This document discusses various methods for collecting data for research purposes. It describes primary data collection methods like observation, interviews, questionnaires, and surveys which involve directly collecting original data. Secondary data collection involves using existing data collected by others. The key primary data collection methods covered are observation, interviews, questionnaires, and their types and steps. The advantages and disadvantages of each method are also outlined.
Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. The data collected ...
Methods of data collection (research methodology)Muhammed Konari
It includes all the classification of the Methods of Data Collection,Both Primary data and Secondary Data. Described all classifications which are included in the syllabus of KTU Kerala.
Methods of data collection (research methodology)Muhammed Konari
Included all types of data collection.Includes primary data collection and secondary data collection. Described each and every classification of Data collections which are included in KTU Kerala.
Some common data collection methods include surveys, interviews, observations, focus groups, experiments, and secondary data analysis. The data collected ...
Methods of data collection (research methodology)Muhammed Konari
It includes all the classification of the Methods of Data Collection,Both Primary data and Secondary Data. Described all classifications which are included in the syllabus of KTU Kerala.
Methods of data collection (research methodology)Muhammed Konari
Included all types of data collection.Includes primary data collection and secondary data collection. Described each and every classification of Data collections which are included in KTU Kerala.
This is lesson 7 of the course on Research Methodology conducted at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
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This is lesson 7 of the course on Research Methodology conducted at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. What is Data Collection?
It is the process by which the
researcher collects the information
needed to answer the research
problem.
3. In collecting the data,the researcher must decide:
Which data is to collect?
How to collect the Data?
Who will collect the Data?
When to collect the Data?
5. Methods of Data Collection
Essentialy Two Types:
PRIMARY DATA
Primary data are those which are collected for
the first time and are original in character.
SECONDARY DATA
Secondary data are those which have already
been collected-by someone else.
7. Methods of Collecting Primary Data
•Observation
• .Interviews
•questionnaires
•Schedules
•surveys
Primary
Data may
be
collected
through:
8. 1. Observation Method
Observation method is a method under which data
from the field is collected with the help of
observation by the observer or by personally going to
the field.
9. Steps ForAn Effective Observation
Determine what needs to be observed
Select participants
Random/Selected
Conduct the observation
(venue, duration, recording materials, take photographs )
Compile data collected
Analyze and interpret data collected
10. Types of OBSERVATION Methods
1- Structured Observation
When the observation is characterized by a careful definition
of the units to be observed (predefined), the style of recording the
observed information, standardized conditions of observation and
the selection of related data of observation.
2- Unstructured Observation
When it takes place without the above characteristics.
(Not predefined)
11. 3- Participant Observation
When the observer is member of the group which he is
observing then it is Participant Observation.
4- Non-Participant Observation
When the observer is not the member of the group
which he is observing then it is Non-Participant Observation.
observer is observing people without giving any
information to them then it is Non-Paricipant Observation.
12. 5- Uncontrolled Observation
When the observation takes place in natural contition i.e.,
uncontrolled observation.It is done to get spontaneous picture of
life and persons.
6- Controlled Observation
When observation takes place according to pre-arranged
plans, with experimental procedure then it is controlled observation
generally done in laboratory under controlled condition.
13. Advantages of observation Method
Produces Large quantities of data.
All data obtained from observations are usable.
The observation technique can be stopped or begun at any time.
Relative Inexpensive
14. disadvantages of observation Method
Interviewing selected subjects may provide more
information, economically, than waiting for the
spontaneous occurrence of the situation.
Limited information
Extensive Training is needed.
15. 3.Interview Method
The Interview Method of collecting data
involves presentation of oral-verbal stimuli
and reply in terms of oral- verbal responses.
where the questions are asked personally
directly to the respondent.
Interviewer asks questions to respondent.
(which are aimed to get information
required for study)
16. Prepare interview schedule
Select subjects/ key Respondent
Conduct the interview
Analyze and interpret data collected from the interview
Steps ForAn EffectiveInterview
17. Types of Interview Methods
1- Structured Interviews :
In this case, a set of predecided questions
are there.
2- Unstructured Interviews :
In this case, we don’t follow a system of
pre-determined questions.
18. 3- Focused Interviews:
Attention is focused on the given
experience of the respondent and its possible
effect
4-Group interview:
a group of 6 to 8 individuals interwied.
19. Advantages of Interview Method
More information at greater
depth can be obtained
Resistance may be overcome by
a skilled interviewer
Personal information can be
obtained
20. disadvantages of Interview Method
It is an expensive Method
Interviewer bias
Respondent bias
Time consuming
21. 4.Questionnaires
A Questionnaire is sent ( by post or by mail ) to the
persons concerned with a request to answer the
questions and return the Questionnaire.
A Questionnaire consists of a number of questions
printed in a definite order on a form.
22. Steps ForAn Effective Questionnaire
Prepare questions
(Formulate & choose types of questions, order them, write instructions, make copies)
Select your respondents
Random/Selected
Administer the questionnaire
(date, venue, time )
Tabulate data collected
Analyze and interpret data collected
23. Types of Questionnaire Methods
1- Open-ended questions
This gives the respondents the ability to respond in their own
words.
2- Close-ended or fixed alternative questions
This allows the respondents to choose one of the given
alternatives.
Types:- Dichotomous questions and Multiple Questions.
24. Essentials of Good Questionnaire
Should be short and simple
Follow a sequence of questions from easy to difficult one
Technical terms should be avoided
Should provide adequate space for answers in
questionnaire
Directions regarding filling of questionnaire should be
given Physical Appearance – Quality of paper, Color
Sequence must be clear
25. advantages of questionnaire Method
Low cost –even when the universe is large and is widespread
Free from interviewer bias
Responddents have adequate time to think through the answers.
Respondents who are not easily approachable, can also be reached
conveniently.
Large samples can be used
26. disadvantages of questionnaire Method
Time consuming
The respondents need to be educated and cooperative
This method is slow
Possibility of unclear replies.
27. Secondary Data Collection Methods
• Data gathered and recorded by someone else.
• Secondary data is data that has been collected for
another purpose.
• It involves less cost, time and effort.
• Secondary data is data that is being reused. Usually
in a different context.
• For example: data from a book.
28. SOURCES of secondary data collection
INTERNAL SOURCES
Internal sources of secondary data are usually for
marketing application-
Sales Records
Marketing Activity
Cost Information
Distributor reports and feedback
Customer feedback