the norbeck social support questionnaire ( nssq ) [ 1 , 2 ] is a network - based social support inventory .   that is , unlike global support measures which assess overall perception of how supported one feels , the nssq asks participants to take detailed stock or inventory of ( a ) how many supportive network members they have and ( b ) how much affection , affirmation , and aid each network member provides . the strength of a network - based inventory is that it allows testing of multiple social support hypotheses , which are impossible to test using global measures of social support . moving from general to specific , one can investigate the effects of total functional support ( affect , affirmation , and aid ) from the entire network , each of the three types of support from the entire network , total functional support from each network relationship ( e.g. , total functional support from spouse , friends , etc . ) , or each of the three types of support from each network relationship ( e.g. , affect from spouse ) .   despite the advantages of being a network based inventory , house and kahn   argued that the nssq scoring system both creates extraneous variance and is a source of measurement error . they noted that network size varies considerably , because participants can nominate up to 24 network members . thus , when participants ' support ratings of network members are summed , nssq support scores may be confounded from extraneous variance from network size , and this is especially problematic when using total network scores versus specific network relationship scores . that is , because more network members implies more support , support scores from the entire network most heavily reflect both support ratings and number of supporters listed .   recognizing this problem , some investigators use averaged scores ( support ratings divided by network size ) to remove the influence of network size variability . however , though effective in removing the effects of network size variability , as fully detailed below , norbeck   discouraged this practice , cautioning that averaging can unduly lower scores of some participants with large networks .   though this may be true , to date , the effects of averaging on support scores as network size increases have not been investigated . moreover , because extraneous variance due to network size variability in raw scores increases measurement error , averaged scores continue to be used . in fact , 23% of nssq - based studies published since norbeck 's caution in 1995 report averaged scores . the purpose of this paper is twofold : first , using three different data sets , we evaluate norbeck 's   concern by investigating whether averaged support scores do indeed decrease as network size increases . second , we evaluate the statistical efficiency of averaged versus raw scores by comparing the powers of their correlations with a reasonable criterion variable . the conceptual basis for the nssq [ 1 , 2 ] is kahn 's   definition of social support , and it thus includes measures of functional support ( affect , affirmation , and aid ) , network size , and network relationships .   for this reason , the nssq was commended in house and kahn 's   classic work on social support concepts and measures , where they urged investigators to consider all three of these aspects of support , because network size is a  necessary condition and hence a partial determinant ( page 85 ) of network relationships and the types of support given .   due to its comprehensive scope and extensive ongoing psychometric evaluation [ 1 , 2 , 4 , 68 ] , it is one of the most widely used social support measures in nursing research . in fact , since its inception in 1981 , the nssq has been used in over 250 studies published in peer - reviewed journals , and its use increases each year . one reason for its widespread use is that unlike other network - based support inventories , the nssq is completed by the participant without input from an interviewer . this makes it ideal for use in large - scale studies such as mailed surveys . because of this self - report feature , the nssq requires a unique layout . specifically , participants are first asked to list from 1 to 24 network members  who provide personal support for you or who are important to you  and then specify their relationship ( spouse , parent , friend , etc . ) .   after completing the network list , they are instructed to successively turn the half pages and rate each listed network member ( 04 ) on six functional support questions measuring three types of support : affect , affirmation , and aid ( see table 1 ) . normative data ( n = 1,067 )   shows that the average network size is 10.9 members , but the high standard deviation of this average ( 5.9 ) reveals the considerable variability between reported network sizes . this is because participants ' network size is dependent on many factors , including how influenced participants are by the presence of 24 spaces as well as the size of their immediate and extended family . for example , family ( other than spouse ) is the most often listed relationship , so a participant with two living parents , a spouse , and four children may list up to seven immediate family members plus supportive siblings , friends , neighbors , and so forth . in contrast , a participant with deceased parents , a spouse , and two children will only have three possible immediate family members to list .    because support ratings for each network member are summed , support scores ( range = 0576 ) vary greatly due to network size alone . thus , the above participant with seven immediate family members functionally inflates his / her support score . in fact , in the three samples [ 911 ] used in the present study , network size was very highly correlated with affect scores ( .95 ,  .94 ,  and  .95 ,  resp . ) and affirmation scores ( .92 ,  .90 ,  and  .92 ,  resp . ) and a bit less with aid scores ( .81 ,  .82 ,  and  .82 ,  resp . ) . it is likely that aid 's lower correlations with network size are the result of more participants giving some network members aid ratings of 0 than giving 0 's for affect or affirmation ratings . when this happens , the participant has effectively dropped that person from their network , and thus reduced the influence of network size on that support score . this happens most often with aid , because some participants list network members who may like ( affect ) and agree with ( affirmation ) them but be unable to provide tangible help ( aid ) such as children , elderly parents , and