Logistic regression analysis to know the factors affecting the financial knowledge in decision of investment non riil assets at university investment gallery

International Journal of Management (IJM)
Volume 11, Issue 2, February 2020, pp. 147–162, Article ID: IJM_11_02_016
Available online at />Journal Impact Factor (2020): 10.1471 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6502 and ISSN Online: 0976-6510
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LOGISTIC REGRESSION ANALYSIS TO KNOW
THE FACTORS AFFECTING THE FINANCIAL
KNOWLEDGE IN DECISION OF INVESTMENT
NON RIIL ASSETS AT UNIVERSITY
INVESTMENT GALLERY
Isfenti Sadalia
Dr., Faculty of Economics and Business, Universitas Sumatera Utara, Indonesia
Fahmi Natigor Nasution
Dr., Faculty of Economics and Business, Universitas Sumatera Utara, Indonesia
Iskandar Muda
Dr., Faculty of Economics and Business, Universitas Sumatera Utara, Indonesia
ABSTRACT
The large population of Indonesia is not an indication of the increasing number of
investors in Indonesia, especially investors in non-real assets. This study aims to
investigate the variable causes of non-real asset investment decisions. The population
used in this research is university students who have an investment gallery and
registered as an investor at university investment gallery in Aceh, North Sumatera,
West Sumatera and Pekan Baru, Riau using Lemeshow formulation for the sample
size. Dependent variable in this research is non asset investment decision, while
independent variable are gender, education level, and availability of financial
advisory. This research uses binary logistic regression approach. The samples used in
this study were 384 samples selected by the Lemeshow formulation method. The test
results with binary logistic regression on the designed model, ie there is one predictor

variable that significantly affects the non-real asset investment decision is the variable
availability of financial advisors.
Keywords: Financial Knowledge, Investment Knowledge, Educational Level, Nonreal asset investment decision, binary logistic regression.
Cite this Article: Isfenti Sadalia, Fahmi Natigor Nasution, Iskandar Muda, Logistic
Regression Analysis to Know the Factors Affecting the Financial Knowledge in
Decision of Investment Non Riil Assets at University Investment Gallery,
International Journal of Management (IJM), 11 (2), 2020, pp. 147–162.
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147




Logistic Regression Analysis to Know the Factors Affecting the Financial Knowledge in Decision of
Investment Non Riil Assets at University Investment Gallery

1. INTRODUCTION
The large population of Indonesia is not an indication of the increasing number of investors in
Indonesia, especially investors in non-real assets. Investment is sacrificing something in the
present to get something in the future with hope of course better (Abdul, 2005). Investment is
essentially a placement of funds in the hope of making a profit in the future. Investment plays
an important role in driving economic growth and employment in Indonesia. On capital
market has a strategic position in national economic development. Growth in the non-real
sector plays an important role in the process of economic growth. Therefore, the growth of the
non-real sector requires investment to maintain the sustainability of economic growth itself.
The purpose of this study is aimed to investigate the variable causes of investment decisions
on non-real assets seen from several variables namely, gender, education level, and
availability of financial advisors. Through this research is expected to yield the best
investment decisions.

