Writing assignment 2:

Effects of antismoking campaigns on non-smokers and smokers




                  784 words in the ...
a) Introduction
A research of the influence of antismoking messages was carried out by Paek and Gunther

(2007). They used...
measurement model with Confirmatory Factor Analysis. When it did not fit acceptable, the

model was improved by reviewing ...
included, ECVI difference is not significant, and therefore it is assumed that models do not

differ significantly.

c) Re...
seen differently than for those who did. The model with equal factor loadings for the three

                             ...
The proportion of explained variance of behavioural intention in this model seemed to be

higher in the group of smokers t...
However the effect of media exposure to antismoking messages on attitude seems to be little

or not significant and even p...
groups. Constraining this effect to be equal across groups resulted in a significant decrease in

model fit.




         ...
References

Dudgeon, P. (2003). NIESEM: A computer program for calculating noncentral interval

       estimates (and powe...
Appendix 1: Correlation matrices and standard deviations
Non-smokers (N=902)
        1       2       3      4       5     ...
Smokers (N=391)
         1      2       3      4       5      6      7       8      9      10     11     12     13      14...
Structural equation modelling
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Structural equation modelling

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Structural equation modelling

  1. 1. Writing assignment 2: Effects of antismoking campaigns on non-smokers and smokers 784 words in the results section Mark Boukes 5616298 1st semester 2009/2010 Structural Equation Modelling (SEM) Instructors: dr. F.J. Oort and S. Jak 19 December 2009 Communication Science (Research MSc) Faculty of Social and Behavioural Sciences University of Amsterdam
  2. 2. a) Introduction A research of the influence of antismoking messages was carried out by Paek and Gunther (2007). They used a structural regression model to do this with three latent factors and four observed single indicator factors. Their model was partially latent (Kline, 2005), however a fully latent model would seem to make it clearer to understand what is happening. Therefore their data was used to construct a fully latent structural regression model to answer the following research question: What influence do antismoking messages have on of non- smokers’ and smokers’ behavioural intention to smoke, their attitude with regard to smoking and the perceived influence on others of these messages? b) Method: Data and participants, analysis and procedure Paek and Gunther (2007) gathered data in two groups on 21 variables related to: how many antismoking messages a person did see; how they thought others did see these messages and what the influence was on others; what their attitude was with regard to smoking; how they thought they would behave in certain future situations; and finally the demographic variables gender, race and grade. With a survey among 1687 respondents these answers were collected. They were split in people who did never smoke and people who were steady smokers or at least tried smoking already. For both groups a correlation matrix (Paek & Gunther, 2007, pp. 427-428) was presented, and a table showed the standard deviations of the observed variables of both groups, except for the demographic variables (p. 417). It should be noticed that the table with standard deviations referred to a somewhat larger number of respondents (N), than the correlation matrices did. However the number of respondents remained rather large (N = 1293), which makes it likely that these values would still be representative for both groups. The correlation matrices taken from Paek and Gunther (2007) can be found in Appendix 1. Structural Equation Modelling (SEM) was used to do the analysis of this research. A structural regression model was drafted based on theory. This model was firstly analysed as a 1
