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5.2.2 Validity of H1: Self-report

A self-report was used after the eye tracker to determine the validity of H1. This section regards the results obtained using the self-report. The chi-squared test was first conducted to determine association between regulatory focus and asset allo- cation on the self-report. The cross tabulation is denoted in Table 5.6. Significant results were not reported (n = 93, χ2 = 0.43, p > 0.05) for the chi-squared test, and Table 5.6 Cross tabulation for regulatory focus * self-report—asset allocation

Self-report—asset

Total

Promotion

Prevention

Regulatory focus

Promotion

22

28

50

Prevention

18

25

43

Total

40

53

93

for the association between regulatory focus and self-report—asset allocation. H1 is unsupported by the chi-squared analysis, and for the self-report.

Although the chi-squared results are insignificant, further insight may be gained

by controlling for other variables, and a logit regression was thus conducted. In Table 5.7, self-report—asset selection is the dependent variable, and age, education, ethnicity, marital status, regulatory focus, financial literacy and gender are the independent variables. Table 5.7 indicates no significant results for the logit model. H1 is not supported by the logistic regression, with the self-report as the measure.

Overall, H1 is unsupported, regardless of the measure used. Participants do not appear to choose assets that are associated with their chronic regulatory foci. It is postulated that the negative world financial outlook may be influencing the majority of participants to select the prevention asset on both measures. This effect will be explored in later sections. The following section will determine the validity of H2, with the eye tracker and self-report as the measure.

5.2.3 Validity of H2: Eye Tracker

H2 tests the association between chronic regulatory focus and portfolio allocations,

first with the eye tracker, followed by the self-report. This section determines the

Table 5.7 Logit model for self-report—asset selection against age, education, ethnicity, gender, marital status, regulatory focus, financial literacy

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Self-report—asset selection

120.380

0.042

0.056

Variable

β

S.E.

Sig.

Age

−0.344

0.793

0.488

Education

0.852

0.845

0.157

Ethnicity

−0.093

0.998

0.463

Gender

0.298

0.464

0.260

Marital status

0.171

0.675

0.400

Regulatory focus

0.116

0.450

0.399

Financial literacy

0.633

0.494

0.100

Constant

−0.373

0.488

0.222


Table 5.8 Cross tabulation for regulatory focus * eye tracker—portfolio allocation

Eye tracker—portfolio

Total

Promotion

Prevention

Regulatory focus

Promotion

18

32

50

Prevention

14

29

43

Total

32

61

93

association between chronic regulatory focus and portfolio allocation, with the eye tracker as a measure. A chi-squared test was first conducted to test this association. Table 5.8 indicates the cross tabulation between regulatory focus and eye tracker— portfolio. No significant results were reported (n = 93, χ2 = 0.121, p > 0.05). The chi-squared analysis does not support H2, with the eye tracker as the measure.

Although the chi-squared results are insignificant, greater insight may be gained by controlling other variables, and thus a logit regression was conducted. Table 5.9 indicates that eye tracker—portfolio selection is the dependent variable, and age, education, ethnicity, marital status, regulatory focus, financial literacy and gender are the independent variables in the regression. Table 5.9 states that there are no significant results for regulatory focus in the logit model. H2 is thus unsupported by the logistic regression, with the eye tracker as the measure.

Although Table 5.9 states that H2 is unsupported by the eye tracker, financial

literacy is significant (β = −1.014, p < 0.10). This means that those who have a low financial literacy score are less likely to look at the prevention portfolio for a proportionately longer time. The next section will determine the validity of H2 with the self-report as the measure.

Table 5.9 Logit model for eye tracker—portfolio selection against age, education, ethnicity, gender, marital status, regulatory focus, financial literacy

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Eye tracker—portfolio selection

110.899

0.077

0.108

Variable

β

S.E.

Sig.

Age

−0.953

0.900

0.145

Education

0.549

0.952

0.282

Ethnicity

−0.742

0.960

0.220

Gender

0.601

0.489

0.110

Marital status

0.758

0.737

0.152

Regulatory focus

0.128

0.472

0.394

Financial literacy

−1.014

0.500

0.021

Constant

0.627

0.515

0.112


 
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