Financial knowledge is now an integrated part of financial literacy (Forster et al., 2019
and Saurabh and Nandan, 2019). Current financial knowledge needs to be known by students
since my lecture. This is because financial knowledge will have a personal impact on students
in the form of individuals' ability to utilize financial knowledge to make decisions. Financial
knowledge occurs when individuals have a set of skills and abilities that make these
individuals able to utilize existing resources to achieve expected goals. Financial knowledge
involves not only knowledge and ability to deal with financial problems but also other
attributes. Financial knowledge will improve financial skills and mastery of financial tools.
Financial skills as a technique for making decisions in financial management behavior, such
as preparing a budget, choosing investment, choosing an insurance plan, and using credit are
examples of financial skills in students so that the entrepreneurial spirit of students is getting
stronger.
College graduates are also expected to be experts or professionals to plan finances with
markets in Indonesia that are still extensive as population growth develops. Financial
education programs must be carried out to the community and students. This program is in
line to educate the public about the benefits of financial planning. This activity also helps
students understand and begin planning and managing finances. Financial Management is all
activities or company activities related to how to obtain, use and manage company finances.
Financial management is a management activity that aims to manage funds and assets owned
by the company to be used on things or activities that help achieve the company's main goal,
namely profit. In a company or business, financial management has 3 main activities carried
out by financial managers, namely the acquisition of funds, activities for using funds and
managing assets. These three things are related to internal and external funding sources that
students need to know. Working capital and share ownership also include tasks in financial
management.
Research by Xiao el.al, (2008); Mandell, (2007) and Klein, (2009), Nababan and Sadalia
(2013) concluded that the best way to improve behavior in adulthood is to teach good
behavior from childhood, including financial behavior. While in Indonesia alone personal
finance education is still rarely found either in elementary schools to universities. Developed
countries such as the United States, Canada, Japan and Australia are incessantly providing

financial education to their communities, especially students with hopes of literacy. Financial
behavior of the community is increasing. Several institutions were formed, as well as various
researches and programs conducted to measure and improve the financial literacy of their
people. Research by Nababan and Sadalia (2013) concluded that the average respondent was
only able to answer half of the 27 questions correctly, which amounted to 56.11%, this means
that the level of personal financial literacy of the student strata one overall was included in the

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low category (<60%) . In addition, other results concluded that the characteristics of
respondents with relatively high financial literacy were male students, economic development
study programs, 2008 stambukes (seniors), GPA ≥3, and self-residence, while the
characteristics of respondents with financial literacy tendencies were relatively low are female
students, management study programs, stambuk 2011 (junior), GPA <3.00, and stay with
parents.

2. THEORETICAL BACKGROUND
2.1. Financial Knowledge
Financial knowledge is everything about finance that is experienced or that occurs in
everyday life (Lusardi, 2019). Financial knowledge as someone's mastery of various things
about the world of finance, which consists of your financial skills financial tools. Financial
knowledge consists of knowledge of financial management, knowledge of financial planning,
knowledge of expenditure and income, knowledge of money and assets, knowledge of interest
rates, knowledge of credit, basic knowledge of accounting standards, insurance, basic

knowledge of investment, deposits, stocks, investments on bonds, and property knowledge.
Financial knowledge is an integrated part of financial literacy, but financial literacy still
includes the ability of individuals to utilize financial knowledge to make decisions. Financial
literacy occurs when individuals have a set of skills and abilities that make the individual able
to utilize existing resources to achieve the expected goals (Momtalto et al. 2019 and
Totenhagen et al., 2019). Financial knowledge involves not only knowledge and ability to
deal with financial problems but also other attributes. Important financial knowledge is owned
by individuals to develop their ability to manage their assets. Financial knowledge does not
only make individuals able to use assets smartly and wisely, but through financial knowledge
will provide added value economically. The higher the level of financial knowledge a person
will be the better the financial behavior he shows. With increasing knowledge, behavior
patterns shown by individuals will also increase.

2.2. Investment Knowledge
Investing is sacrificing something nowadays to get something in the future with hope of
course better (Abdul, 2005). Investment is essentially a placement of funds in the hope of
making a profit in the future. Investment plays an important role in driving economic growth
and employment in Indonesia. The growth of the non-real sector requires investment to
maintain the sustainability of economic growth itself. Irman (2018) explains that the learning
process is very important in the process of knowledge formation in each student. The
understanding associated with having the role necessary to improve the economic
environment is very diverse today. It is hoped that students with better knowledge can have a
more prosperous life in the future.
Margaretha and Pambudhi (2015) uses the level of financial literacy on undergraduate
students from the faculty of economics, shows gender, and parental income significantly
affects students' financial literacy. Kristanto and Andreas (2015) also stated that information
is very important. A good level of financial literacy has an important role to play in investing.
The exposure made in the preceding paragraph indicates that education, gender and the
availability of financial advisors are considered capable of influencing the outcome of
investment decisions to be made by investors, where in this study the investor is a student

who becomes the sample.

