.1 49.43 < 0.001 0.053 small 
amplifiers 0.56 0.23 0.24 0.37 0.41 0.42 0.34 0.35 0.25 4.88 < 0.001 0.048 small 
sentence relatives 0.55 0 0 0 0 0 0 0 0 NaN NaN 0.019 small 
WH questions 0.52 0.05 0 0 0 0 0 0 0 NaN NaN 0.097 medium 
possibility modals 0.5 0.86 0.86 0.57 0.68 0.68 1.06 0.72 0.63 6.10 < 0.001 0.04 small 
non-phrasal coordination 0.48 0.13 0.07 0.14 0.12 0.12 0.07 0.11 0.13 7.46 < 0.001 0.03 small 
WH clauses 0.47 0.23 0.25 0.2 0.19 0.09 0.13 0.15 0.11 10.89 < 0.001 0.074 medium 
stranded prepositions 0.43 0.06 0 0 0 0 0 0 0 NaN NaN 0.15 large 
2 
attributive adjectives -0.47 4.99 5.4 4.93 5.29 6.35 5.49 3.98 6.27 11.50 < 0.001 0.079 medium 
prepositions -0.54 5.45 5.82 6.46 7.59 10.15 8.19 10.12 9.35 75.23 < 0.001 0.532 large 
type/token ratio -0.54 40.19 40.61 42.77 45.08 51.10 43.98 43.81 48.24 32.95 < 0.001 0.202 large 
word length -0.58 3.66 3.77 3.82 4.03 4.56 4.06 4.3 4.47 126.27 < 0.001 0.621 large 
nouns -0.8 14.02 14.93 15.52 16.82 21.24 19.14 18.13 20.12 51.61 < 0.001 0.361 large 
3 
Appendix 7.2. Custom complex contrasts for Factor 1 features with significant omnibus effects for register (Holm-Bonferroni corrected p-values; 
p < 0.05 underlined; Cohen’s d > 0.8 emboldened). 
Factor 1 features BNC2014 F2F Phone / CMC Broadcast Parliamentary Commentary Legal Prepared 
Feature Loading p d p d p d p d p d p d p d p d 
private verbs 0.96 < 0.001 5.48 < 0.001 12.15 0.01 5.91 < 0.001 7.41 < 0.001 -11.35 < 0.001 -8.17 0.048 -5.02 < 0.001 -6.42 
THAT deletion 0.91 < 0.001 9.04 < 0.001 14.51 0.002 6.93 < 0.001 7.02 < 0.001 -11.39 < 0.001 -9.01 0.027 -5.60 < 0.001 -11.50 
contractions 0.9 < 0.001 17.74 < 0.001 13.31 < 0.001 10.17 0.145 2.35 < 0.001 -19.85 0.142 2.51 < 0.001 -9.93 < 0.001 -16.29 
2nd person pronouns 0.86 < 0.001 8.64 < 0.001 7.14 < 0.001 11.02 < 0.001 8.42 < 0.001 -17.29 0.63 -0.67 < 0.001 -11.16 < 0.001 -6.10 
present tense verbs 0.86 < 0.001 8.94 < 0.001 8.15 < 0.001 7.35 0.02 4.16 < 0.001 -11.99 0.014 3.40 0.018 -6.92 < 0.001 -13.10 
DO as pro-verb 0.82 < 0.001 7.51 < 0.001 5.75 0.108 4.66 0.265 1.80 0.012 -6.17 0.128 -2.57 0.01 -7.74 0.102 -3.24 
analytic negation 0.78 < 0.001 15.48 < 0.001 10.49 0.294 3.05 0.042 -3.98 < 0.001 -9.70 < 0.001 -8.74 0.62 1.26 < 0.001 -7.85 
demonstrative pronouns 0.76 < 0.001 3.75 0.172 2.29 0.445 1.61 0.854 1.28 0.085 -4.85 0.012 4.32 0.666 -3.10 < 0.001 -5.30 
general emphatics 0.74 < 0.001 6.87 < 0.001 6.44 0.012 6.02 0.04 3.74 < 0.001 -9.68 0.028 3.04 < 0.001 -11.54 0.004 -4.89 
1st person pronouns 0.74 < 0.001 5.60 < 0.001 5.35 0.008 6.68 0.408 2.05 0.873 -0.33 < 0.001 -7.45 0.063 -5.85 < 0.001 -6.07 
BE as main verb 0.71 < 0.001 11.18 < 0.001 7.23 < 0.001 11.96 0.004 5.09 < 0.001 -14.80 < 0.001 -8.66 0.556 -1.49 < 0.001 -10.50 
pronoun IT 0.71 < 0.001 10.83 < 0.001 6.49 0.258 3.19 0.162 3.11 < 0.001 -16.05 0.084 3.20 0.997 0.01 < 0.001 -10.77 
causative subordination 0.66 1 -0.25 0.315 2.27 1 2.17 0.854 -0.30 0.048 -5.59 1 -1.15 1 3.47 1 -0.64 
indefinite pronouns 0.62 < 0.001 2.90 0.752 1.49 0.105 -5.10 0.736 1.45 0.138 -4.60 0.675 -1.68 0.41 4.40 0.457 1.14 
general hedges 0.58 < 0.001 2.48 < 0.001 8.12 0.051 5.01 0.236 2.52 < 0.001 -6.89 0.654 -0.62 0.045 -6.64 0.036 -3.99 
amplifiers 0.56 < 0.001 -4.80 < 0.001 -4.49 0.969 2.08 0.054 4.23 0.08 4.71 1 0.87 0.599 1.33 0.05 -3.94 
possibility modals 0.5 < 0.001 2.86 0.066 2.89 0.07 -5.18 0.594 -2.08 0.588 -2.13 < 0.001 8.39 0.654 -1.14 0.072 -3.61 