peripheral network members . in fact , in the present study 's third sample , where data were entered at this level of specificity ( i.e. , affect , affirmation , and aid scores from each network member ) , it was determined that for aid scores , 33% of participants gave a 0 aid rating to at least 1 network member , and 20% of participants gave more than 1 network member a 0 aid rating . in contrast , only 9% of participants gave a 0 affect rating , and 19% gave a 0 affirmation rating . few participants gave more than one network member a 0 rating for affect or affirmation questions . in summary , though nssq support scores are meant to measure quantity of support , they have two determinants of variability : support ratings and network size . therefore , raw support scores can not be taken at face value but should be viewed as support ratings relative to network size . for this reason , many investigators remove the influence of network size variability by averaging nssq scores , that is , dividing the support score by the network size . averaging is not a problem when participants rate all network members uniformly highly or lowly . for example , participant a lists 7 highly supportive network members , and participant b lists 14 equally supportive members . b 's score ( 305 ) is higher than a 's ( 154 ) only because she listed more network members . however , a 's and b 's averaged support scores ( a ( 154/7 = 22 ) and b ( 305/14 = 21.79 ) ) reflect their support quality relative to their respective network sizes . this is because only their network sizes varied ; their support ratings were consistently high . all of their network members uniformly liked them ( affect ) , agreed with them ( affirmation ) , and could help them ( aid ) , but this equality among network members is not typical .    in a typical nssq network , only a few supporters give large amounts of all three types of support and the others contribute in varying degrees , and this reflects reality . that is , some network members make one feel loved and/or are good confidants but can not offer tangible support and vice versa . this pattern is typified by participant c : like b , participant c has a relatively large network ( 14 ) , but unlike b , her network members ' ratings were more varied . c rated 7 network members highly on most support questions but varied the ratings of the other 7 network members giving some high and some low ratings for some types of support . though both a and c each have 7 highly supportive network members and c has 7 additional network members giving some support , c 's averaged score ( 277/14 = 19.79 ) is 2.21 points lower than a 's ( 154/7 = 22 ) . because of variations in her ratings , unlike b , c 's numerator ( support score ) did not keep up with her denominator ( network size ) . of course , it is possible that this  deflation  of averaged scores happens at all network sizes . in fact , if averaging lowered scores consistently for all participants , then lowered scores due to averaging would be the norm and would result in true regressions to the mean . thus , as is the case with uniformly high or low ratings , if all participants vary their support ratings averaging is again not a problem . norbeck   was concerned , however , because participants ' support ratings show increased variability with increased network size . that is , the more people one has in his / her network ( denominator ) , the more room for variability of ratings ( numerator ) and the more chance that one 's score will be unduly lowered by averaging if the numerator does not keep pace with the denominator .   indeed , we saw previously that raw support scores increase as network size increases as evidenced by the high positive correlations . thus , the question is whether averaging results in a statistically significant lowering of ( averaged ) scores as network size increases . the potential for this is greatest for total network scores and less so for source - specific scores due to the smaller denominators ( one relationship category ) . nevertheless , though norbeck 's   concern about the effects of averaging may be warranted , one must also be aware of the effects of using support scores which contain variability due to network size . for example , if high support is related to low stress but support scores are reflective of support as well as network size , it is likely that the size of the relation between support and stress will be reduced . this is due to measurement error or the extraneous variance present in support scores resulting from network size variability . thus , the risk of making a type 2 error ( failing to detect a real effect ) may be greater when using raw support scores , and the deleterious effects of averaging , if any , must be weighed against their beneficial effects in terms of explained variance in the criterion variable . is there a statistically significant negative correlation between averaged total functional support scores ( entire network ) and number in network ? in addition , are there statistically significant negative correlations between averaged affect , affirmation , and aid scores ( entire network ) and number in network ? does using averaged total functional support scores provide a measure less infected with extraneous variance and thus produce a more efficient measure than raw total functional support scores ? do averaged total functional support scores yield higher powers than raw scores produced under the same conditions ? similarly , do averaged affect , affirmation , and aid scores provide an analysis with a higher power than respective analyses with raw scores ? with institutional review board approval , a secondary analysis was conducted on data from three different samples [ 911 ] of women who were mothers attending college for their first postsecondary school degree . the same data collection protocol was used in all three studies : participation was invited during a brief in - class presentation of the study , and participants completed the self - administered surveys on their own time and anonymously returned surveys in postage - paid envelopes addressed to the first author . response rates were high ( 66% , 45% , and 57% , resp . ) . along with the nssq , participants completed the perceived multiple role stress scale   and role involvement questionnaires   as well as a demographic data sheet . table 2 shows that these samples ' ( ( n = 157 ) ; ( n = 263 ) ; ( n = 189 ) ) parametric properties are consistent with norbeck 's   normative data for community dwelling adult females ( n = 1,067 ) .   