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2.3. Educational Level
Lee and Widyaningrum (2019) states the there stages or levels of formal education. Each has
a different education time. The following is the level of education :

2.4. Basic Education
The first formal education is basic education. Primary education is in the form of elementary
schools and Madrasah Ibtidaiyah as well as junior high schools and Madrasah Tsanawiyah
(Demina et al., 2019). Basic education is the beginning of a child's education starting from
training children to reading well, sharpening numeracy and thinking skills. The aim of this
level of basic education is to lay the foundation for knowledge, personality, noble character
and also the skills to live independently and to pursue further education.

2.5. Middle Education
Secondary education is an advanced education from basic education. These forms of
secondary education are high school, Islamic schools, vocational high schools, and vocational
Aliyah madrasas (Eliza et al., 2019). The general purpose of secondary education is to
improve intelligence, knowledge, personality, noble character and skills to live independently
and to attend further education. The general purpose of vocational secondary education is to
improve intelligence, knowledge, personality, noble character, and skills to live independently

and follow further education in accordance with their vocational.

2.6. Higher Education
Higher education is a continuation of secondary education. Higher education includes a
variety of diploma, bachelor, master, doctoral and specialist education programs organized by
universities. This university is obliged to organize education, research, and community
service. At this level of higher education students are required to be more active in practicing
and directly involved in each learning activity because the ultimate goal of this level of
education is that students are expected to become human beings who are beneficial to others
(Heiman and Olenik, 2019). Colleges can hold academic, professional or vocational
programs.

3. STAGES IN THE CONSUMER PURCHASE DECISION PROCESS
Basically, consumer decision making for a product varies depending on the type of
purchasing decision. Complex and expensive purchases may involve more consideration of
buyers and more participants but in certain purchasing processes. Consumers must go through
several stages, known as the "stage model". This model shows that consumers must go
through 5 (five) stages in the process of buying a product. According to Armstrong et al.,
(2014) the stages of the process of consumer purchasing decisions as shown below:
Introduction
to problems

Information
search

Evaluate
alternatives

Buying
decision

Postpurchase
behavior

Sources : Principles of marketing. Armstrong et al., (2014).
Figure 1. Five Stages of Decision Making Process

The more detailed explanation of the image stages of the consumer decision making
process are as follows:

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3.1. Need Recognition
The purchase process begins when the buyer recognizes a problem or decision, the buyer feels
the difference between the actual state and the desired condition, these needs can be caused by
an internal stimulus such as hunger, thirst, sex, reaching a certain point of the occurrence of a
stimulus caused by stimulation externally, for example, someone passes a pastry shop and
sees fresh bread that stimulates their hunger.

4. ALTERNATIVE SEARCH (EVALUATION OF ALTERNATIVES)
Stimulus consumers try to find more information. The amount of search done depends on the
strength of the drive, the amount of additional information and satisfaction in searching for
the information.

4.1. Evaluation of Alternatives
Certain methods view cognitive-oriented processes, namely they assume consumers form
judgments on products primarily based on and ratios. Some basic concepts in understanding
consumer evaluation processes. First, consumers try to make ends meet. Second, consumers
seek certain benefits from product solutions. Third, consumers perceive each product as a set
of attributes with different abilities in providing benefits used to satisfy needs.

4.2. Purchase Behavior
In the evaluation phase, consumers form preferences for brands, brands in a collection of
choices. Consumers may also form an intention to buy the product they like best, but two
factors can be between purchase intentions and purchasing decisions, namely: (Panda et al.,
2019)
 The establishment of other people, the more aggressively the negative attitude of
others, the closer the other person is to the consumer, so the greater the consumer will
adjust the purchase intention and in the opposite condition also applies.
 Factor situation, which is not anticipated, this factor can arise and can change the
purchase intention of a consumer.