non-phrasal coordination 0.48 0.048 1.79 < 0.001 -3.93 1 2.63 1 1.28 1 0.44 0.014 -4.21 0.958 0.13 0.88 1.88 
WH clauses 0.47 < 0.001 4.98 < 0.001 7.09 0.414 3.12 0.57 1.73 0.005 -6.64 0.056 -3.42 0.532 -1.58 < 0.001 -5.28 
attributive adjectives -0.47 < 0.001 -3.26 1 0.55 0.272 -3.85 0.799 -0.41 < 0.001 9.55 0.951 1.39 < 0.001 -12.78 < 0.001 8.81 
type/token ratio -0.54 < 0.001 -23.50 < 0.001 -19.98 < 0.001 -13.75 0.076 -2.86 < 0.001 21.76 0.076 2.88 < 0.001 21.44 < 0.001 14.02 
prepositions -0.54 < 0.001 -10.18 < 0.001 -9.19 0.22 -4.05 1 1.45 < 0.001 15.76 0.792 -1.18 0.536 -1.57 < 0.001 8.96 
word length -0.58 < 0.001 -27.15 < 0.001 -19.75 < 0.001 -16.67 0.054 -3.58 < 0.001 30.44 0.273 -1.52 < 0.001 13.95 < 0.001 24.27 
nouns -0.8 < 0.001 -15.40 < 0.001 -11.36 < 0.001 -8.74 0.13 -2.98 < 0.001 16.66 < 0.001 7.31 0.263 2.84 < 0.001 11.66 
4 
Appendix 7.3. Statistical comparison (Welch’s two-sample t-test) of Factor 1 features for female and male speakers in the combined Spoken 
BNC2014 and LLC-2 corpora (p-values below the significance threshold p < 0.05 underlined). 
Factor 1 features 
Mean normalised frequency per 
100 words (M)ii 
Welch’s t-test 
Feature Loading M (female) M (male) d.f. t p r 95% CI Overuse 
private verbs 0.96 2.63 2.44 2039.97 6.34 < 0.001 0.13 [0.09, 0.17] female 
THAT deletion 0.91 1.01 0.93 2083.04 5.32 < 0.001 0.11 [0.07, 0.15] female 
contractions 0.9 5.87 5.55 2017.60 6.37 < 0.001 0.13 [0.09, 0.17] female 
2nd person pronouns 0.86 3.19 3.00 2152.10 3.94 < 0.001 0.08 [0.04, 0.12] female 
present tense verbs 0.86 10.15 10.07 2127.50 1.17 0.242 0.02 [-0.02, 0.07] female 
DO as pro-verb 0.82 0.49 0.43 2167.18 6.14 < 0.001 0.13 [0.09, 0.17] female 
analytic negation 0.78 2.29 2.01 2119.93 10.14 < 0.001 0.21 [0.17, 0.25] female 
demonstrative pronouns 0.76 1.04 1.10 1993.14 -3.92 < 0.001 -0.08 [-0.12, -0.04] male 
1st person pronouns 0.74 5.70 5.24 2141.41 7.48 < 0.001 0.16 [0.12, 0.2] female 
general emphatics 0.74 1.87 1.55 2201.15 13.97 < 0.001 0.28 [0.24, 0.32] female 
BE as main verb 0.71 3.54 3.51 1996.04 0.88 0.377 0.02 [-0.02, 0.06] female 
pronoun IT 0.71 3.21 3.22 2022.53 -0.33 0.744 -0.01 [-0.05, 0.03] male 
causative subordination 0.66 0.24 0.19 2130.42 6.71 < 0.001 0.14 [0.1, 0.18] female 
discourse particles 0.66 0.06 0.06 1975.77 -1.35 0.178 -0.03 [-0.07, 0.01] male 
indefinite pronouns 0.62 0.05 0.05 1865.71 0.46 0.646 0.01 [-0.03, 0.05] female 
general hedges 0.58 0.26 0.25 1975.03 0.50 0.617 0.01 [-0.03, 0.05] female 
amplifiers 0.56 0.24 0.24 2130.11 0.75 0.453 0.02 [-0.03, 0.06] female 
sentence relatives 0.55 0.00 0.00 2114.77 0.96 0.337 0.02 [-0.02, 0.06] female 
WH questions 0.52 0.06 0.06 2206.33 0.39 0.698 0.01 [-0.03, 0.05] female 
possibility modals 0.5 0.86 0.87 2044.65 -0.56 0.578 -0.01 [-0.05, 0.03] male 
non-phrasal coordination 0.48 0.13 0.12 2137.49 3.72 < 0.001 0.08 [0.04, 0.12] female 
WH clauses 0.47 0.23 0.20 2110.65 6.87 < 0.001 0.14 [0.1, 0.18] female 
final prepositions 0.43 0.06 0.05 2110.99 1.91 0.056 0.04 [0, 0.08] female 
attributive adjectives -0.47 5.06 5.26 2150.86 -4.48 < 0.001 -0.09 [-0.13, -0.05] male 
prepositions -0.54 5.36 5.82 1814.51 -8.87 < 0.001 -0.19 [-0.23, -0.15] male 
type/token ratio -0.54 41.21 41.40 2099.41 -7.64 < 0.001 -0.16 [-0.2, -0.12] male 
mean word length -0.58 3.66 3.72 2080.56 -7.03 < 0.001 -0.15 [-0.19, -0.11] male 
5 
 
