Decision-Making and Impulsivity: An SPSS-Based Research Report

This research report investigates the impact of functional and dysfunctional impulsivity on decision-making strength. An experiment was conducted where participants opened boxes and made color judgments. The findings reveal a significant association between the number of boxes opened and correct judgments. Functional impulsivity showed no significant impact on decision correctness under both fixed win (FW) and decreasing win (DW) conditions. Dysfunctional impulsivity significantly impacted decision correctness in FW but not in DW. Statistical analyses, including ANOVA and regression, were performed using SPSS to analyze the data, providing insights into the relationships between impulsivity, decision-making, and performance under different conditions. The report includes descriptive statistics, reliability analyses, and correlation matrices to support the conclusions. The discussion section compares the findings with existing research, highlighting the distinct roles of functional and dysfunctional impulsivity and their implications for cognitive processes.

SPSS

ABSTRACT
The research study aims at investigating the impact of functional and dysfunctional impulsivity on the
overall decision-making strength of the people. For this, an experiment had been carried out in which, participants
had to open a box and decides which colour has majority. As per the research findings, it is found that number of
boxes opened has a significant association with the number of correct judgements being made. Moreover, the
research results do not reveal strong relationship between functional and dysfunctional impulsivity. There is no
significant impact discovered of functional impulsivity on the correctness of the judgements in the ICT performed
under both FW and DW conditions. However, in case of dysfunctional impulsivity, it showed significant impact on
decision correctness in FW whereas it is not so with DW condition.

TABLE OF CONTENTS
ABSTRACT.....................................................................................................................................2
RESULTS........................................................................................................................................1
DISCUSSION..................................................................................................................................4
REFERENCES................................................................................................................................5
APPENDIX......................................................................................................................................6
TABLE OF FIGURES
Table 1 Group characteristics..........................................................................................................6
Table 2 Frequency table for age group............................................................................................6
Table 3 Frequency table for gender.................................................................................................6
Table 4 Number of items, range, average, standard deviation, internal consistency coefficient and subscale
intercorrelation.................................................................................................................................7
Table 5 Reliability Statistics............................................................................................................7
Table 6 Inter-Item Correlation Matrix.............................................................................................7
Table 7 Mean and standard deviation measures of FI and DI new.................................................8
Table 8 Reliability Statistics............................................................................................................8
Table 9 Inter-Item Correlation Matrix.............................................................................................8
Table 10 Mean and standard deviation measures of fixed win and decreasing wins......................8
Table 11 One-Way Anova...............................................................................................................9
Table 12 Model Summary...............................................................................................................9
Table 13 ANOVA............................................................................................................................9
Table 14 Regression coefficient......................................................................................................9
Table 15 Model Summary...............................................................................................................9
Table 16 ANOVA results..............................................................................................................10
Table 17 Coefficients.....................................................................................................................10
able 18 Model Summary................................................................................................................10
Table 19 ANOVA results..............................................................................................................10
Table 20 Coefficient......................................................................................................................10
Table 21 Model Summary.............................................................................................................11
Table 22 ANOVA results..............................................................................................................11
Table 23 Coefficients.....................................................................................................................11
Table 24 Correlation under FW condition.....................................................................................11
Table 25Correlation under DW condition.....................................................................................12

RESULTS
Descriptive statistics
Descriptive statistics is used to know basic or general characteristics of the series including central
tendency and dispersion measures.
Appendix 1
The findings shows that age group series reported (M = 0.40, SD = 0.491) and Gender shows (M = 0.24,
SD = 0.429) respectively.
Appendix 2
The results indicate that out of total 170 participants, most of them belongs to low age group (F = 102,
60%) however rest (F = 68, 40%) belongs to high age group.
Appendix 3
The results represents that out of 170 participants, maximum of them are of female category (F=129,
75.9%) whereas only (F = 41, 24.1%) are male candidates.
Cronbach alpha
Cronbach alpha measures internal consistency among two series and shows that how closely these are
related to each other.
Appendix 4
From the result, it is visualized that both the functional and dysfunctional impulsivity has a (M = 1.00, SD
= 0.843) and (M = 1.00, SD = 0.836) respectively. The reliability statistics results stated that Cronbach alpha for
these scale is found (a = -0.479) indicates negative covariance and also do not found the internal consistency good.
However, correlation between both these impulsivity is found negative (r = -.193). It shows that both the scale
seems independent because their relationship does not show strong relationship as the value of correlation
coefficient falls below 0.25.
Appendix 5
The results represent that FI and DI new reported (M = 29.63, SD = 5.69) and (M = 28.61, SD = 4.52)
respectively. Cronbach alpha found to -0521 that interpret that internal consistency does not seem effective.

