what is experiment method of data collection

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Data Collection Methods | Primary and Secondary Data

Data Collection refers to the systematic process of gathering, measuring, and analyzing information from various sources to get a complete and accurate picture of an area of interest. Data collection is a critical step in any research or data-driven decision-making process, ensuring the accuracy and reliability of the results obtained. By employing various methods of data collection researchers and organizations can gather the necessary data to support their objectives effectively.

What is Data Collection?

Data Collection is the process of collecting information from relevant sources to find a solution to the given statistical inquiry. Collection of Data is the first and foremost step in a statistical investigation. It’s an essential step because it helps us make informed decisions, spot trends, and measure progress.

Different methods of collecting data include

• Questionnaires
• Observations
• Experiments
• Published Sources and Unpublished Sources

Here, statistical inquiry means an investigation by any agency on a topic in which the investigator collects the relevant quantitative information. In simple terms, a statistical inquiry is a search for truth by using statistical methods of collection, compiling, analysis, interpretation, etc. The basic problem for any statistical inquiry is the collection of facts and figures related to this specific phenomenon that is being studied. Therefore, the basic purpose of data collection is collecting evidence to reach a sound and clear solution to a problem.

Table of Content

Terms Related to Data Collection

Methods of collecting data, primary data, methods of collecting primary data, secondary data, methods of collecting secondary data, 1. published sources, 2. unpublished sources.

• Data: Data is a tool that helps an investigator in understanding the problem by providing him with the information required. Data can be classified into two types; viz., Primary Data and Secondary Data.
• Investigator: An investigator is a person who conducts the statistical enquiry.
• Enumerators: In order to collect information for statistical enquiry, an investigator needs the help of some people. These people are known as enumerators.
• Respondents: A respondent is a person from whom the statistical information required for the enquiry is collected.
• Survey: I t is a method of collecting information from individuals. The basic purpose of a survey is to collect data to describe different characteristics such as usefulness, quality, price, kindness, etc. It involves asking questions about a product or service from a large number of people.

Also Read: Sources of Data Collection | Primary and Secondary Sources

There are two different methods of collecting data: Primary Data Collection and Secondary Data Collection. 

Primary data refers to information collected directly from first-hand sources specifically for a particular research purpose. This type of data is gathered through various methods, including surveys, interviews, experiments, observations, and focus groups. One of the main advantages of primary data is that it provides current, relevant, and specific information tailored to the researcher’s needs, offering a high level of accuracy and control over data quality.

There are a number of methods of collecting primary data, Some of the common methods are as follows:

1. Interviews: Collect data through direct, one-on-one conversations with individuals. The investigator asks questions either directly from the source or from its indirect links.

• Direct Personal Investigation: T he method of direct personal investigation involves collecting data personally from the source of origin. In simple words, the investigator makes direct contact with the person from whom he/she wants to obtain information. For example, direct contact with the household women to obtain information about their daily routine and schedule.
• Indirect Oral Investigation: In the indirect oral investigation method of collecting primary data, the investigator does not make direct contact with the person from whom he/she needs information, instead they collect the data orally from some other person who has the necessary required information. For example, collecting data of employees from their superiors or managers.
• Advantage: Provides real-time, natural data; no reliance on self-reported information.
• Disadvantage: Observer bias; limited to what can be seen; may influence subjects’ behavior.
• Suitable Use Case: Behavioral studies, user experience research.

2. Questionnaires: Collect data by asking people a set of questions, either online, on paper, or face-to-face. In this method the investigator prepares a questionnaire to collect Information through Questionnaires and Schedules , while keeping in mind the motive of the study, . The investigator can collect data through the questionnaire in two ways:

• Mailing Method: This method involves mailing the questionnaires to the informants for the collection of data. The investigator attaches a letter with the questionnaire in the mail to define the purpose of the study or research.
• Enumerator’s Method: This method involves the preparation of a questionnaire according to the purpose of the study or research. However, in this case, the enumerator reaches out to the informants himself with the prepared questionnaire.
• Advantage: Can reach a large audience quickly and cost-effectively.
• Disadvantage: Responses may be biased or inaccurate; low response rates.
• Suitable Use Case: Customer satisfaction surveys, market research.

3. Observations: The observation method involves collecting data by watching and recording behaviors, events, or conditions as they naturally occur. The observer systematically watches and notes specific aspects of a subject’s behavior or the environment, either covertly or overtly.

