Descriptive Statistics Analysis - Week 6

Descriptive Statistic Analysis:
Collecting, Presenting, and
Analyzing Quantitative Data
Disampaikan oleh:
Tim Pengajar MPPI
Research Methodology and Scientific Writing
Faculty of Computer Science, University of Indonesia
Oct 2019

Pengantar…
 Salah satu komponen penelitian adalah data,
disamping permasalahan dan penyelesaian
masalah secara sistematis (metodologi).
 Data mesti dikumpulkan secara objective (tidak
boleh subjective) dengan instrument tertentu.
 Data ada yang bersifat kuantitatif dan/atau
kualitatif.
 Penelitian kuantitatif menggunakan data yang
bersifat kuantitatif.

Discussion
 Understanding Quantitative Research
 Source of Data
 Types of Data
 Parametric vs. Non-Parametric Statistics
 Validity vs. Reliability
 Collecting and Presenting Quantitative Data
 Analyzing Quantitative Data

Quantitative Research: Definition
(Source: Wikipedia)
 In sociology, quantitative research refers to the
systematic empirical investigation of social
phenomena via statistical, mathematical or
computational techniques.[1]
 The objective of quantitative research is to develop
and employ mathematical models, theories and/or
hypotheses pertaining to phenomena.
 Intinya, data kuantitatif adalah data empiris hasil dari
suatu pengamatan (bisa hasil survey, hasil
experiment, hasil observasi, dll.)

 The process of measurement is central to
quantitative research because it provides the
fundamental connection between empirical
observation and mathematical expression of
quantitative relationships.
 Quantitative data is any data that is in
numerical form such as statistics,
percentages, etc.[1]

 In layman's terms, this means that the quantitative
researcher asks a specific, narrow question and
collects a sample of numerical data from participants
to answer the question.
 The researcher analyzes the data with the help of
statistics.
 The researcher is hoping the numbers will yield an
unbiased result that can be generalized to some
larger population.

Source of Data
Source of data
Continuous
Discrete
Qualitative
(categorical)
Quantitative
(numerical)
Discrete
Quantitative Analysis
Qualitative Analysis

What Is Quantitative Data? (Source:
http://study.com/academy/lesson/what-is-quantitative-data.html, March 2016)
 What's the difference between having seven apples and saying
that they are delicious?
– We can count or measure the seven apples, but…
– We can't put a number to how delicious they are. Those apples
might be delicious to one person and be completely sour to another
person.
 Saying you have seven apples, because they can be
represented numerically, is a piece of quantitative data. But
saying that they are delicious is not because you can't write that
using numbers.
 There are two types of data that quantitative data covers: can be
counted and can be measured.

Quantitative or Numerical Data
 Discrete Data
– Only certain values are possible (there are gaps
between the possible values).
 Continuous Data
– Theoretically, any value within an interval is
possible with a fine enough measuring device.

Example of Discrete Quantitative Data
Nilai Quiz 1 MPPI 2016
Frekuensi

Example of Continuous & Quantitative Data

Continuous and Discrete (End of citing from
http://changingminds.org/explanations/research/measurement/types_data.htm)
 Continuous measures are measured along a continuous
scale which can be divided into fractions, such as
temperature. Continuous variables allow for infinitely fine
sub-division, which means if you can measure
sufficiently accurately, you can compare two items and
determine the difference.
 Discrete variables are measured across a set of fixed
values, such as age in years (not microseconds). These
are commonly used on arbitrary scales, such as scoring
your level of happiness, although such scales can also
be continuous.

Types of Data
 Primary data: data observed and recorded or collected
directly from respondents.
– Data diperoleh secara langsung dari objek pengamatan.
 Secondary data: data complied both inside and outside
the organization for some purpose other than the
current investigation.
– Data diperoleh dari sumber lain seperti buku laporan,
artikel, dll. Sipeneliti tidak secara langsung melakukan
pengamatan kepada objek penelitian.
 Kedua jenis data tersebut dapat digunakan sebagai
data penelitian.

Types of Data
Secondary
Data
Compilation
Observation
Experimentation
Print or Electronic
Survey
Primary
Data Collection
Basic Business Statistics 10e, 2006 Prentice Hall

Descriptive Statistics Analysis - Week 6

Measurement Scales of Data
Ratio Data
Interval Data
Ordinal Data
Nominal Data
Differences between
measurements, true
zero exists.
Differences between
measurements but no
true zero
Ordered Categories
(rankings, order, or scaling)
Categories (no ordering
or direction)
Height, Age, Weekly
Food Spending
Temperature in Fahrenheit,
Standardized exam score
Service quality rating,
Standard & Poor’s bond
rating, Student letter grades
Marital status, Type of car
owned
Basic Business Statistics 10e, 2006 Prentice Hall

Nominal Data (Sumber:
http://changingminds.org/explanations/research/measurement/types_data.html)
 The name 'Nominal' comes from the Latin nomen, meaning 'name' and
nominal data are items which are differentiated by a simple naming system.
 The only thing a nominal scale does is to say that items being measured
have something in common, although this may not be described.
 Nominal items may have numbers assigned to them. This may appear
ordinal but is not -- these are used to simplify capture and referencing.
 Nominal items are usually categorical, in that they belong to a
definable category, such as 'employees'.
 Nominal scales are used for labeling variables, without any
quantitative value. “Nominal” scales could simply be called
“labels.”
 Example: The number male and female students at Fasilkom, UI, colors of
their hair, place of their stay....