However, correlation between both the sub-scale measures is determined to -0.212<+-0.25 demonstrates that both
the sub-scales shows less likely association.
Appendix 6
As per the findings, it is identified that number of boxes opened under fixed wins (FW) condition reported
(M = 119.29, SD – 67.427) greater than decreasing wins (DW) condition with (M = 69.55, SD = 37.263). Findings
the correct judgements under both the condition, FW reported greater value for the correct selection with (M =
8.27, SD = 1.583) while in DW condition, it is reported (M = 7.40, SD = 1.637) respectively (Kpolovie, 2017). The
total point value indicates (M = 1797.12, SD = 566.503) shows that participant score greatly vary from the average
points score.
H0: There is no significant mean difference in the total points between male and female.
H1: There is significant mean difference in the total points between male and female
Test: One-Way ANOVA
Appendix 7
As per the findings of One-Way Anova, no significant difference had been discovered in the total points
obtained by male and female participants. It is because, looking to the Anova Statistics, [F(1,168) = 1.398, P =
0.239>0.005], therefore, null hypothesis had proven valid.
H0: There is no significant impact of functional impulsivity on the number of correct judgements in FW condition.
H1: There is significant impact of functional impulsivity on the number of correct judgements in FW condition.
Test: Regression
Appendix 8
Interpretation: As per the results, it is seen that [F(1,168) = 1.333, p = 0.250>0.005], therefore, null
hypothesis is true and there is no significant impact of functional impulsivity on the correct decision making ability
of the participants. Individual with lower, moderate or higher functional impulsivity, when it is helpful does not
have a significant impact on their quick decision making strength (Cronk, 2017).
H0: There is no significant impact of functional impulsivity on the number of correct judgements in DW condition.
H1: There is significant impact of functional impulsivity on the number of correct judgements in DW condition.

Test; Regression
Appendix 9
Finding out the results, it is visualized that [F(1,168) = 1.955, p = 0.164>0.05], therefore it can be said that
tendency to make quick decisions in case of non optimal situation do not have a significant impact on the correct
judgements making ability of the people.
H0: There is no significant impact of dysfunctional impulsivity on the number of correct judgements under FW
conditions.
H1: There is significant impact of dysfunctional impulsivity on the number of correct judgements under FW
Condition
Test: Regression
Appendix 10
Interpretation: Looking to the findings, it is determined that ANOVA result presented [F(1,168) = 7.427, p
= 0.007<0.05], therefore, it is clear that dysfunctional impulsivity have a significant impact over the correct
judgements in FW condition (Hayes and Montoya, 2017).
H0: There is no significant impact of dysfunctional impulsivity on the number of correct judgements under DW
conditions.
H1: There is significant impact of dysfunctional impulsivity on the number of correct judgements under DW
Condition
Test: Regression
Appendix 11
As per the results founded, it is determined that [F(1,168) = 1.680, p = 0.197>0.05], therefore, null
hypothesis found correct and it is easy to say that dysfunctional impulsivity did not have a significant impact on the
correct decisions making strength under DW condition.
H0: there is no significant impact of the boxes opened on the correct judgements made during FW and DW
conditions.