• Advantage: Provides real-time, authentic data without reliance on self-reported information.
• Disadvantage: Observer bias can influence the results, and the presence of an observer might alter subjects’ behavior.
• Suitable Use Case: Studying user interactions with a product in a natural setting, monitoring wildlife behavior, or assessing classroom dynamics.

4. Experiments: The experiment method involves manipulating one or more variables to determine their effect on another variable, within a controlled environment. Researchers create two groups (control and experimental), apply the treatment or variable to the experimental group, and compare the outcomes between the groups.

• Advantage: Allows for the establishment of cause-and-effect relationships with high precision.
• Disadvantage: Experiments can be artificial, limiting the ability to generalize findings to real-world settings, and they can be resource-intensive.
• Suitable Use Case: Testing the efficacy of a new drug, assessing the impact of a new teaching method, or evaluating the effect of a marketing campaign.

5. Focus Group: The focus group method involves gathering a small group of people to discuss a specific topic or product, facilitated by a moderator. A group of 6-12 participants engages in a guided discussion led by a moderator who asks open-ended questions to elicit opinions, attitudes, and perceptions.

• Advantage: Provides in-depth insights and diverse perspectives through interactive discussions, revealing the reasoning behind participants’ thoughts and feelings.
• Disadvantage: Results can be influenced by dominant participants or groupthink, and the findings are not easily generalizable due to the small, non-representative sample size.
• Suitable Use Case: Exploring customer attitudes towards a new product, gathering feedback on a marketing campaign, or understanding public opinion on social issues.

6. Information from Local Sources or Correspondents : In this method, for the collection of data, the investigator appoints correspondents or local persons at various places, which are then furnished by them to the investigator. With the help of correspondents and local persons, the investigators can cover a wide area.

Qualities of a Good Questionnaire and Types of Questionnaires Difference between Questionnaire and Schedule

Secondary data refers to information that has already been collected, processed, and published by others . This type of data can be sourced from existing research papers, government reports, books, statistical databases, and company records. The advantage of secondary data is that it is readily available and often free or less expensive to obtain compared to primary data. It saves time and resources since the data collection phase has already been completed.

Secondary data can be collected through different published and unpublished sources. Some of them are as follows:

• Government Publications: Government publishes different documents which consists of different varieties of information or data published by the Ministries, Central and State Governments in India as their routine activity. As the government publishes these Statistics, they are fairly reliable to the investigator. Examples of Government publications on Statistics are the Annual Survey of Industries, Statistical Abstract of India, etc.
• Semi-Government Publications: Different Semi-Government bodies also publish data related to health, education, deaths and births. These kinds of data are also reliable and used by different informants. Some examples of semi-government bodies are Metropolitan Councils, Municipalities, etc.
• Publications of Trade Associations: Various big trade associations collect and publish data from their research and statistical divisions of different trading activities and their aspects. For example , data published by Sugar Mills Association regarding different sugar mills in India.
• Journals and Papers: Different newspapers and magazines provide a variety of statistical data in their writings, which are used by different investigators for their studies.
• International Publications: Different international organizations like IMF , UNO , ILO, World Bank, etc., publish a variety of statistical information which are used as secondary data.
• Publications of Research Institutions: Research institutions and universities also publish their research activities and their findings, which are used by different investigators as secondary data. For example National Council of Applied Economics, the Indian Statistical Institute, etc.

Read More: Published Sources of Collecting Secondary Data

Unpublished sources are another source of collecting secondary data. The data in unpublished sources is collected by different government organizations and other organizations . These organizations usually collect data for their self-use and are not published anywhere . For example, research work done by professors, professionals, teachers and records maintained by business and private enterprises . 

The table below shows the production of rice in India.

The above table contains the production of rice in India in different years. It can be seen that these values vary from one year to another. Therefore, they are known as variable . A variable is a quantity or attribute, the value of which varies from one investigation to another. In general, the variables are represented by letters such as X, Y, or Z. In the above example, years are represented by variable X, and the production of rice is represented by variable Y. The values of variable X and variable Y are data from which an investigator and enumerator collect information regarding the trends of rice production in India. 

Data collection is the backbone of any research or statistical investigation, providing the necessary information to make informed decisions, identify trends, and measure progress . By understanding the various methods of data collection —such as direct personal investigation, indirect oral investigation, questionnaires, observations, experiments, and focus groups—researchers can choose the most suitable approach to gather primary data that is current, relevant, and accurate. Similarly, using secondary data from published and unpublished sources like government reports, trade associations, and research institutions can save time and resources while offering valuable insights. Mastering these data collection techniques ensures the reliability and validity of the research, ultimately leading to sound and actionable conclusions.

