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5.2 Validity of Hypotheses

Tests of the hypotheses were explored in this section. The main hypotheses will be explored, followed by the additional hypotheses. As indicated, two instruments (eye tracker and self-report) were used to validate the hypotheses. The eye tracker was first used to assess the validity of the hypotheses. The self-report was then applied to test the hypotheses. This section will first report the eye tracker results followed by the results from the self-report, for each individual hypothesis. For the eye tracker, the participant indicated a selection by looking at a particular asset or portfolio for a proportionately longer time. For the self-report, the asset and port- folio selection is indicated by the participant on the paper provided. SPSS was used to conduct the tests of association and the logistic regression. Tests of association were done via the Pearson chi-squared test and Fisher's exact test. For the chi-squared test, asymptotic significance is used (Mehta et al. 1984). For samples that are too small for chi-squared analysis, Fisher's exact test is utilised (Upton 1992). A significance level of p = 0.05 (one sided) is assumed for the chi-squared and Fisher's exact test. Given the small number of participants, the statistical power of the analysis is limited and a single tailed significance level of p = 0.10 (one sided) is assumed for the logit model (Egger et al. 1997; Sterne et al. 2000). As indicated in Sect. 5.1, there was an effective sample of 93 participants. For the logistic regression, the variables were coded as per Table 5.3. Table 5.3 Logit model variable coding

Variable

0

1

Age

<30

>30

Education

Basic degree

Higher degree

Ethnicity

Asian

Non-Asian

Gender

M

F

Marital status

Single

Married

Eye tracker—asset selection

Promotion

Prevention

Eye tracker—portfolio selection

Promotion

Prevention

Regulatory focus

Promotion

Prevention

Self-report—asset selection

Promotion

Prevention

Self-report—portfolio selection

Promotion

Prevention

Financial literacy

Y

N

5.2.1 Validity of H1: Eye Tracker

H1 tests the association between chronic regulatory focus and asset allocation. To test this association, an eye tracker was used, followed by a self-report. This section determines the relationship between chronic regulatory focus and asset allocation, with the eye tracker as a measure. The chi-squared test was first conducted to test this association. Table 5.4 indicates the chi-squared result for the association between regulatory focus and asset allocation for the eye tracker. There is a main effect of eye tracker—asset on regulatory focus, (n = 93, χ2 = 3.442, p < 0.05). However, as in Table 5.4, participants do not choose the assets that are associated with their regulatory foci. H1 is unsupported by the chi-squared test, with the eye tracker as the measure.

To gain further insight into the relationship between chronic regulatory focus and the asset selections on the eye tracker, a logistic regression was conducted. The logistic regression controlled for the effect of other variables is to indicate the strength and direction of the relationship between chronic regulatory focus and asset selections on the eye tracker.

Table 5.4 Basic Pearson chi-squared test and cross tabulation for regulatory focus * eye tracker— asset allocation

Value

df

Asymp. sig. (1-sided)

Pearson chi-square

3.442

1

0.032

Eye tracker—asset

Total

Promotion

Prevention

Regulatory focus

Promotion

9

41

50

Prevention

15

28

43

Total

24

69

93


Table 5.5 Logit model for eye tracker—asset selection against age, education, ethnicity, marital status, regulatory focus, financial literacy, gender

Dependent variable

−2 Log likelihood

Cox and Snell R square

Nagelkerke R square

Eye tracker—asset selection

96.998

0.114

0.166

Variable

β

S.E.

Sig.

Age

−0.297

0.918

0.373

Education

−0.660

1.003

0.256

Ethnicity

0.937

1.202

0.218

Gender

1.027

0.542

0.029

Marital status

0.812

0.764

0.144

Regulatory focus

−1.166

0.532

0.014

Financial literacy

−0.899

0.539

0.048

Constant

1.491

0.58

0.005

Eye tracker—asset selection was the dependent variable, and age, education, ethnicity, marital status, regulatory focus, financial literacy and gender are the independent variables for the logit model performed and this is indicated in Table 5.5.

Regulatory focus (β = −1.166, p < 0.10), financial literacy (β = −0.899, p < 0.10)

and gender (β = 1.027, p < 0.10) are significant. Although regulatory focus is significant, the results indicate that prevention-focused participants are less likely to look at the prevention assets, and promotion-focused participants are less likely to look at promotion assets (see Sect. 6.1.2). This is in opposition to H1. Thus, using the eye tracker as a measure, H1 is unsupported by the logit model.

Participants with a low financial literacy score are less likely to look at the

prevention asset for a proportionately longer time, than compared to those with a high financial literacy score. Female participants are more likely to look at the prevention asset for a proportionately longer time, compared to male participants. Although H1 is unsupported by the eye tracker measure, significant results are obtained for financial literacy and gender. Further analysis will be conducted in later sections, to gain more insight. The next section will determine the validity of H1 with the self-report as the measure.

 
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