4.3. Post Purchase Behavior
After buying a product, consumers will experience satisfaction or dissatisfaction. Satisfaction
and dissatisfaction will influence subsequent behavior. If consumers feel satisfied, there will
be a repeat purchase, if not then the consumer will switch to another store until the consumer
is satisfied.

5. METHODS
Sources of data used in this study are college students who have an investment gallery and
registered as an investor in the university investment gallery in Aceh, North Sumatra, West
Sumatra and Pekan Baru, Riau, Indonesia. This population is not known exactly how many
students are investors in each investment gallery. The next step uses the Lemeshow
formulation to determine the total sample or population not known for certain (Hosmer and

Lemeshow, 2000) calculated using the formula:
N = Z2 P(1-P) : d2
Information :
Z=
1.96

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P=
Maximum estimation = 0.5 (a standard for research that has not been done before, if it
has been done then used the standard in accordance with previous research or use the number
P = 0.4)
d=
Alpha 5%
Then the total required sample is:
N = Z2 P(1-P) : d2
N = (1,962)(0,5)(1-0,5) : (0,052)
N = 384 sample
In this study response variable (Y) used has 2 categories, namely:
Y = 1 for investment decisions in non-real assets.
Y = 0 for non-investment decision
The predictor variable (X) used is:
The Description of variable as a follows :

Table 1 Description of Variable
Variable Type

Description
1=Male
0=Female
1=S1
0=Not S1
1=Has a financial advisor
0=Do not have a financial advisor

Gender (X1)
Education (X2)
Availability of financial advisor (X3)

Binary logistic regression is a method of data analysis used to find the relationship
between binary response or outcome or dependent variables, with predictor or explanatory or
independent variables (Hosmer and Lemeshow, 2000). The response variable Y consists of 2
categories, investment in non-real and non-investment assets denoted by Y = 1 (investment)
and Y = 0 (not invested). In such circumstances, the variable Y follows the Bernoulli
distribution for every single observation.

5.1. Simultaneous Test
The simultaneous test is conducted to find out the significance of β parameter to the response
variable as a whole. Testing the significance of these parameters using G test statistic, where
the test statistic G follows the distribution of Chi-Square (Hosmer and Lemeshow, 2000).
Hypothesis used :
H0 : β1= β2 ... βp =0
H1 : at least one βj ≠ 0, with i = 1, 2, …,p
Test statistics :

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Area of rejection : Reject Ho if G > X2

5.2. Partial Test
Individual test results will show whether a predictor variable is eligible to enter the model.
Hypothesis used :
H0 : βj = 0
H1: βj ≠ 0 with j= 1, 2, 3,…, p
Test Statistics: Wald Test Statistics

Area of rejection : reject Ho if

6. RESULTS AND DISCUSSION
Iteration History
The iteration history as a follows :
Table 2 Iteration History
Iteration Historya,b,c
Coefficients
-2 Log likelihood
Constant
Step 0
1

522.574
-.211
2
522.574
-.211
a. Constant is included in the model.
b. Initial -2 Log Likelihood: 522,574
c. Estimation terminated at iteration number 2 because parameter
estimates changed by less than, 001.
Iteration

Source: Processed SPSS test calculator results (2019).

H0 = model before entering the independent variable is FIT with data.
H1 = model before entering the independent variable is NOT FIT with data.
Accepting H1 if ...... Value -2Loglikelihood > Chi square table
In block 0 when the independent variable is not included in the model with the total sample N
= 384 got the value of log-2 likelihood 522,574.
Degree of Freedom (DF) = N-1 = 384-1 = 383
Chi square table on DF 383 and probability 0.05 = 429,6324881
Value -2Loglikelihood (522,574) > Chi square table (429,6324881), so reject H0, it’s
mean show model before entering independent variable is NOT FIT with data. Clasification
Table (Frequency of expectation based on the empirical data of the dependent variable) as a
follows :

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Table 3 Classification Table
Classification Tablea,b
Predicted
Investasi Aset Non Riil
tidak
investasi aset
investasi
non riil

Observed
Step 0

investment in non-real not investment feel
assets
satisfied.
investment in nonreal assets
Overall Percentage
a. Constant is included in the model.
b. The cut value is ,500
Source: Processed SPSS test calculator results (2019).