  3. 3. measurement model with Confirmatory Factor Analysis. When it did not fit acceptable, the model was improved by reviewing correlation residuals. Thereupon factor loadings in both groups were constrained to be equal to test for measurement invariance. In this way it could be investigated if the indicators in both groups measured the same constructs (Kline, 2005). Finally the measurement model was altered into the theorized structural regression model. The statistical analysis of this research is done with the computer program Mx 1.7.03, which uses maximum likelihood estimation (Neale, 2003). This could be used, because multivariate normality was assumed to occur (McDonald & Ho, 2002). Parameter values are estimated with it and also the χ2 fit index, with the degrees of freedom (df) and the probability of it (p). CFI was also calculated with Mx. The null model necessary for this consisted of the same factors measured by the same indicators that relate in the same way to each other, but factors did not relate to each other neither by covariances nor by direct effects. CFI values greater than 0.90 were interpreted as indicating good fit (Kline, 2005). A model was interpreted as well fitting when the probability of χ2 was below 0.05. However because there were many variables used and sample size was so high, this index can very hardly be not significant different, and model fit will always look bad. Therefore another computer program, NIESEM, was used to calculate point and interval estimates of the parsimony-adjusted index RMSEA (Dudgeon, 2003). NIESEM was also used to calculate RDR and ECVI-difference to evaluate the difference between two models. The parsimony- adjusted index RMSEA was interpreted in accordance with the criteria of McDonald & Ho (2002). A value of 0.05 and below corresponds to ‘good’ fit, while values up to 0.08 correspond to ‘acceptable’ fit. RDR values lower than 0.05 will be interpreted as indicating ‘essentially equivalent fit’, while values above 0.08 will be interpreted as indicating ‘unequivalent fit’. Besides, it is assumed that when the confidence interval of ECVI- difference does not contain zero, models are not equivalent and vice versa. When zero is 2
  4. 4. included, ECVI difference is not significant, and therefore it is assumed that models do not differ significantly. c) Results The theorized model had four latent factors, with four or five indicators. Between the latent factors were five direct effects, which resulted in one exogenous factor and three endogenous factors that together formed the recursive structural part of the model. To see if it was identified first a confirmatory factor analysis was done, with covariances among all the factors, instead of the five direct effects. Factors in all models were scaled by imposing unit loading identification (ULI) constraints. Particular factor loadings to constrain were chosen by exploring which indicators assessed the factor equally well in both groups. The measurement model did fit the data poorly: χ2 = 1396.943 (df = 258, p < 0.001), RMSEA = 0.0827 (90% CI = [0.0785, 0870]), CFI = 0.5229. When the correlation residuals of this model were reviewed, it seemed that there needed to be a covariance between the measurement error of two indicators of ‘Perceived exposure and effects on others’: ‘the effects on close friends’ and ‘the effects on peers’. When those were included in the measurement 2 model, the model fit improved significantly and became acceptable: χ = 777.689 (df = 256, p < 0.001), RMSEA = 0.0562 (90% CI = [0.0517, 0607]), CFI = 0.7066. Hereafter factor loadings were constrained to be equal in both groups to test for measurement invariance. When these equality constraints were imposed on the factor loadings of ‘Media exposure to the antismoking campaign’, ‘Perceived exposure and effects on others’ and ‘Attitude with regard to smoking’ model fit did not change significantly. Only when factor loadings of ‘Behavioural intention to smoke’ were constrained to be equal in both groups, model fit did decrease significantly. Therefore it could be concluded that in both groups indicators measured the same constructs, except for those measuring behavioural intention. This was logical, because for people who did never smoke, this behaviour will be 3
  5. 5. seen differently than for those who did. The model with equal factor loadings for the three 2 factors fitted the data acceptably: χ = 809.000 (df = 267, p < 0.001), RMSEA = 0.0561 (90% CI = [0.0517, 0605]), CFI = 0.6952. Overall model fit did not change significantly: RDR = 0.0538 (90% CI = [0.0320, 0.0760]) and ECVI Δ = 0.0056 (90% CI = [- 0.0046, 0.0224]). Finally the measurement model with equality constraints was altered into the theorized structural regression model with the added covariance (see Figure 1). As a consequence 2 model fit did decrease just very slightly and remained acceptable: χ = 809.106 (df = 269, p < 0.001), RMSEA = 0.0558 (90% CI = [0.0514, 0.0602]), CFI = 0.6963. Figure 1: Structural regression model used for the analysis of parameter estimates. Note that factor loadings of all factors except behavioural intention were constrained to be equal and that ULI constraints were used. 4