Methods of Data Collection-FAQs

Why is data collection important in economics.

Data collection is crucial in economics because it provides the empirical foundation for analyzing economic phenomena, testing theories, forecasting trends, and informing policy decisions. Accurate data collection ensures the reliability and validity of economic analyses.

What are surveys and questionnaires, and how are they used?

Surveys and questionnaires are tools used to collect data from a large number of respondents. They contain a series of questions designed to gather information on specific topics. Surveys can be conducted online, by phone, by mail, or in person. They are widely used in economics to collect data on consumer behavior, market trends, and economic conditions.

How are interviews conducted, and what types are there?

Interviews involve direct interaction between the interviewer and the respondent to gather detailed information. Types of interviews include: Structured Interviews: Follow a fixed set of questions. Unstructured Interviews: Open-ended, allowing for in-depth exploration. Semi-Structured Interviews: Combination of structured and unstructured formats. Interviews are useful for obtaining qualitative data and understanding complex economic issues.

How do you choose the appropriate data collection method for a study?

Choosing a data collection method depends on several factors: Research Objectives: Define the goals and questions of the study. Nature of Data Required: Determine whether quantitative or qualitative data is needed. Resources Available: Consider budget, time, and personnel constraints. Population and Sample: Assess the accessibility and characteristics of the target population. Ethical Considerations: Ensure ethical standards are met in data collection.

What are the ethical considerations in data collection?

Ethical considerations include obtaining informed consent from participants, ensuring confidentiality and privacy, avoiding harm to respondents, and maintaining data integrity. Ethical practices are essential for the credibility and validity of the research.

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Chapter 2: Collection of Data

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Chapter 3: Organisation of Data

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Chapter 4: Presentation of Data: Textual and Tabular

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Chapter 5: Diagrammatic Presentation of Data

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Chapter 6: Measures of Central Tendency: Arithmetic Mean

• Measures of Central Tendency in Statistics Central Tendencies in Statistics are the numerical values that are used to represent mid-value or central value a large collection of numerical data. These obtained numerical values are called central or average values in Statistics. A central or average value of any statistical data or series is th 10 min read
• Arithmetic Mean: Meaning, Example, Types, Merits, and Demerits A single value used to symbolise a whole set of data is called the Measure of Central Tendency. In comparison to other values, it is a typical value to which the majority of observations are closer. The arithmetic mean is one approach to measure central tendency in statistics. This measure of centra 8 min read
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Chapter 7: Measures of Central Tendency: Median and Mode

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• Calculation of Median in Continuous Series | Formula of Median The median is a measure of central tendency that represents the middle value in a dataset, dividing it into two equal halves. In a continuous series, data is grouped into class intervals, and the median is calculated differently than in a discrete series. The median provides valuable insights into t 6 min read
• Graphical determination of Median A measure of central tendency that determines the centrally located value of a given series is known as the Median. The number of values of the series below and above the given series is always equal. To determine the median value of a given series, it is first managed in increasing or decreasing or 5 min read
• Mode: Meaning, Formula, Merits, Demerits, and Examples The word mode is derived from the French word 'La Mode', meaning anything that is in fashion or vogue. A measure of central tendency in statistical series that determines the value occurring most frequently in a given series is known as mode. In other words, the modal value of the series has the hig 9 min read
• Calculation of Mode in Individual Series | Formula of Mode The word mode comes from the Latin word ‘Modus’, meaning measurements, quantity, way, or manner. In statistics, Mode refers to the variable that occurs most of the time or repeats itself most frequently in a given series of variables (say X). It is a maximum occurrence at a particular point or a val 5 min read
• Calculation of Mode in Discrete Series | Formula of Mode The word mode comes from the Latin word 'Modus', meaning measurements, quantity, way, or manner. In statistics, Mode refers to the variable that occurs most of the time or repeats itself most frequently in a given series of variables (say X). It is a maximum occurrence at a particular point or a val 6 min read
• Grouping Method of Calculating Mode in Discrete Series | Formula of Mode Mode is the value from a data set that has occurred the most number of times. It is one of the most crucial measures of central tendency and is represented by Z. For example, if, in a class of 35 students, 10 students are 10 years old, 20 students are 11 years old, and the rest 5 students are 9 year 5 min read
• Calculation of Mode in Continuous Series | Formula of Mode Mode, in statistics, refers to the variable that occurs most of the time in the given series. In simple words, a mode is a variable that repeats itself most frequently in a given series of variables (say X). We can determine the mode in two series; viz., individual and discrete series. Mode is denot 7 min read
• Calculation of Mode in Special Cases The word mode is derived from the French word ‘La Mode’, meaning anything that is in fashion or vogue. A measure of central tendency in statistical series that determines the value occurring most frequently in the given series is known as mode. In other words, the modal value of the series has the h 6 min read
• Calculation of Mode by Graphical Method The word mode is derived from the French word 'La Mode', meaning anything that is in fashion or vogue. A measure of central tendency in statistical series that determines the value occurring most frequently in the given series is known as mode. In other words, the modal value of the series has the h 4 min read
• Mean, Median and Mode| Comparison, Relationship and Calculation A single value used to symbolise a whole set of data is called the Measure of Central Tendency. In comparison to other values, it is a typical value to which the majority of observations are closer. Average and Measure of Location are other names for the Measure of Central Tendency. In statistical a 7 min read