Percentage
Correct

210

0

100.0

170

0

.0
55.3

Classification Table is a contingency table 2 x 2 that should occur or also called the
expected frequency based on the empirical data of the dependent variable, where the number
of samples that have the category of dependent variable reference or decision of investment of
non real assets (code 1) is 170. Meanwhile, invested as much as 210. Total sample 384
people. So the overall percentage value before the independent variable is included in the
model of 55.3%. Variables in the equation as a follows :
Table 4 Variables in the Equation
Variables in the Equation
B
S.E.
Wald
Df
Step 0 Constant
-.211
.103
4.195
1
Source: Processed SPSS test calculator results (2019).

Sig.
.041

Exp(B)
.810

Table 4 explianed in the equation when before the independent variable is inserted into the
model, this means there is no independent variable in the model yet. The value of Slope or
Coefficient Beta (B) of Constant is equal to -0.211 with Odds Ratio or Exp (B) of 0.810. The
significance or p value of the Wald test is 0.041. Variable not in Equinning Stage Beginning
(Interpretation of Logistic Regression with SPSS) as a follows :
Table 5 Variable Not In Equinning Stage Beginning
Variables not in the Equation
Score
Step 0 Variables
X1(1)
.237
X2
1.756
X3
5.237
Overall Statistics
6.958
Source: Processed SPSS test calculator results (2019).

Df
1
1
1
3

Sig.
.627
.185
.022
.073

Table Variables not in the Equation shows the variables that have not been included in the
regression model, namely variables X1, X2, and X3. Where X1 is the gender variable, X2 is the
education level variable and X3 is the variable availability of the financial advisor. Entry
Stage Variable (Interpretation of Logistic Regression with SPSS) as a follows :

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Isfenti Sadalia, Fahmi Natigor Nasution, Iskandar Muda
Table 6 Entry Stage Variable
Iteration Historya,b,c,d
Coefficients
Iteration
-2 Log likelihood Constant
X1(1)
X2
X3
Step 1
1
515.309

-.263
.284
.264
-.937
2
515.213
-.266
.290
.269
-1.084
3
515.213
-.266
.290
.269
-1.090
4
515.213
-.266
.290
.269
-1.090
a. Method: Enter
b. Constant is included in the model.
c. Initial -2 Log Likelihood: 522,574
d. Estimation terminated at iteration number 4 because parameter estimates changed by less
than, 001.
Source: Processed SPSS test calculator results (2019).

When the independent variable is included in the model with N = 384.

Where :
H0 = model by entering independent variable is FIT with data
H1 = model by entering an independent variable is NOT FIT with data
Accept H0 if ........ Value -2 loglikelihood < Chi Square table
Degree of Freedom (DF)
= N-number of independent variables - 1
= 384-3-1 = 380.
Chi-Square Table On DF 380 and Prob 0.05 = 426,4537305
The value of -2 loglikelihood (515.213) < Chi Square table (426,4537305), thus receiving H0,
which shows the model by entering the independent variable is FIT with the data.
Table 7 Omnibus Test

Step 1

Omnibus Tests of Model Coefficients
Chi-square
Df
Step
7.361
3
Block
7.361
3
Model
7.361
3