though norbeck gives no information concerning network size distribution , the majority ( 75% ) of participants in the present study 's samples listed 14 network members , and 50% listed 10 - 11 members . all samples ' total functional support scores were statistically significantly nonnormal , showing positive skews ( 5.78 , 3.32 , and 5.60 , resp . ) , and all except sample two showed statistically significantly positive kurtoses ( 3.5 and 4.54 ) .   these findings are consistent with findings concerning normality throughout the nssq literature . that is , due to high network size , some participants have very high support scores . research questions 1 and 2.using pasw 18   with a.05 alpha level , a bivariate pearson correlation was computed to determine if there was a statistically significant negative correlation between averaged total functional support scores ( summed affect , affirmation , and aid scores from the entire network divided by total number in network ) and number in the network . in addition , separate correlations were computed to determine if there were statistically significant negative correlations between averaged affect scores , averaged affirmation scores , and averaged aid scores from the entire network and network number .   because the effect of network size is removed from averaged scores , a significant decrease in averaged scores as network number increases ( i.e. , a significant negative correlation between averaged scores and network number ) would support the claim that averaging scores indeed unduly lowers support scores as network size increases . using pasw 18   with a.05 alpha level , a bivariate pearson correlation was computed to determine if there was a statistically significant negative correlation between averaged total functional support scores ( summed affect , affirmation , and aid scores from the entire network divided by total number in network ) and number in the network . in addition , separate correlations were computed to determine if there were statistically significant negative correlations between averaged affect scores , averaged affirmation scores , and averaged aid scores from the entire network and network number .   because the effect of network size is removed from averaged scores , a significant decrease in averaged scores as network number increases ( i.e. , a significant negative correlation between averaged scores and network number ) would support the claim that averaging scores indeed unduly lowers support scores as network size increases . research questions 3 and 4.in order to answer these research questions , pmrs   was used as the criterion variable . pmrs is the amount of role stress experienced by women who are both mothers and students . it has been hypothesized that increased social support is related to decreased levels of pmrs [ 911 , 13 ] .   using the same software and criteria as above , we tested the correlations between pmrs and both raw and averaged total functional support scores and affect , affirmation , and aid scores.using g * power 3 , we then computed the power of each of the tests given the respective sample sizes , correlations with pmrs of both the various raw and the averaged scores , and  = .05 . analogous to the power of a microscope , tests with higher powers can detect finer differences ( and thus avoid more type ii errors ) than less powerful tests conducted under the same level of statistical rigor . although there is no established standard for minimally acceptable levels of power , cohen   suggested that power should be at least  .80 . that is , there is an 80% chance of finding a real , significant effect .   given that averaged scores retain the same information about support quantity as do raw scores but remove the variance associated with differences in network sizes , we expected higher powers among averaged scores than among raw scores . 
  in order to answer these research questions , pmrs is the amount of role stress experienced by women who are both mothers and students . it has been hypothesized that increased social support is related to decreased levels of pmrs [ 911 , 13 ] . using the same software and criteria as above , we tested the correlations between pmrs and both raw and averaged total functional support scores and affect , affirmation , and aid scores . using g * power 3 , we then computed the power of each of the tests given the respective sample sizes , correlations with pmrs of both the various raw and the averaged scores , and  = .05 . analogous to the power of a microscope , tests with higher powers can detect finer differences ( and thus avoid more type ii errors ) than less powerful tests conducted under the same level of statistical rigor . although there is no established standard for minimally acceptable levels of power , cohen   suggested that power should be at least  .80 . that is , there is an 80% chance of finding a real , significant effect .   given that averaged scores retain the same information about support quantity as do raw scores but remove the variance associated with differences in network sizes , we expected higher powers among averaged scores than among raw scores . research questions 1 and 2results are presented in table 3 .   in all three samples , there are no statistically significant decreases in averaged total functional support scores as network size increases .   nor are there statistically significant decreases in averaged affect or affirmation support scores as network size increases . thus , averaged total functional support scores and averaged affect and affirmation scores do not unduly lower scores as one 's network size increases.however , this is not true for averaged aid scores . in all three samples , there are statistically significant decreases in averaged aid support scores as network size increases . these results are most likely due to the aforementioned high percentage of participants who rated some network members 0 ( none at all ) for one or both of the aid questions . when participants scored a network member as providing 0 aid , it was most often for network members mentioned later in the network list . that is , it appears that participants begin completing the list of network members by nominating their closest supporters followed by more peripheral supporters . thus , with the exception of young children and elderly parents , these close supporters are likely able to offer more tangible help than the others . when rating members as providing 0 aid , participants already reduced the influence of network size , and averaging penalized them further , because the denominator was not adjusted to account for this .   