Capstone Project (S.I.P.

)
QUANTITATIVE DATA COLLECTION
Objectives:

After going through this module, you are expected to:

1. describe what quantitative data is;

2. identify the methods for collecting data;
3. explain the procedure for data collection; and
4. collect sample data from initial part of Science Investigation/ Experimentation
It is necessary to employ the appropriate tool and technique to draw out the information relevant to research and its objectives.
The tool and technique of data collection varies according to purpose of research, nature of data, nature of sample etc.

The tools may be enlisted as follows:

1. observation 3. questionnaires 5. inventories & tests

2. interviews 4. rating scales
Statistics is a branch of mathematics dealing with the collection, analysis, presentation, interpretation, and conclusion of data,
while biostatistics is a branch of statistics, where statistical techniques are used on biomedical data to reach a conclusion.
Measurement scale (data type) is an important part of data collection, analysis, and presentation. In the data collection, the type of
questionnaire and the data recording tool differ according to the data types. Similarly, in the data analysis, statistical tests or
methods differ from one data type to another. Data presentation is an important step to communicate our information and findings
to the audience and readers in an effective way. If done properly, they not only reduce word count but also convey an important
message in a meaningful way so that the readers can grasp it easily. There are various tabulation and graphical methods used to
present the data, which are not possible without proper knowledge of data types.
Data are a collection of facts such as values or measurements. It can be numbers, words, measurements, observations, or even just
descriptions of things. Basically, data are two types: constant and variable. Constant is a situation or value that does not change,
while a characteristic, number, or quantity that increases or decreases over time or takes different values in different situations is
called variable. Due to unchangeable property, constant is not used and only variable is used for summary measures and analysis.
Within the context of a research investigation, concepts are generally referred to as variables. A variable is, as the name applies,
something that varies. Age, sex, export, income and expenses, family size, country of birth, capital expenditure, class grades,
blood pressure readings, preoperative anxiety levels, eye color, and vehicle type are all examples of variables because each of
these properties varies or differs from one individual to another. There are five types of variable in terms of research methodology
as follows:
For independent variable, the value is not affected by the change in the value of another variable but affects the value of another
variable.
For dependent variable, the value may change due to change in the value of another variable.
We know that independent variable affects the value of dependent variable and there has been cause and effect relationship
between these two. The variable that affects the cause-and-effect relationship between these two variables is called moderator
variable. It means the effect of independent variable may be different in the presence of moderator variable.
Next, if the effect of moderator variable (which can affect the cause-and-effect relationship of dependent and independent
variable) is eliminated, it is called controlled variable
Lastly, any such variable is called intervening variable, that may affect the cause-and-effect relationship of dependent and
independent variables but either cannot be measured clearly or is to be ignored during research. It means, intervening variables are
neither controlled nor taken care of during research. In other words, any moderator variable, that cannot be measured or observed
clearly or ignored is called intervening variable. See Figure 1 for better understanding.
For quantitative research, there are four types of variables: nominal, ordinal, discrete, and continuous. The first two are called
qualitative data and the last two are quantitative data. The first two (nominal and ordinal) are assessed in terms of words or
attributes called qualitative data, whereas discrete and continuous variables are part of the quantitative data.

Quantitative variable is the data that show some quantity through numerical value. Quantitative data are the numeric variables
(e.g., how many, how much, or how often). Age, blood pressure, body temperature, hemoglobin level, and serum creatinine level
are some examples of quantitative data. It is also called metric data. It has two types: discrete and continuous.
Discrete variable is the quantitative data, but its values cannot be expressed or presented in the form of a decimal. For example,
number of males, number of females, number of patients, and family size are data that cannot be expressed in decimal points.
Continuous data are measured in values and can be quantified and presented in decimals. Age, height, weight, body mass index,
serum creatinine, heart rate, systolic blood pressure, and diastolic blood pressure are some examples.
Data collection is the process of gathering and measuring information on variables of interest in an established systematic fashion
that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.
The data collection component of research is common to all fields of study including physical and social sciences, humanities,
business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.
The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a
convincing and credible answer to questions that have been posed. Data collection is one of the most important stages in
conducting a research. You can have the best research design in the world but if you cannot collect the required data you will not
be able to complete your project.
Data collection is a very demanding job which needs thorough planning, hard work, patience, perseverance and more to be able to
complete the task successfully. Data collection starts with determining what kind of data required followed by the selection of a
sample from a certain population. After that, you need to use a certain instrument to collect the data from the selected sample. Let
us now take a closer look on quantitative data.

