![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.)](https://izqule7twkl7vq3ljkxejyz-s-a2157.bj.tsgdht.cn/w6descriptivestatisticsanalysis-240729082925-58ca6c77/85/Descriptive-Statistics-Analysis-Week-6-4-320.jpg)
![ 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]](https://izqule7twkl7vq3ljkxejyz-s-a2157.bj.tsgdht.cn/w6descriptivestatisticsanalysis-240729082925-58ca6c77/85/Descriptive-Statistics-Analysis-Week-6-5-320.jpg)
More Related Content
Similar to Descriptive Statistics Analysis - Week 6 (20)
Recently uploaded (20)
Descriptive Statistics Analysis - Week 6
- 1. 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
- 2. 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.
- 3. 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
- 4. 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.)
- 5. 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]
- 6. 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.
- 7. Source of Data Source of data Continuous Discrete Qualitative (categorical) Quantitative (numerical) Discrete Quantitative Analysis Qualitative Analysis
- 8. 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.
- 9. 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.
- 10. Example of Discrete Quantitative Data Nilai Quiz 1 MPPI 2016 Frekuensi
- 11. Example of Continuous & Quantitative Data
- 12. 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.
- 13. 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.
- 14. Types of Data Secondary Data Compilation Observation Experimentation Print or Electronic Survey Primary Data Collection Basic Business Statistics 10e, 2006 Prentice Hall
- 16. 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
- 17. 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....
- 18. Example of Nominal Data Simple analysis of the above data, presenting them in bar chart.
- 19. 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.
- 20. Example of Ordinal Data How do you analyze above data?
- 21. 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.
- 22. Example of Interval Data How do you analyze interval data?
- 23. 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
- 24. Example of Ratio Data How do you analyze ratio data?
- 25. 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.
- 26. 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.
- 27. 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.
- 29. 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.
- 30. No No
- 31. 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
- 32. Collecting Data
- 33. 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
- 34. 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?
- 35. Collecting Quantitative Data What information you collect? – Observations – Interviews and questionnaires – Documents – Audiovisual materials Use formalized instrument to collect each information.
- 36. Presenting Data
- 37. Teknik Penyajian dan Peringkasan Data dan Informasi Peringkasan Data Ukuran Pemusatan Ukuran Penyebaran Teknik Penyajian Tabel Grafik
- 38. 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%
- 39. Representing Data as Pie Chart 9% 65% 23% 3% Graphic Pie Chart Buat legendnya:
- 40. 9% 65% 23% 3% 0% 10% 20% 30% 40% 50% 60% 70% Graphic Bar Chart Representing Data as Graphs
- 41. 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
- 42. 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
- 43. Frequency Distribution Polygons 0 1 2 3 4 5 1 2 3 4 5 6 7 8 9 10 11 12
- 44. Frequency Distribution Bar Chart 0 2 4 6 8 10 12 14 16 phone numbers historical dates family dates
- 45. 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
- 46. 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
- 48. 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.
- 49. 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.
- 50. Histogram – Mengukur Distribusi FREQUENCY Skewed to Right FREQUENCY Symmetric FREQUENCY WEIGHT WEIGHT WEIGHT Skewed to Left Miring Ke kiri SIMETRIK Miring Ke KANAN
- 51. Kaitan Antara Distribusi dengan Ukuran Pemusatan Mean = Median = Mode
- 52. Analyze Quantitative Data 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.
- 53. Analyze Quantitative Data 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.
- 54. 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
- 55. Thank You