Example of Nominal Data
Simple analysis of the above data, presenting them in bar chart.

Ordinal Data
 Items on an ordinal scale are set into some kind of order by their
position on the scale. This may indicate such as temporal position,
superiority, etc.
 The order of items is often defined by assigning numbers to them to
show their relative position. Letters or other sequential symbols may
also be used as appropriate.
 Ordinal items are usually categorical, in that they belong to a definable
category, such as '1956 marathon runners'.
 You cannot do arithmetic with ordinal numbers -- they show sequence
only.
 Ordinal scales are typically measures of non-numeric concepts like
satisfaction, happiness, discomfort, etc.
 Example: The first, third and fifth person in a race; Pay bands in an
organization, as denoted by A, B, C and D.

Example of Ordinal Data
How do you analyze above data?

Interval Data
 Interval data (also sometimes called integer) is measured along
a scale in which each position is equidistant from one another.
This allows for the distance between two pairs to be equivalent
in some way.
 This is often used in psychological experiments that measure
attributes along an arbitrary scale between two extremes.
 Interval scales are numeric scales in which we know not only
the order, but also the exact differences between the values
 Interval data cannot be multiplied or divided.
 Example
– My level of happiness, rated from 1 to 10.
– Temperature, in degrees Fahrenheit.

Example of Interval Data
How do you analyze interval data?

Ratio Data
 In a ratio scale, numbers can be compared as multiples of one another.
Thus one person can be twice as tall as another person. Important also,
the number zero has meaning.
 Thus the difference between a person of 35 and a person 38 is the same
as the difference between people who are 12 and 15. A person can also
have an age of zero.
 Ratio data can be multiplied and divided because not only is the difference
between 1 and 2 the same as between 3 and 4, but also that 4 is twice as
much as 2.
 Interval and ratio data measure quantities and hence are quantitative.
Because they can be measured on a scale, they are also called scale data.
 Example: A person's weight; The number of pizzas I can eat before
fainting

Example of Ratio Data
How do you analyze ratio data?

 Categorical data are such that measurement scale
consists of a set of categories.
 SOME VISUALIZATION TECHNIQUES for categorical
data: Jittering, mosaic plots, bar plots etc.
 Correlation between ordinal or nominal measurements
are usually referred to as association.

Parametric vs. Non-parametric
 Interval and ratio data are parametric, and
are used with parametric tools in which
distributions are predictable (and often
Normal).
 Nominal and ordinal data are non-
parametric, and do not assume any particular
distribution. They are used with non-
parametric tools such as the Histogram.

Statistics
 Parametric Statistics
– Parametric statistics is a branch of statistics which
assumes that sample data comes from a population
that follows a probability distribution based on a fixed
set of parameters. Most well-known elementary
statistical methods are parametric.
 Non-parametric Statistics
– Nonparametric statistics are statistics not based on
parameterized families of probability distributions.
They include both descriptive and inferential statistics.

Validity and Reliability
 In science and statistics, validity has no
single agreed definition but generally refers
to the extent to which a concept, conclusion
or measurement is well-founded and
corresponds accurately to the real world.
 In normal language, we use the word reliable
to mean that something is dependable and
that it will give the same outcome every time.

Diskusi….
 Berikan contoh-2 penggunaan data nominal ,
ordinal, interval, dan ratio dalam bidang
Sistem Informasi dan Teknologi Informasi.
 Pengolahan statistika apa saja yang sesuai
untuk masing-2 data?
 Sejauh mana kita bisa menyimpulkan hasil
dari berbagai type data tersebut?
– Validitas
– Reliabilitas

Collecting Quantitative Data
 Identify your unit analysis
– Who can supply the information that you will use to answer
your quantitative research questions or hypotheses?
 Specify the population and sample
 Information will you collect
– Specify variable from research questions and hypotheses
– Operationally define each variable
– Choose types of data and measures

Instrument Will You Use To Collect
Quantitative Data
 Locate or develop an instrument
 Search for an instrument
 Criteria for choosing a good instrument
– Have authors develop the instrument recently, and can you
obtain the most recent version?
– Is the instrument widely cited by other authors?
– Are reviews available for the instrument?
– Is there information about the reliability and validity of
scores from past uses of the instrument?
– Does the procedure for recording data fit the research
questions/hypotheses in your study?
– Does the instrument contain accepted scales of
measurement?