Data collection is the process of gathering information on variables of interest from a sample of research participants. There are
two types of data collection:
1. Primary data collection refers to data that is collected from research participants directly by the investigators of a study and
the data is used for that study. Below are some of the sources of primary data:
a. Experiments require an artificial or natural setting in which to perform logical study to collect data. Experiments are more
suitable for medicine, psychological studies, nutrition, and for other scientific studies. In experiments, the experimenter must keep
control over the influence of any extraneous variable on the results.
b. Survey is the most commonly used method in social sciences, management, marketing, and psychology to some extent.
Surveys can be conducted in different methods.
c. Questionnaire is the most commonly used method in survey. Questionnaires are list of questions either open-ended or close-
ended for which the respondents give answers. Questionnaire can be conducted via telephone, mail, live in a public area, or in an
institute, through electronic mail or through online platforms and other methods.
d. Interview is a face-to-face conversation with the respondent. In interview the main problem arises when the respondent
deliberately hides information otherwise it is an in-depth source of information. The interviewer can not only record the
statements the interviewee speaks.
2. Secondary data collection refers to data that is collected by investigators from research papers that are already published
online. Secondary data is used by these investigators in a secondary research study (e.g., review of primary research).
The following are some examples of collecting secondary data:
• Books • Data archives
• Records • Internet articles
• Biographies • Research articles by other researchers (journals)
• Newspapers • Databases, etc.
• Published censuses or other statistical data
Quantitative Data
It is numerical in nature and can be mathematically computed. Quantitative data measure uses different scales, which can be
classified as nominal scale, ordinal scale, interval scale and ratio scale.
Often (not always), such data includes measurements of something. Quantitative approaches address the ‘what’ of the study. They
use a systematic standardized approach and employ methods such as surveys and ask questions.
Quantitative approaches have the advantage that they are cheaper to implement, are standardized so comparisons can be easily
made, and the size of the effect can usually be measured. Quantitative approaches however are limited in their capacity for the
investigation and explanation of similarities and unexpected differences. It is important to note that for peer-based programs
quantitative data collection approaches often prove to be difficult to implement for agencies as lack of necessary resources to
ensure rigorous implementation of surveys and frequently experienced low participation and loss to follow up rates are commonly
experienced factors.
The quantitative data collection methods rely on random sampling and structured data collection instruments that fit diverse
experiences into predetermined response categories. They produce results that are easy to summarize, compare, and generalize. If
the intent is to generalize from the research participants to a larger population, the researcher will employ probability sampling to
select participants.
There are two main quantitative data collection methods:
1. Surveys: Traditionally, surveys were conducted using paper-based methods and have gradually evolved into online mediums.
Closed-ended questions form a major part of these surveys as they are more effective in collecting quantitative data. The survey
makes include answer options which they think are the most appropriate for a particular question. Surveys are integral in
collecting feedback from an audience which is larger than the conventional size. A critical factor about surveys is that the
responses collected should be such that they can be generalized to the entire population without significant discrepancies. On the
basis of the time involved in completing surveys, they are classified into the following –
a. Longitudinal Studies are types of observational research in which the market researcher conducts surveys from a specific time
period to another (i.e., over a considerable course of time, is called longitudinal survey). This survey is often implemented for
trend analysis or studies where the primary objective is to collect and analyze a pattern in data.