H1: there is no significant impact of the boxes opened on the correct judgements made during FW and DW
conditions.
Test: Correlation
Appendix 12
In FW condition, r = 0.623, p = 0.00<0.05, similarly, under DW condition, pick up found a correlation (r) =
0.467, p = 0.000<0.05 showed significant relationship (Shevlyakov and Smirnov, 2016).
DISCUSSION
From the findings of the study, it is determined that although the average number of correct judgements
found greater under the fixed win conditions than decreasing win conditions. Functional impulsivity is related to
the adventurousness, extraversion, activity, enthusiasm and narcissism. It is often believed that individual with
higher functional impulsivity have greater executive functioning. Moreover, high level of trait is associated with
higher success in the game and exhibits lesser probability of experiencing negative consequences.
As per the research results, functional and dysfunctional impulsivity did not showed any strong connection.
In the research findings of Dickman (1990), it presented a clear distinguish between functional and dysfunctional
impulsivity, Former is defined as a tendency to act with little forethought when it is optimal to do so, dysfunctional
impulsivity, in contrast, had been defined as a tendency to act with the less forethought than other people with
equal ability. The findings of the study reveal no significant level of correlation between both the tendencies as
both of them bear distinctive relationship to the personality traits.
Number of boxes opened has a significant association with the number of correct judgements being made.
Likewise, Clark and et.al. (2006), supported it as it presented that number of boxes which were opened by the
participants during the IST showed significant correlation with the incorrect judgements under both the FW and
DW Condition. It presents that response accuracy is a function of the extent to which information had been
analyzed by the participants and also a principal feature or characteristics of the reflection impulsivity.
Besides this, according to the research results, there is no significant impact discovered of functional
impulsivity on the correctness of the judgements in the ICT performed under both FW and DW conditions.

However, in case of dysfunctional impulsivity, it showed significant impact on decision correctness in FW whereas
it is not so with DW condition.

REFERENCES
Books and Journals
Clark, L. and et.al., 2006. Reflection impulsivity in current and former substance users. Biological psychiatry.
60(5). pp.515-522.
Cronk, B.C., 2017. How to use SPSS®: A step-by-step guide to analysis and interpretation. Routledge.
Dickman, S.J., 1990. Functional and dysfunctional impulsivity: personality and cognitive correlates. Journal of
personality and social psychology. 58(1). p.95.
Hayes, A.F. and Montoya, A.K., 2017. A tutorial on testing, visualizing, and probing an interaction involving a
multicategorical variable in linear regression analysis. Communication Methods and Measures. 11(1). pp.1-
30.
Kpolovie, P.J., 2017. Statistical analysis with SPSS for research.
Shevlyakov, G. and Smirnov, P., 2016. Robust estimation of the correlation coefficient: An attempt of
survey. Austrian Journal of Statistics. 40(1&2). pp.147-156.

APPENDIX
Appendix 1
Table 1 Group characteristics
N Minimum Maximum Mean Std. Deviation
age_gp 170 0 1 .40 .491
gender 170 0 1 .24 .429
Valid N (listwise) 170
Appendix 2
Table 2 Frequency table for age group
Frequency Percent Valid Percent Cumulative Percent
Valid
Low 102 60.0 60.0 60.0
High 68 40.0 40.0 100.0
Total 170 100.0 100.0
Appendix 3

Table 3 Frequency table for gender
Frequency Percent Valid Percent Cumulative Percent
Valid
Female 129 75.9 75.9 75.9
Male 41 24.1 24.1 100.0
Total 170 100.0 100.0
Appendix 4
Table 4 Number of items, range, average, standard deviation, internal consistency coefficient and subscale
intercorrelation
Subscale N Range M Standard
deviation
Dys_Imp 170 0-3 1.00 0.836
Funct_Imp 170 0-3 1.00 0.843

Table 5 Reliability Statistics
Cronbach's Alphaa Cronbach's Alpha Based on
Standardized Itemsa
N of Items
-.479 -.479 2
Table 6 Inter-Item Correlation Matrix
Dys_Imp Funct_Imp
Dys_Imp 1.000 -.193
Funct_Imp -.193 1.000
Appendix 5
Table 7 Mean and standard deviation measures of FI and DI new
N Mean Std. Deviation
FI_new 170 29.6353 5.69407
DI_new 170 28.6118 4.52630
Table 8 Reliability Statistics
Cronbach's Alphaa Cronbach's Alpha Based on
Standardized Itemsa
N of Items
-.521 -.539 2
Table 9 Inter-Item Correlation Matrix
FI_new DI_new
FI_new 1.000 -.212
DI_new -.212 1.000
Appendix 6