Chapter 8: Measures of Dispersion

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• Range | Meaning, Coefficient of Range, Merits and Demerits, Calculation of Range What is Range?Range is the easiest to understand of all the measures of dispersion. The difference between the largest and smallest item in a distribution is called range. It can be written as: Range (R) = Largest item (L) – Smallest item (S) For example, If the marks of 5 students of class Xth are 4 min read
• Calculation of Range and Coefficient of Range What is Range?Range is the easiest to understand of all the measures of dispersion. The difference between the largest and smallest item in a distribution is called range. It can be written as: Range (R) = Largest item (L) – Smallest item (S) For example, If the marks of 5 students of class XIth are 5 min read
• Interquartile Range and Quartile Deviation The extent to which the values of a distribution differ from the average of that distribution is known as Dispersion. The measures of dispersion can be either absolute or relative. The Measures of Absolute Dispersion consist of Range, Quartile Deviation, Mean Deviation, Standard Deviation, and Loren 3 min read
• Partition Value | Quartiles, Deciles and Percentiles Partition values are statistical measures that divide a dataset into equal parts. They help in understanding the distribution and spread of data by indicating where certain percentages of the data fall. The most commonly used partition values are quartiles, deciles, and percentiles. Table of Content 9 min read
• Quartile Deviation and Coefficient of Quartile Deviation: Meaning, Formula, Calculation, and Examples The extent to which the values of a distribution differ from the average of that distribution is known as Dispersion. The measures of dispersion can be either absolute or relative. The Measures of Absolute Dispersion consist of Range, Quartile Deviation, Mean Deviation, Standard Deviation, and Loren 3 min read
• Quartile Deviation in Discrete Series | Formula, Calculation and Examples What is Quartile Deviation?Quartile Deviation (absolute measure) divides the distribution into multiple quarters. Quartile Deviation is calculated as the average of the difference of the upper quartile (Q3) and the lower quartile (Q1). [Tex]Quartile~Deviation=\frac{Q_3-Q_1}{2} [/Tex] Where, Q3 = Upp 2 min read
• Quartile Deviation in Continuous Series | Formula, Calculation and Examples What is Quartile Deviation?Quartile Deviation (absolute measure) divides the distribution into multiple quarters. Quartile Deviation is calculated as the average of the difference of the upper quartile (Q3) and the lower quartile (Q1). [Tex]Quartile~Deviation=\frac{Q_3-Q_1}{2} [/Tex] Where, Q3 = Upp 3 min read
• Mean Deviation: Coefficient of Mean Deviation, Merits, and Demerits Range, Interquartile range, and Quartile deviation all have the same defect; i.e., they are determined by considering only two values of a series: either the extreme values (as in range) or the values of the quartiles (as in quartile deviation). This approach of analysing dispersion by determining t 5 min read
• Calculation of Mean Deviation for different types of Statistical Series What is Mean Deviation?The arithmetic average of the deviations of various items from a measure of central tendency (mean, median, or mode) is known as the Mean Deviation of a series. Other names for Mean Deviation are the First Moment of Dispersion and Average Deviation. Mean deviation is calculate 3 min read
• Mean Deviation from Mean | Individual, Discrete, and Continuous Series Mean Deviation of a series can be defined as the arithmetic average of the deviations of various items from a measure of central tendency (mean, median, or mode). Mean Deviation is also known as the First Moment of Dispersion or Average Deviation. Mean Deviation is based on all the items of the seri 4 min read
• Mean Deviation from Median | Individual, Discrete, and Continuous Series What is Mean Deviation from Median?Mean Deviation of a series can be defined as the arithmetic average of the deviations of various items from a measure of central tendency (mean, median, or mode). Mean Deviation is also known as the First Moment of Dispersion or Average Deviation. Mean Deviation is 5 min read
• Standard Deviation: Meaning, Coefficient of Standard Deviation, Merits, and Demerits The methods of measuring dispersion such as quartile deviation, range, mean deviation, etc., are not universally adopted as they do not provide much accuracy. Range does not provide required satisfaction as in the entire group, range's magnitude is determined by most extreme cases. Quartile Deviatio 6 min read
• Standard Deviation in Individual Series A scientific measure of dispersion that is widely used in statistical analysis of a given set of data is known as Standard Deviation. Another name for standard deviation is Root Mean Square Deviation. Standard Deviation is denoted by a Greek Symbol σ (sigma). Under this method, the deviation of valu 3 min read
• Standard Deviation in Discrete Series A scientific measure of dispersion that is widely used in statistical analysis of a given set of data is known as Standard Deviation. Another name for standard deviation is Root Mean Square Deviation. Standard Deviation is denoted by a Greek Symbol σ (sigma). Under this method, the deviation of valu 5 min read
• Standard Deviation in Frequency Distribution Series A scientific measure of dispersion that is widely used in statistical analysis of a given set of data is known as Standard Deviation. Another name for standard deviation is Root Mean Square Deviation. It is denoted by a Greek Symbol σ (sigma). Under this method, the deviation of values is taken from 4 min read
• Combined Standard Deviation: Meaning, Formula, and Example A scientific measure of dispersion, which is widely used in statistical analysis of a given set of data is known as Standard Deviation. Another name for standard deviation is Root Mean Square Deviation. Standard Deviation is denoted by a Greek Symbol σ (sigma). Under this method, the deviation of va 2 min read
• How to calculate Variance? What is Variance?Variance is another measure of dispersion and is based on standard deviation. The term variance was first used by R.A. Fisher in 1913 and means the square of the standard deviation of the given distribution. Symbolically, Variance is denoted by σ2. Variance = σ2 [Tex]Standard~Deviat 1 min read
• Coefficient of Variation: Meaning, Formula and Examples What is Coefficient of Variation? As Standard Deviation is an absolute measure of dispersion, one cannot use it for comparing the variability of two or more series when they are expressed in different units. Therefore, in order to compare the variability of two or more series with different units it 2 min read
• Lorenz Curveb : Meaning, Construction, and Application What is Lorenz Curve?The variability of a statistical series can be measured through different measures, Lorenz Curve is one of them. It is a Cumulative Percentage Curve and was first used by Max Lorenz. Generally, Lorenz Curves are used to measure the variability of the distribution of income and w 4 min read