b. Cross-sectional Studies are types of observational research in which the market research conducts surveys at a particular time
period across the target sample is known as cross-sectional survey. This survey type implements a questionnaire to understand a
specific subject from the sample at a definite time period.
To administer a survey to collect quantitative data, the below principles are to be followed.
a. Fundamental levels of measurement (nominal, ordinal, interval and ratio scales). There are four measurement scales which
are fundamental to creating a multiple-choice question in a survey in collecting quantitative data. They are, nominal, ordinal,
interval and ratio measurement scales without the fundamentals of which, no multiple-choice questions can be created.
b. Use of different question types. To collect quantitative data, close-ended questions have to be used in a survey. They can be a
mix of multiple question types including multiple-choice questions like semantic differential scale questions, rating scale
questions etc. that can help collect data that can be analyzed and made sense of.
c. Survey distribution and survey data collection. In the above, we have seen the process of building a survey along with the
survey design to collect quantitative data. Survey distribution to collect data is the other important aspect of the survey process.
There are different ways of survey distribution. Some of the most commonly used methods are:
➢ e-mail
➢ sample size
➢ embedding a survey
➢ social distribution
2. One-on-one Interviews. This quantitative data collection method was also traditionally conducted face-to-face but has shifted
to telephonic and online platforms. Interviews offer a marketer the opportunity to gather extensive data from the participants.
Quantitative interviews are immensely structured and play a key role in collecting information. There are three major sections of
these online interviews:
a. face-to-face interviews
b. online or telephonic interviews
c. computer assisted personal interview
Data Collection Procedure It is a systematic process of gathering observations or measurements. Whether you are performing
research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original
insights into your research problem. While methods and aims may differ between fields, the overall process of data collection
remains largely the same. Before you begin collecting data, you need to consider:
a. the aim of the research;
b. the type of data that you will collect; and
c. the methods and procedures you will use to collect, store, and process the data.
To collect high-quality data that is relevant to your purposes, follow these four steps.
Step 1: Define the aim of your research. Before you start the process of data collection, you need to identify exactly what you
want to achieve. You can start by writing a problem statement: what is the practical or scientific issue that you want to address and
why does it matter?
Step 2: Develop operational definitions and procedures. What are we measuring? How will it be measured? Who will measure it?
Having clarity in these questions is of utmost importance. Often, we will employ sampling in which case we need to define a
sampling plan.
Step 3: Choose more than one data collection technique. There is no “best” tool. Do not let the tool drive your work but rather
choose the right tool to address the evaluation question.
Step 4: Begin to collect your data. From these, you have already learned the basic quantitative data collection. Let us try to answer
the activities below.

LESSON ACTIVITIES FOR QUANTITATIVE DATA

A. Identify whether the following statements are primary data or secondary data.
Write SD or PD for your answers on the space before each number.
_______1. an artifact, document, diary, manuscript, or other source of information created at the
time of study
_______2. a training record
_______3. a map produced in 2016 showing what land European countries controlled in the world
in the 18th
_______4. a professional journal discussing the impact of rust on metal
_______5. a source created about an event or time-period by someone who was there

B. Read each statement carefully. Choose the letter of the best answer. Write your answers on your notebook.
1. Which one is a strength of using questionnaires in the data gathering?
a. cheap and easy way to collect data c. low response rate
b. interviewer effect d. unethical
2. Which one is a weakness of questionnaires in the data gathering?
a. cheap c. interviewer effect
b. demand characteristics d. low response rate
3. Which one is a strength of observations in the data gathering?
a. cheap and easy way to collect data c. quick
b. first hand data, which is rich in validity d. representative
4. What is quantitative data?
a. A study of an individual or group over a long period of time
b. A small scale study done prior to the actual study
c. Data collected the written or descriptive form
d. Data collected in the numeric form
5. Which of the following is a type of secondary data?
a. interview c. observation
b. official statistics d. questionnaire

Performance Task:
A. From the experimentation you are currently undergoing, perform the following:
1) Recognize the variables in your experiment and identify which ones, if any, are the
a) dependent variable
b) independent variable
c) moderator variable
d) controlled variable
 e) intervening variable
2) Analyze and explain what makes each of these variables belong to the categories you have placed them.

B. Prepare a table of the initial data you have gathered in your experiment. Specify the details of your trials.