averaging does indeed unduly lower aid scores of participants as network number increases . results are presented in table 3 .   in all three samples , there are no statistically significant decreases in averaged total functional support scores as network size increases .   nor are there statistically significant decreases in averaged affect or affirmation support scores as network size increases . thus , averaged total functional support scores and averaged affect and affirmation scores do not unduly lower scores as one 's network size increases . however , this is not true for averaged aid scores .   in all three samples , there are statistically significant decreases in averaged aid support scores as network size increases . these results are most likely due to the aforementioned high percentage of participants who rated some network members 0 ( none at all ) for one or both of the aid questions . when participants scored a network member as providing 0 aid , it was most often for network members mentioned later in the network list . that is , it appears that participants begin completing the list of network members by nominating their closest supporters followed by more peripheral supporters . thus , with the exception of young children and elderly parents , these close supporters are likely able to offer more tangible help than the others .   when rating members as providing 0 aid , participants already reduced the influence of network size , and averaging penalized them further , because the denominator was not adjusted to account for this . averaged total functional support scores , averaged affect scores , and averaged affirmation scores resulted in higher powers when correlated with pmrs than their respective raw scores . these results are most dramatic in samples one and three , where powers increased  .50 ( .47 to  .97 ) and  .66 ( .13 to  .79 ) , respectively . though sample two 's results do not show as marked an improvement , gains in power did considerably improve their probabilities by  .24 to  .35 points .   it should be noted that  all else being equal  larger sample sizes will yield higher power ; sample two had 74 more participants than sample one and 106 more participants than sample three . moreover , we found no statistically significant lowering of averaged scores , as network size increased when investigating research questions 1 and 2   above . therefore , we recommend using averaged total functional support scores and averaged affect and affirmation scores.however , averaged aid scores did not perform as well as raw aid scores .   the gains in power were modest ( .15 and  .20 ) for samples one and three , while power actually decreased in sample two by  .19 .   thus , in light of results of research questions 1 and 2 showing the statistically significant lowering of averaged aid scores as network size increases and the equivocal effect on power of a test correlation , use of averaged aid scores is not recommended . averaged total functional support scores , averaged affect scores , and averaged affirmation scores resulted in higher powers when correlated with pmrs than their respective raw scores . these results are most dramatic in samples one and three , where powers increased  .50 ( .47 to  .97 ) and  .66 ( .13 to  .79 ) , respectively . though sample two 's results do not show as marked an improvement , gains in power did considerably improve their probabilities by  .24 to  .35 points .   it should be noted that  all else being equal  larger sample sizes will yield higher power ; sample two had 74 more participants than sample one and 106 more participants than sample three . moreover , we found no statistically significant lowering of averaged scores , as network size increased when investigating research questions 1 and 2   above . therefore , we recommend using averaged total functional support scores and averaged affect and affirmation scores . however , averaged aid scores did not perform as well as raw aid scores . the gains in power were modest ( .15 and  .20 ) for samples one and three , while power actually decreased in sample two by  .19 . thus , in light of results of research questions 1 and 2 showing the statistically significant lowering of averaged aid scores as network size increases and the equivocal effect on power of a test correlation , use of averaged aid scores is not recommended . averaging reduces the influence of varied network size , but norbeck   was concerned that if support ratings decrease ( lower numerators ) as network size increases ( higher denominators ) , averaging may unduly lower scores as network size increases . it was found that averaging does not significantly lower total functional support scores or affect and affirmation scores as network size increases . furthermore , these averaged scores improve analyses by decreasing measurement error as demonstrated by an increase in power . use of these averaged scores is acceptable , given the underlying nature of the data and the improvements in power . however , norbeck 's   concern about averaging is well founded regarding averaged aid scores .   because network size 's influence on aid scores was already reduced by participants ' ratings of 0 ( none at all ) for some network members , averaged aid scores unduly penalize participants as network size increases .   moreover , reduction in measurement error improved only slightly in two samples , and measurement error actually increased in one sample .
background . averaged norbeck social support questionnaire ( nssq ) support scores 
 remove the influence of network size variability but may unduly lower scores for participants with large networks . objectives . to evaluate the use of averaged nssq scores 
 . method . 
 pearson correlations determined if averaged scores decreased as network size increased across three samples . then , pearson correlations between a criterion variable and both averaged and raw support scores were computed along with the resultant power to detect a true effect . results . neither averaged total functional support nor averaged affect and affirmation scores decreased as sample size increased . however 
 , averaged aid scores did decrease as network size increased . 
 power also increased markedly in all averaged versus raw scores except in averaged aid scores . 
 discussion and conclusions . 
 use of averaged aid scores is not recommended . 
 use of all other averaged scores appears acceptable .