  6. 6. The proportion of explained variance of behavioural intention in this model seemed to be higher in the group of smokers than in the group of non-smokers (see Table 1); respectively 43.9% and 19.2% were explained. The proportion of explained variance for the factor ‘Attitude with regard to smoking’ seemed to be very low, on the other hand seemed ‘Perceived exposure of and effects on others’ to be explained rather well. Table 1: Factor variances and covariances and the proportion of explained variance Variances Standardized R2 Non-smokers group Media exposure to campaign 0.7374 1.0000 0 Perceived exposure of and effects on others 0.0080 0.2551 0.7449 Attitude with regard to smoking 0.8194 0.9906 0.0094 Behavioural intention to smoke 0.1017 0.8076 0.1924 Smokers group Media exposure to campaign 0.6770 1.0000 0 Perceived exposure of and effects on others 0.0047 0.2067 0.7933 Attitude with regard to smoking 1.3956 0.9974 0.0026 Behavioural intention to smoke 0.3782 0.5614 0.4386 The standardized estimates of direct effects in the structural part of the model are given in Table 2. It is remarkable that it seems that media exposure to antismoking messages, has a positive influence on the intention people have to smoke. This confirms the results of Paek and Gunther (2007). In the both groups the total effect of media exposure to antismoking messages on behavioural intention is significantly positive, but small (β = 0.1654, p < 0.05, respectively β = 0.1159, p < 0.05). The interpretation of those is that when people increase with one standard deviation on their media exposure to antismoking messages, their behavioural intention to smoke will increase with this value times the standard deviation. Media exposure only seems to have a moderate negative effect on this intention via the indirect effect of ‘Perceived exposure of and effects on others’ in the group of non-smokers (β = - 0.1712, p < 0.05). Logically the attitude people have with regard to smoking seems to have a large effect on the intention people have to smoke (β = 0.3955, p < 0.05, respectively β = 0.6486, p < 0.05). 5
  7. 7. However the effect of media exposure to antismoking messages on attitude seems to be little or not significant and even positive (β = 0.0969, p < 0.05, respectively β = 0.0505, n.s.). If this effect was positive it means that people who are more exposed to antismoking messages, will have a more positive attitude with regard to smoking; an opposite effect as intended. Noticed should also be that media exposure has a large positive effect on how people perceive how often others are exposed to and effected by the campaign (β = 0.8631, p < 0.05, respectively β = 0.8907, p < 0.05). While they are self not influenced much by the campaign, they expect others nevertheless to be influenced. Table 2: Direct, indirect and total effects of the structural part Type Effect Standardized (β) Non-smokers group Direct effect Media exposure to campaign → Perceived exposure of and effects on others 0.8631* Direct effect Media exposure to campaign → Attitude with regard to smoking 0.0969* Direct effect Media exposure to campaign → Behavioural intention to smoke 0.2982* Indirect effect Media exposure...→ Perceived … others→ Behavioural intention … -0.1712* Indirect effect Media exposure...→ Attitude … → Behavioural intention … 0.0383* Total effect Media exposure to campaign → Behavioural intention to smoke 0.1654* Direct effect Perceived exposure of and effects on others → Behavioural intention to smoke -0.1983* Direct effect Attitude with regard to smoking → Behavioural intention to smoke 0.3955* Smokers group Direct effect Media exposure to campaign → Perceived exposure of and effects on others 0.8907* Direct effect Media exposure to campaign → Attitude with regard to smoking 0.0505 Direct effect Media exposure to campaign → Behavioural intention to smoke 0.2294 Indirect effect Media exposure...→ Perceived … others→ Behavioural intention … -0.1462 Indirect effect Media exposure...→ Attitude … → Behavioural intention … 0.0327 Total effect Media exposure to campaign → Behavioural intention to smoke 0.1159* Direct effect Perceived exposure of and effects on others → Behavioural intention to smoke -0.1642 Direct effect Attitude with regard to smoking → Behavioural intention to smoke 0.6486* * p < .05 To explore if the effects are equal or different in both groups, direct effects were constraint to be equal between the two groups one by one. It turned out that only the effect of ‘Attitude with regard to smoking’ on ‘Behavioural intention to smoke’ differed significantly across 6
  8. 8. groups. Constraining this effect to be equal across groups resulted in a significant decrease in model fit. 7