Sig.
.061
.061

.061

Source: Processed SPSS test calculator results (2019).
Hypothesis used:
H0 = the addition of independent variables does NOT give a real effect on the model
H1 = addition of independent variables GIVE a real effect on the model
Accept H0 if .....
Chi square value < Chi square table or
sig value > alpha 0,05
In the omnimbus table, it can be seen that Chi square value 7,361 < Chi square table at df
3 (7,8147) or sig value 0.061 > 0,05 so it can be concluded that the received result is receiving
H0 which indicates that the addition of independent variable does NOT give effect real to the
model.
Interpretation of Logistic Regression with SPSS (Hypothesis Answers)
In OLS to test the simultaneous significance using F test, whereas in logistic regression
use the Chi-Square value of the difference between -2 Log likelihood before the independent
variables enter the model and -2 Log likelihood after the independent variable enter the
model. This test is also called Maximum likelihood testing.
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Hypothesis used:
H0 = no significant influence simultaneously level of education, sex, and availability of
financial advisor to investment decision on non real asset

H1 = there is significant influence simultaneously education level, gender, and availability of
financial advisor to investment decision on non real asset
Accept H0 if ........ Value p value Chi-Square > Alpha 0,05 or
Chi square < Chi square table
Table 8 Omnibus Test

Step 1

Omnibus Tests of Model Coefficients
Chi-square
Df
Step
7.361
3
Block
7.361
3
Model
7.361
3

Sig.
.061
.061
.061

Source: Processed SPSS test calculator results (2019).

Based on the Omnibus Test table it is known that the answer to the hypothesis of
independent simultaneous influence on the dependent variable is to accept H0 and reject H1 or

meaning no significant influence simultaneously level of education, sex, and availability of
financial advisors to investment decisions on non-real assets, value of p value Chi-Square
equal to 0.061 > Alpha 0,05 or Chi square value 7,361 < Chi square table at df 3 (7,8147).
Table 9 Psudo R Square
Model Summary
-2 Log
Cox & Snell R Nagelkerke R
Step
likelihood
Square
Square
1
515.213a
.019
.026
a. Estimation terminated at iteration number 4 because
parameter estimates changed by less than, 001.

Source: Processed SPSS test calculator results (2019).

Table Summary Model to see the ability of independent variables in explaining the
dependent variable, used the value of Cox & Snell R Square and Nagelkerke R Square. These
values are also called Pseudo R-Square or if the linear regression (OLS) is better known as RSquare.
The value of Nagelkerke R Square is 0.026 and Cox & Snell R Square 0.019, indicating
that the ability of independent variable in explaining the dependent variable is 0,026 or 2,6%
and there are 100% - 2,6% = 97,4% other factor outside model that explains the dependent
variable. The value of 2.6% can also mean the ability of education, gender, and availability of
financial advisors to explain investment decision variables on non-real assets of only 2.6%
and 97.4% explained other factors outside the model.
Table 10 Hosmer and Lemeshow Test

Hosmer and Lemeshow Test
Chi-square
df
Sig.
.162
2
.922
Source: Processed SPSS test calculator results (2019).
Step
1

Hosmer and Lemeshow Test is a test of Goodness of fit test (GoF), which is a test to
determine whether the model is correct or not (whether the model is appropriate). It is said
that if there is no significant difference between the model and the observation value, there
can be no difference between the observation result and the possibility of the model prediction
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result. The result of model conformity test on the data of factors influencing the decision
result to make investment is as follows.
Hypothesis used:
H0: The model is appropriate (there is no significant difference between observation results
and the possibility of model prediction)
H1: The model is not suitable (there is a significant difference between the observation result
and the possibility of predicted model)

Significant level: α = 0.05
Accept H1 if  P_value < α
The value of Chi Square table for DF 2 (DF 2 = Number of independent variables - 1) at
the 0.05 significance level is 5,9915.
Because the value of Chi Square Hosmer and Lemeshow calculate 0,162 table 5,9915 or significance value equal to 0,922 (> 0,05) so accept H0, indicating that CAN
model can be accepted and hypothesis CAN testing because NO significant difference
between model with the value of his observations.
Table 11 Classification Table
Classification Tablea

Observed
Step 1 investment in non-real not investment feel
assets
satisfied.
investment in non-real
assets
Overall Percentage
a. The cut value is ,500

Predicted
Investasi Aset Non Riil
investasi aset
tidak investasi
non riil

Percentage
Correct

135

75

64.3

97

73

42.9
54.7

Source: Processed SPSS test calculator results (2019).

