Analysing the data 154

In  this  section,  I  will  explain  the  steps  taken  to  analyse  the  data,  beginning  with   thematic  analysis,  deeper  exploration  using  line-­‐by-­‐line  coding,  improving   trustworthiness  by  approaching  the  data  a  third  time  using  a  mindmapping   technique  and  additional  graphical  software,  and  finally,  using  a  theoretical   framework  to  create  a  composite  analysis.    

There  are  obviously  a  large  number  of  ways  of  analysing  qualitative  data,  

and  no  single  ‘correct’  way.  Rather,  the  goal  of  a  qualitative  analysis  strategy   should  be  to  impose  systematic  order  on  the  data,  immerse  oneself  in  it,  and  align   the  analysis  with  the  overall  research  questions  and  epistemology  underlying  the   project.  This  comprised  a  number  of  steps  that  were  a  combination  of  pre-­‐defined   process  and  evolution  as  the  needs  of  the  project  were  made  increasingly  apparent.  

Step  1:  Generating  initial  codes  in  thematic  analysis  

Thematic  analysis  (TA)  is  a:    

‘poorly  demarcated,  rarely  acknowledged,  yet  widely  used  qualitative  

analytic  method…’  (Braun  and  Clarke,  2006,  p.77)  

In  it,  the  researcher  seeks  the  themes  in  the  data.  As  such,  this  method  is  not   bound  to  philosophical  perspective  (in  contrast  to,  say,  Interpretive  

Phenomenological  Analysis,  which  is  aligned  with  phenomenology).  This  lack  of  

demarcation  means  that  the  conscientious  researcher  must  be  scrupulously   detailed  in  outlining  the  method  used  to  generate  the  themes.      

  This  project  used  ‘theory-­‐led’  TA  (Coolican,  2009)  as  it  drew  on  existing   theories  surrounding  social  cognition  and  adolescence.  It  did,  however,  also   include  an  element  of  ‘inductive’  TA,  as  the  technological  element  was  explored   without  a  preconceived  conceptual  framework.  As  a  result  of  this  tension,  I  began   the  coding  process  by  completing  an  initial  axial  coding  of  each  tool  in  NVIVO.  I  

used  pre-­‐determined  themes  as  codes  as  well  as  themes  emerging  from  the  data.   The  pre-­‐determined  codes  were  high  level,  related  directly  to  the  research   questions  and  without  granularity:  

Attachment           Mentalizing/ToM  

Attribution           Self  

Identity           Risk  

Technology           Adolescence  

Axial  coding  has  been  criticized  for  distracting  researchers  from  the  themes   emerging  from  the  analysis  (e.g.  Glaser,  1992).  Given  the  conceptual  framework   within  which  this  project  was  operating,  I  did  not  see  that  an  open  coding   approach  would  be  a  productive  first  step.    

Step  2:  Deeper  exploration    

Once  the  data  set  had  been  reviewed  and  broadly  coded,  I  revisited  each  data   source  in  NVIVO,  and  carried  out  ‘line-­‐by-­‐line’  coding,  during  which  a  significant   number  of  codes  and  sub-­‐codes  emerged  alongside  the  initial  axial  codes.  A  full  list   of  codes  and  sub-­‐codes  may  be  found  in  Appendix  5.        

  In  parallel,  during  this  step  I  explored  the  data  emerging  from  the  surveys.   These  findings  had  to  be  handled  carefully  to  ensure  they  were  used  to  illuminate  

the  research  questions  in  tandem  with  more  traditionally  qualitative  data,  rather   than  to  over-­‐claim  or  claim  generalizability  for  the  population.    

Step  3:  Increasing  trustworthiness  

It  became  clear  at  this  point  that  despite  the  repeated  revisiting  of  the  data,  and   the  attempts  to  be  reflexive  in  that  process  (described  in  more  detail  in  Section  6   of  this  chapter  below),  I  needed  to  introduce  an  additional  layer  of  analysis  to   provide  another  ‘way  into’  the  data,  away  from  NVIVO.  This  would  have  the   important  added  benefit  of  providing  an  additional  layer  of  robustness;  if  the   themes  and  findings  emerging  from  this  new  coding  correlated  with  those  from   the  work  in  NVIVO,  I  could  be  increasingly  confident  that  the  findings  were   reflective  of  the  reality.  This  explanatory  framework  resulting  from  these  

additional  reflections  should,  insofar  as  possible,  be  ‘saturated’,  as  a  grounded   theorist  might  put  it  –  that  is,  additional  data  should  not  alter  the  themes  and   findings  emerging.  In  order  to  do  this,  I  used  two  techniques.    

Firstly,  I  once  again  revisited  each  data  source,  and  focused  this  time  on  

emerging  findings  that  responded  to  each  research  question  rather  than  the  axial   codes  I  had  used  in  the  previous  step.  I  used  the  Mindnode  mindmapping  software   package  to  represent  my  thinking,  an  example  of  which  may  be  found  in  Appendix   6.  To  use  Mindnode  I  chose  a  main  node  from  the  NVIVO  analysis  and  placed  it  as   the  core  node  on  the  mindmap  map.  As  important  quotes  from  the  data  emerged  I   added  them  to  relevant  edges  (linking  nodes)  of  the  map.  Sub-­‐nodes  in  NVIVO   correlated  well  with  radial  edges  in  Mindnode.  

Secondly,  when  the  analysis  called  for  it,  I  used  yEd  to  create  diagrammatic   representations  of  the  data  using  nodes  to  represent  key  points,  with  edges  linking   the  nodes.  yEd  is  graphical  software,  intended  to  enable  the  user  to  create  and   recreate  a  range  of  representations  of  a  single  data  set.  In  yEd  the  graphical  user   interface  (GUI)  allows  the  user  to  choose  nodal  points  and  designs,  and  then  to  

link  them  using  edges  by  clicking  from  one  node  to  another.  Edges  can  be  bi-­‐ directional,  uni-­‐directional,  or  not  indicate  direction.  As  before,  nodes  correlated   with  main  nodes  in  NVIVO.  I  used  yEd  because  it  offers  a  range  of  layout  

algorithms  enabling  creative  representation  of  the  data,  and  highlighting  relational   aspects  of  the  data  that  I  might  otherwise  have  missed.  Once  the  data  have  been   inputted,  the  user  can  easily  apply  a  layout  algorithm  from  the  list  of  hierarchical,   organic,  orthogonal,  circular,  tree,  radial  and  series  parallel.  For  each  of  the   concept  areas  I  implemented  in  yEd  I  tried  all  of  these  algorithms  before  choosing   the  representations  that  were  most  illuminating  and  honest.    

Step  4:  Composite  analysis,  or  bringing  it  all  together    

The  next  stage  was  to  bring  the  analyses  together  in  a  way  that  responded  to  the   research  questions.  The  goal  was  to  extend  the  thematic  analysis  by  placing  the   data  within  a  theoretical  framework.  The  framework  needed  to  (Damasio,  2012):  

-­‐ define  and  organize  the  resolved  themes  in  such  a  way  that  produced  a   strong,  complete  theoretical  response  to  the  research  questions  

-­‐ propose  explanations  for  the  findings  at  the  level(s)  at  which  these   explanations  could  apply  and  make  explicit  the  interconnectedness   between  levels  

-­‐ identify  the  findings  amenable  to  analytical  methods  available  to  social   research,  and  resolve  discrepancies  arising  from  the  previous  analytical  

stages  

-­‐ result  in  messages  for  young  women  and  the  adults  in  their  lives  (see   Chapters  5  and  6).