  9. 9. References Dudgeon, P. (2003). NIESEM: A computer program for calculating noncentral interval estimates (and power analysis) for structural equation modeling. Melbourne: University of Melbourne, Department of Psychology. Kline, R. B (2005). Principles and practices of structural equation modeling (2nd ed.). New York: The Guilford Press. McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analysis. Psychology Methods, 7(1), 64-82. Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical Modeling (6th ed.). Downloaded, 22 October, 2009, from http://www.vipbg.vcu.edu/~vipbg/software /mxmanual.pdf Paek, H., & Gunther, A. C. (2007). Smoking how peer proximity moderates indirect media influence on adolescent smoking. Communication Research, 34(4), 407-432. 8
  10. 10. Appendix 1: Correlation matrices and standard deviations Non-smokers (N=902) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 1.00 2 .58 1.00 3 .39 .52 1.00 4 .47 .48 .50 1.00 5 .51 .51 .42 .54 1.00 6 .58 .43 .36 .47 .52 1.00 7 .65 .55 .42 .51 .56 .70 1.00 8 .10 .00 .00 .03 .09 .14 .12 1.00 9 .12 -.01 -.05 .01 .09 .14 .12 .65 1.00 10 .08 .08 .05 .06 .07 .07 .06 -.11 -.14 1.00 11 .08 .07 .04 .04 .09 .09 .06 -.13 -.14 .75 1.00 12 .06 .06 .04 .02 .07 .06 .05 -.10 -.11 .76 .78 1.00 13 .06 .05 .03 .05 .08 .06 .06 -.12 -.14 .72 .76 .78 1.00 14 .06 .08 .03 .07 .09 .07 .07 -.16 -.18 .67 .75 .72 .83 1.00 15 .03 .09 .05 .08 .11 .08 .04 -.14 -.14 .28 .28 .26 .28 .30 1.00 16 .06 .11 .15 .09 .11 .05 .08 -.16 -.20 .25 .25 .25 .26 .28 .56 1.00 17 .05 .10 .09 .08 .11 .06 .06 -.14 -.16 .28 .32 .28 .28 .33 .63 .59 1.00 18 .07 .13 .09 .11 .12 .06 .08 -.17 -.18 .26 .28 .24 .27 .27 .55 .65 .58 1.00 SD 1.29 1.22 1.17 1.26 1.22 1.56 1.13 1.26 1.37 .93 .98 1.00 .96 1.07 .70 .48 .56 .46 Note: Variable names: 1. Antismoking messages on TV; 2. Antismoking messages on the radio; 3. Antismoking messages on the Internet; 4. Anti-smoking messages in magazines; 5. Anti−smoking messages on TV; 6. Perceived exposure of other peers to antismoking messages; 7. Perceived exposure of close friends to antismoking messages; 8. Perceived effects of antismoking messages on other peers; 9. Perceived effects of antismoking messages on close friends; 10. How do you feel about smoking (grown-up); 11. How do you feel about smoking (good-looking); 12. How do you feel about smoking (exciting); 13. How do you feel about smoking (cool); 14. How do you feel about smoking (has friends); 15. Behavioral intention (experiment with cigarettes in future?); 16. Behavioral intention (smoke a cigarette at anytime during the next year); 17. Behavioral intention (Will you be smoking cigarettes 5 years from now); 18. Behavioral intention (If your best friend offered you a cigarette, would you smoke it). 9
  11. 11. Smokers (N=391) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 1.00 2 .56 1.00 3 .37 .45 1.00 4 .45 .45 .51 1.00 5 .34 .40 .40 .51 1.00 6 .51 .43 .36 .50 .48 1.00 7 .50 .51 .41 .53 .49 .64 1.00 8 .11 .02 .12 .08 .04 .12 .13 1.00 9 .09 .06 .09 .11 .09 .17 .16 .56 1.00 10 .04 .07 -.01 .05 .03 .04 .02 -.14 -.30 1.00 11 .02 .08 .00 .03 .03 .06 .01 -.05 -.24 .71 1.00 12 .04 .08 .00 .06 .04 .06 .04 -.13 -.33 .73 .79 1.00 13 .02 .05 .01 .01 .02 .06 .03 -.13 -.32 .72 .75 .83 1.00 14 .00 .01 -.02 -.01 .02 .03 .02 -.06 -.26 .67 .74 .75 .81 1.00 15 .06 .09 .05 .06 .01 .07 -.01 -.07 -.30 .46 .46 .46 .47 .42 1.00 16 .10 .14 .02 .11 .05 .11 .07 -.12 -.36 .48 .45 .50 .50 .47 .71 1.00 17 .10 .08 .04 .06 .01 .07 .01 -.13 -.30 .47 .43 .49 .46 .44 .67 .68 1.00 18 .03 .07 .00 .07 .02 .05 .03 -.16 -.39 .53 .46 .51 .51 .47 .65 .73 .66 1.00 SD 1.22 1.27 1.25 1.31 1.22 1.20 1.15 1.25 1.50 1.22 1.23 1.28 1.32 1.33 .97 1.03 .88 .99 Note: Variable names: 1. Antismoking messages on TV; 2. Antismoking messages on the radio; 3. Antismoking messages on the Internet; 4. Anti-smoking messages in magazines; 5. Anti−smoking messages on TV; 6. Perceived exposure of other peers to antismoking messages; 7. Perceived exposure of close friends to antismoking messages; 8. Perceived effects of antismoking messages on other peers; 9. Perceived effects of antismoking messages on close friends; 10. How do you feel about smoking (grown-up); 11. How do you feel about smoking (good-looking); 12. How do you feel about smoking (exciting); 13. How do you feel about smoking (cool); 14. How do you feel about smoking (has friends); 15. Behavioral intention (experiment with cigarettes in future?); 16. Behavioral intention (smoke a cigarette at anytime during the next year); 17. Behavioral intention (Will you be smoking cigarettes 5 years from now); 18. Behavioral intention (If your best friend offered you a cigarette, would you smoke it). 10

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