Number of non-investment samples = 135 + 75 = 210 people. That really does not make
an investment of 135 people, and who should not invest but invest, as many as 75 people.
Number of samples that invested in non-real assets = 97 + 73 = 170 people. That really do
invest as many as 73 people and who should make investment but not investment, as many as
97 people.
Overall percentage of 54.7%, which means the accuracy of this research model is 54.7%.
A value of 54.7% can also be given the meaning that this logistic regression equation model
can predict investment decisions made in non-real assets, and can predict non-investment
decisions. In fact, investment decision on non-real assets is 54.7%.
Table 12 Parameter Estimation
Variables in the Equation

B
S.E.
X1(1)
.290

.467
X2
.269
.218
X3
-1.090
.518
Constant
-.266
.138
a. Variable(s) entered on step 1: X1, X2, X3.
Step 1a

Wald
.385
1.519
4.425
3.727

Df
1
1
1
1

Sig.
.535
.218
.035
.054

Exp(B)
1.336
1.309
.336
.766

95,0% C.I.for
EXP(B)
Lower
Upper
.535
3.337
.853
2.009
.122
.928

Source: Processed SPSS test calculator results (2019).

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Partial Test of Variable Level of Education (X1)

Hypothesis:
H0 = educational level does not give a significant partial effect on investment incidence in non
real sector
H1 = educational level gives a significant partial influence to investment incident on non real
sector
Accept H0 if ......Sig Wald value > Alpha 0,05
Table Variable in the Equation shows independent variable X1 value P value of wald test
(Sig) > 0,05, meaning variable X1 has no significant partial influence to Y in model. X1 or
education level has a value of Sig Wald 0,535 > 0,05 so receiving H0 or meaning education
level does not give a significant partial influence to the decision of investing in non-real
sector.
Partial Test of Variable Gender (X2)
Hypothesis:
H0 = gender does not give a significant partial effect on investment incidence in non real
sector
H1 = gender gives a significant partial effect on investment incidence in non real sector
Accept H0 if ........ Sig Wald value > Alpha 0,05
Table Variable in the equation shows independent variable X2 value P value of wald test
(Sig) > 0,05, meaning that variable X2 has no significant partial influence to Y in model. X2 or
sex has a Sig Wald value of 0.218 > 0.05 so receiving H0 or sex does not give a significant
partial effect on the decision to invest in the non-real sector.
Partial Test Variable Availability of Financial Advisors (X3)
Hypothesis:
H0 = availability of financial advisors does not provide a significant partial effect on the
incidence of investment in the non-real sector
H1 = availability of financial advisors gives a significant partial effect on the incidence of
investment in the non-real sector
Accept H0 if ............ Sig Wald value> Alpha 0,05
Table Variable in the equation shows independent variable X3 value P value of wald test
(Sig) < 0,05, it means X3 variable has significant partial influence to Y in model. X3 or the

availability of financial advisors has a value of Sig Wald 0.035 < 0.05 so that rejecting H0 or
which means the availability of financial advisory gives a significant partial effect on the
decision to invest in the non-real sector.

6.1. Odds Ratio
The amount of influence is shown by the value of Exp (B) or also called Odds Ratio (OR).
Odds ratio is an indicator of a person's tendency to perform or not to engage in activities.
Odds of an event are defined as the probability of an arising outcome divided by the
probability that an event does not occur. In accordance with this understanding, in the case of
investment decisions, the odds ratio coefficient indicates a tendency or opportunity to invest
or not to invest. If the odds ratio is close to zero, it means that one's inclination to invest is
very small.