Collecting Quantitative Data
 What information you collect?
– Observations
– Interviews and questionnaires
– Documents
– Audiovisual materials
 Use formalized instrument to collect each
information.

Teknik Penyajian dan Peringkasan
Data dan Informasi
Peringkasan Data
Ukuran Pemusatan
Ukuran Penyebaran
Teknik Penyajian
Tabel
Grafik

Example of Table from Quantitative Data
Kategori Frekuensi Frekuensi
relatif
Persentase
A 35 35/400=0.09 9%
B 260 260/400=0.65 65%
C 93 93/400=0.23 23%
D 12 12/400=0.03 3%
Total 400 1 100%

Representing Data as Pie Chart
9%
65%
23%
3%
Graphic Pie Chart
Buat legendnya:

9%
65%
23%
3%
0%
10%
20%
30%
40%
50%
60%
70%
Graphic Bar Chart
Representing Data as Graphs

Penyusunan Penyebaran (Distribusi)
Frekuensi
Contoh : Data Tinggi Badan (Cm) Dari 50
Orang Dewasa
176 167 180 165 168 171 177 176 170 175
169 171 171 176 166 179 181 174 167 172
170 169 175 178 171 168 178 183 174 166
181 172 177 182 167 179 183 185 185 173
179 180 184 170 174 175 176 175 182 172

Distribusi Frekuensi Tinggi Badan
Interval kelas Frekuensi Jumlah
164,5 - 167,5 6
167,5 - 170,5 7
170,5 - 173,5 8
173,5 - 176,5 11
176,5 - 179,5 7
179,5 - 182,5 6
182,5 - 185,5 5
Jumlah 50

Frequency Distribution Polygons
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11 12

Frequency Distribution Bar Chart
0
2
4
6
8
10
12
14
16
phone numbers historical dates family dates

Frequency
4
2
0
-2
-4
-6
40
30
20
10
0
4
2
0
-2
-4
-6
20
15
10
5
0
data1 data2
Histogram of data1, data2
Frequency
4
3
2
1
0
-1
-2
25
20
15
10
5
0
4
3
2
1
0
-1
-2
20
15
10
5
0
data1 data3
Histogram of data1, data3
Ukuran Pemusatan relatif sama namun
ukuran penyebaran relatif berbeda
Ukuran Pemusatan relatif berbeda
namun ukuran penyebaran relatif sama
?
C14
Frequency
5
4
3
2
1
0
-1
-2
30
25
20
15
10
5
0
Histogram of C14
bimodus
outlier

Interpretation…
C14
Frequency
5
4
3
2
1
0
-1
-2
30
25
20
15
10
5
0
Histogram of C14
Dari grafik di atas, kemungkinan sample yang diambil berasal
dari populasi yang berbeda

 The basic quantitative analysis of data
use descriptive statistics.
 Descriptive statistics describe the basic
features of the data in a study. They provide
simple summaries about the sample and the
measures. Together with simple graphics
analysis, they form the basis of virtually
every quantitative analysis of data.

Analyze Quantitative Data
 Describe trends in the data to a single variable or
question on your instrument.
– We need Descriptive Statistics that indicate:
 general tendencies in the data mean, median, mode,
 the spread of scores (variance, standard deviation, and
rang),
 or a comparison of how one score relates to all others
(z-scores, percentile rank).
 We might seek to describe any of our variables:
independent, dependent, control or mediating.

Histogram – Mengukur Distribusi
FREQUENCY
Skewed
to Right
FREQUENCY
Symmetric
FREQUENCY
WEIGHT WEIGHT WEIGHT
Skewed
to Left
Miring
Ke kiri
SIMETRIK
Miring
Ke KANAN

Kaitan Antara Distribusi dengan Ukuran
Pemusatan
Mean = Median = Mode

 Compare two or more groups on the independent
variable in terms of the dependent variable.
– We need inferential statistics in which we analyze data
from a sample to draw conclusions about an unknown
population  involve probability.
– We assess whether the differences of groups (their
means) or the relationships among variables is much
greater or less than what we would expect for the total
population, if we could study the entire population.

 Relate two or more variable.
– We need inferential statistics.
 Test hypotheses about the differences in the
groups or the relationships of variables.
– We need inferential statistics.

Pengujian Hipotesis
Hipotesis satu arah
 H0 :   0 vs H1 :  < 0
 H0 :   0 vs H1 :  > 0
Hipotesis dua arah
 H0 :  = 0 vs H1 :   0
 Statistik uji:
– Jika ragam populasi (2) diketahui :
– Jika ragam populasi (2) tidak diketahui :
n
s
x
th
/
0



n
x
zh
/
0





Descriptive Statistics Analysis - Week 6

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