Discussion
Started 24th Jun, 2021

Which method is good for analyzing social identities on forum posts?

Hi, I want to analyse people social identities from forum posts.
For example in a forum post "As a mother, I should look after my children" statement will be a signal of mother identity. I am planning to find out identities until saturation. When I achieved the saturation, I want to claim that these social identities are most salient when people considered do post on forums. It is kind of text analysing. Which method should I use to analyse data? Qualitative content analyses or Thematic analyses or something else?

Most recent answer

8th Aug, 2021
Ingrid Del Valle García Carreño
Universidad Pablo de Olavide
Good morning
Content Analysis plus Atlas.ti and semantic networds
Best regards
Ph.D. Ingrid del Valle García Carreño

All replies (5)

24th Jun, 2021
Ismail Shaheer
University of Otago
I used summative content analysis to analyse identities from Twitter Profiles. Thematic analysis should work too if your analysis does not need frequencies.
Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative health research, 15(9), 1277-1288.
25th Jun, 2021
Soumaya Elhassouni
Mohammed V University of Rabat
Hi Mustapha, I think that the analysis technique depends on the research objective. Thematic analysis is suitable only when themes are identified in advance. But if you are willing to make the study seem to have lead you to some "discovery", you should rather use the grounded theory
21st Jul, 2021
Samitha Udayanga
University of Ruhuna
The content analysis would be best. But invite you to appropriate narrative inquiry in this research. You may contextualize it into your research.

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Warning in Lavaan, variance-covariance not positive definite!, model is not identified?
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  • Mugaahed Abdu Kaid SalehMugaahed Abdu Kaid Saleh
Dear colleagues,
I am new to R, I have been using Amos for conducting CFA but came to learn that when dealing with Likert scale, Lavaan in R is a better tool to do such analysis.
I started using lavaan to conduct CFA and report its fit indices.
I have four factors after conducting EFA, 4 to 5 items under each factor.
I did the following:
cfamodel <- "
communication =~ Com1+Com2+Com3+Com4+Com5
networking=~ Net1+Net2+Net3+Net4+Net5
problemsolving =~ ProblemS1+ProblemS2+ProblemS3+ProblemS4
tech =~ Tech1+Tech2+Tech3+Tech4+Tech5"
fit <- cfa(cfamodel, data = rdata, ordered = c("Com1", "Com2", "Com3", "Com4", "Com5", "Net1", "Net2", "Net3", "Net4", "Net5", "ProblemS1", "ProblemS2" ,"ProblemS3" , "ProblemS4", "Tech1", "Tech2", "Tech3" ,"Tech4", "Tech5"))
However, I get this warning:
Warning message:
In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, :
lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= -2.260671e-18) is smaller than zero. This may be a symptom that
the model is not identified.
I tried finding similar issues online for potential answers, but I failed to find so.
I found one place in the internet talking about the same warning as for second order models, my model is not a second order model. in fact is only CFA after EFA, as I don't have dependent variables to go for SEM structure model.
I would like to know if this warning means I can not continue?
if there is a similar thread that I could not find, kindly share it here.
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Thank you very much in advance.
Mugaahed

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