 
i Normalised frequency (per 100 words) is reported here for all features with the exception of type/token ratio and mean word length. For type/token ratio, MAT (Nini, 2019) 
produces a raw count of the number of types in the first 400 words of each text; here, we report the mean of this count per text, divided by 4 (as per Biber, 1988: 238). Mean 
word length is derived from the raw count of the number of orthographic letters per word; here, we report the mean word length for all texts. 
ii See note (i). 
nouns -0.8 13.91 15.06 2033.06 -12.08 < 0.001 -0.25 [-0.29, -0.21] male 


Data supporting "Lexico-grammatical variation in spoken British English corpora"

Roberts, Brian

Data underpinning article "A re-examination of duplex perception with musical chords"

Wilson-Whitford, S. R.

Kramer, D. J.

Roffin, MC

Gao, J

Gilchrist, J. F.

Fig_Data_Sedimenting_Microrollers_Navigate_Saturated_Porous_Media_for_publication

Hawkins, Olivia

Potter, Thomas

Broadbent, Luke

Kitchen, Philip

Goddard, Alan

Rothnie, Alice

Data underpinning 'Overcoming divalent cation sensitivity is not the only challenge for functional study of ABC transporters within polymer lipid particles.'

Senior, Carl

Physical Activity Project / Ongoing Public Involvement Exercise

Aston University

Not a member yet