USING MRQAP TO ANALYSE THE
DEVELOPMENT OF MATHEMATICS PRE-
SERVICE TRAINEES’COMMUNICATION
NETWORKS
Christian Bokhove and Chris Downey
University of Southampton
Jasperina Brouwer
University of Groningen

Context
Teacher training in UK
• PGCE University Led (UL)
• School Direct (SD)
→ Newly Qualified Teacher
Secondary Maths and Science
• cohort size (~35)
• “sink or swim”
• longevity of course
Social (support)Networks play a role

Social Network Analysis (SNA) as method

Dynamic SNA
From a previous project: networks change over time.

Data collection
• Longitudinal - 4 ‘waves’ of data collection (per 2 months)
• Directed network question: “During the last month, to
whom have you turned for support?”
• Both instrumental and affective aspects of support
• Online questionnaire instrument
Network Related factors
Peer
(whole)
External
(ego)
Trust Network
intent
Support
views
Self-
efficacy
1    
2     
3      
4      
See Bokhove & Downey (2018) for more info on measures

Longitudinal development
• Visual representation of the development of mathematics
trainees’ communication networks.
• Red nodes trainees, arrows indicate communication
• For every trainee, every red node, the attributes gender,
program, trust and self-efficacy were collected
• The figure shows a development from a tight knit community to
a situation where some trainees go their own way.

MRQAP
• Studies have used multiple (logistic) regression or
Quadratic Assignment Procedures (QAP) for a bivariate
association between two networks.
• But longitudinal data violate independence.
• Multiple regression quadratic assignment procedures
(MRQAP)
• In our case we would be able to test whether gender,
program, trust or self-efficacy predict the development of
communication networks.
• The interpretation of the standardised betas is similar to
OLS regression analyses.
• We use R statistical software with the asnipe package.

Results
The program (UL or SD) is a negative predictor in all final
models. Trust only is a positive predictor from timepoints 3 to 4,
perhaps indicating that by that time, towards the end of the year
of training, it is trusted peers that are mainly sought.
The table
shows that
the
configuration
of previous
networks
predict future
networks.

Other methods – other SNA
• Teacher student interaction
• Whole class, directionality
• Student interactions
• Groups and cliques
• Individual behaviour
• Help seeking
• Disturbances
• Central students
• Perhaps, patterns over classes,
schools, countries (analogue
TIMSS video study)
• Longitudinal: STERGM, RSIENA

Conclusions
• Social phenomena can be modelled with SNA.
• Networks change; how can we describe this change?
• Visualisations.
• Descriptive statistics and inferential statistics.
• MRQAP is one example of this.
• Working on a comparison article of popular methods
(Bokhove, Brouwer, & Downey, in preparation).
• These methods can be complemented by rich qualitative
data (and vice versa).

QUESTIONS/DISCUSSION
C.Bokhove@soton.ac.uk
Twitter: @cbokhove

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