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158




Isfenti Sadalia, Fahmi Natigor Nasution, Iskandar Muda
Table 13 Parameter Estimation
Variables in the Equation

B
S.E.
Wald
Step 1
X1(1)
.290
.467

.385
X2
.269
.218
1.519
X3
-1.090
.518
4.425
Constant -.266
.138
3.727
a. Variable(s) entered on step 1: X1, X2, X3.
Source: Processed SPSS test calculator results (2019).
a

df
1
1
1
1

Sig.
.535
.218
.035
.054

Exp(B)
1.336

1.309
.336
.766

95,0% C.I.for
EXP(B)
Lower Upper
.535
3.337
.853
2.009
.122
.928

Variable Level of Education (X1)
Variable X1 with OR 1,336 then X1 (code 1 independent variable), more likely to make or
tend to make investment decision in non real assets (code 1 dependent variable) as much as
1,336 times in comparison with which is not investment (code 0 independent variable). Value
B = Natural Logarithm of 1.336 = 0.290. Because B is a positive value, X1 has a positive
relationship with investment in non-real assets.
Gender Variable (X2)
Variable X2 with OR 1,309 then X2 (code 1 independent variable), it is more possible to
invest in non real assets (code 1 dependent variable) as much as 1,309 times in comparison
with non-investment (code 0 independent variable). Value B = Natural Logarithm of 1.309 =
0.269. Because B is a positive value, X2 has a positive relationship with the incidence of
investment in non-real assets.
Variable Availability of Financial Advisors (X3)
Variable X3 with OR 0.336 then X3 (code 1 independent variable), it is more possible to
invest in non-real assets (code 1 dependent variable) as much as 0.336 times in comparison
with non-investment (code 0 independent variable). Value B = Natural Logarithm of 0.336 = 1.090.

6.2. Logistic Regression Equation
Based on the values of B in Table Variable in the Equation, the equation model formed is as
follows:
Ln P/1-P
= -0,266 + 0,290 X1 + 0,269 X2 - 1,090 X3
or
Ln P / 1-P

= -0.266 + 0.290 Educational Level + 0.269 Gender
- 1,090 Availability of Financial Advisor

From this equation model it is found that the variables that have a significant effect
partially on the occurrence of investment decisions in non real assets is variable availability of
financial advisory.

7. DISCUSSION
There are differences between men and women in making financial decisions, and men are
better because they have higher financial knowledge. The majority of students have low
financial knowledge, and this can lead to inappropriate direction when making financial
decisions every day. Financial management skills are inseparable in one's life because
financial literacy is a useful tool for making financial decisions. Low financial knowledge will

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Logistic Regression Analysis to Know the Factors Affecting the Financial Knowledge in Decision of

Investment Non Riil Assets at University Investment Gallery

lead to making financial plans that are wrong and cause bias in achieving well-being at the
age of being unproductive. Financial difficulties are not only lack of income, financial
difficulties can also arise if there are errors in financial management such as misuse of credit,
and lack of financial planning. Financial limitations can cause stress, and low self-confidence.
Having financial expertise is vital to getting a prosperous, quality life. Further explained that
financial expertise with the environment of residence, the ability to read economic conditions
is the key to becoming a smart consumer. Everyone has equality for money. All transactions
use money. The amount of money owned and how to use money is different from each other.
However, certainly all need money. Financial management activities to fulfill daily
consumption needs until the long-term preparation process in the form of savings is also part
of financial expertise.

8. CONCLUSIONS
The test results with binary logistic regression on the model that is prepared that there is one
predictor variable that partially significant effect on investment decisions on non-real assets is
the availability of financial advisors. It is expected for investors to have financial advisors
when making investment decisions, so that the investment decision results to be the best
decision compared with other decision choices.

ACKNOWLEDGMENTS
This research work was DRPM Grant Years 2019.

CONFLICTS OF INTEREST
The authors declare no conflict of interest.

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