Firstly, excuse my naivity but I am just starting out in research.

Overview of study

I am doing a project looking at Social anxiety in adolescence and using social network analysis (SNA). I argue that negative peer relations add to the variance found in social anxiety above and beyond individual level characteristics (in this case personality variables)

I have the following variables:

• Dependent Variable: Social anxiety score

• Independent Variables:

  • Demographics: Ethnicity, SES from fathers income, SES from mothers income
  • Personality variables (Big 5): Neuroticism, Openness, Conscientiousness, Extroversion, Agreeableness
  • Network variables: Unilateral rejection (indegree of a dislike network), Mutual antipathy (sum of reciprocated dislike ties); relational dissonance (sum of dislike tie received with a like tie sent).

My total number of participants was 94.

From past research and as expected Social anxiety is predicted by Neuro and extroversion. Past research indicated that unilateral rejection should be associated with Social anxiety. In my study none of the network variables have an expected association with Social anxiety, none of them are significant predictors of Social anxiety. In fact it was only Neuroticism and extroversion that came anywhere close to significant. All other bivariate correlations were extremely low.

I believe the non significant results is largely due to methodological issues.

As regression assumes independent observations but SNA assumes interdependent then the analysis may not be able to pick up on the associations. Also with a low sample size there is not enough power.

OK. That is my story in a nutshell. I am not looking to find significant results I just want to know the best way to do a regression, so I at least know I am doing that correctly.

I would like to enter them in blocks as this gives the R squared change but I do not want to make any false assumptions and do something that is inappropriate.

Questions

• Should I use the hierarchical method or not? My initial thought was to have demographics in block 1; personality variables in block 2 and network variables in block 3.
• Or should I just enter the predictors simultaneously and report the results as they were not significant anyway?

Firstly, excuse my naivity but I am just starting out in research.

Overview of study

I am doing a project looking at Social anxiety in adolescence and using social network analysis (SNA). I argue that negative peer relations accounts for variance in social anxiety above and beyond individual level characteristics (in this case personality variables).

I have the following variables:

• Dependent Variable: Social anxiety score

• Independent Variables:

  • Demographics: Ethnicity, SES from fathers income, SES from mothers income
  • Personality variables (Big 5): Neuroticism, Openness, Conscientiousness, Extroversion, Agreeableness
  • Network variables: Unilateral rejection (indegree of a dislike network), Mutual antipathy (sum of reciprocated dislike ties); relational dissonance (sum of dislike tie received with a like tie sent).

My total number of participants was 94.

From past research and as expected Social anxiety is predicted by Neuro and extroversion. Past research indicated that unilateral rejection should be associated with Social anxiety. In my study none of the network variables have an expected association with Social anxiety, none of them are significant predictors of Social anxiety. In fact it was only Neuroticism and extroversion that came anywhere close to significant. All other bivariate correlations were extremely low.

I believe the non significant results is largely due to methodological issues.

As regression assumes independent observations but SNA assumes interdependent then the analysis may not be able to pick up on the associations. Also with a low sample size there is not enough power.

OK. That is my story in a nutshell. I am not looking to find significant results I just want to know the best way to do a regression, so I at least know I am doing that correctly.

I would like to enter them in blocks as this gives the R squared change but I do not want to make any false assumptions and do something that is inappropriate.

Questions

• Should I use the hierarchical method or not? My initial thought was to have demographics in block 1; personality variables in block 2 and network variables in block 3.
• Or should I just enter the predictors simultaneously and report the results as they were not significant anyway?

Firstly, excuse my naivity but I am just starting out in research.

I am doing a project looking at social anxiety in adolescence and using social network analysis (SNA). I argue that negative peer relations add to the variance found in social anxiety above and beyond individual level characteristics (in this case personality variables).

I have the following variables:

IV - Social anxiety score

DV's:

Demographics: Ethnicity, SES from fathers income, SES from mothers income

Personality variables (Big 5): Neuroticism, Openness, Conscientiousness, Extroversion, Agreeableness

Network variables: Unilateral rejection (indegree of a dislike network), Mutual antipathy (sum of reciprocated dislike ties); relational dissonance (sum of dislike tie received with a like tie sent).

My total number of participants was 94.

From past research and as expected SA is predicted by Neuro and extroversion. Past research indicated that unilateral rejection should be associated with SA. In my study none of the network variables have an expected association with SA, none of them are significant predictors of SA. In fact it was only Neuroticism and extroversion that came anywhere close to significant. All other bivariate correlations were extremely low.

I believe the non significant results is largely due to methodological issues.

As regression assumes independent observations but SNA assumes interdependent then the analysis may not be able to pick up on the associations. Also with a low sample size there is not enough power.

OK. That is my story in a nutshell. I am not looking to find significant results I just want to know the best way to do a regression, so I at least know I am doing that correctly.

I would like to enter them in blocks as this gives the R squared change but I do not want to make any false assumptions and do something that is inappropriate.

1: Should I use the hierarchical method or not? My initial thought was to have demographics in block 1; personality variables in block 2 and network variables in block 3. Or just enter them simultaneously and report the results as they were not significant anyway?

Firstly, excuse my naivity but I am just starting out in research.

Overview of study

I am doing a project looking at Social anxiety in adolescence and using social network analysis (SNA). I argue that negative peer relations add to the variance found in social anxiety above and beyond individual level characteristics (in this case personality variables)

I have the following variables:

• Dependent Variable: Social anxiety score

• Independent Variables:

  • Demographics: Ethnicity, SES from fathers income, SES from mothers income
  • Personality variables (Big 5): Neuroticism, Openness, Conscientiousness, Extroversion, Agreeableness
  • Network variables: Unilateral rejection (indegree of a dislike network), Mutual antipathy (sum of reciprocated dislike ties); relational dissonance (sum of dislike tie received with a like tie sent).

My total number of participants was 94.

From past research and as expected Social anxiety is predicted by Neuro and extroversion. Past research indicated that unilateral rejection should be associated with Social anxiety. In my study none of the network variables have an expected association with Social anxiety, none of them are significant predictors of Social anxiety. In fact it was only Neuroticism and extroversion that came anywhere close to significant. All other bivariate correlations were extremely low.

I believe the non significant results is largely due to methodological issues.

As regression assumes independent observations but SNA assumes interdependent then the analysis may not be able to pick up on the associations. Also with a low sample size there is not enough power.

OK. That is my story in a nutshell. I am not looking to find significant results I just want to know the best way to do a regression, so I at least know I am doing that correctly.

I would like to enter them in blocks as this gives the R squared change but I do not want to make any false assumptions and do something that is inappropriate.

Questions

• Should I use the hierarchical method or not? My initial thought was to have demographics in block 1; personality variables in block 2 and network variables in block 3.
• Or should I just enter the predictors simultaneously and report the results as they were not significant anyway?