Chapter 9: Correlation

• Correlation: Meaning, Significance, Types and Degree of Correlation The previous statistical approaches (such as central tendency and dispersion) are limited to analysing a single variable or statistical analysis. This type of statistical analysis in which one variable is involved is known as Univariate Distribution. However, there are instances in real-world situat 9 min read
• Methods of Measurements of Correlation What is Correlation?A statistical tool that helps in the study of the relationship between two variables is known as Correlation. It also helps in understanding the economic behaviour of the variables. However, correlation does not tell anything about the cause-and-effect relationship between the tw 4 min read
• Scatter Diagram Correlation | Meaning, Interpretation, Example What is a Scatter Diagram?A simple and attractive method of measuring correlation by diagrammatically representing bivariate distribution for determination of the nature of the correlation between the variables is known as the Scatter Diagram Method. This method gives the investigator/analyst a visu 6 min read
• Spearman's Rank Correlation Coefficient in Statistics Spearman's Rank Correlation Coefficient or Spearman's Rank Difference Method or Formula is a method of calculating the correlation coefficient of qualitative variables and was developed in 1904 by Charles Edward Spearman. In other words, the formula determines the correlation coefficient of variable 6 min read
• Karl Pearson's Coefficient of Correlation | Assumptions, Merits and Demerits What is Karl Pearson's Coefficient of Correlation?The first person to give a mathematical formula for the measurement of the degree of relationship between two variables in 1890 was Karl Pearson. Karl Pearson's Coefficient of Correlation is also known as Product Moment Correlation or Simple Correlat 9 min read
• Karl Pearson's Coefficient of Correlation | Methods and Examples What is Karl Pearson's Coefficient of Correlation?The first person to give a mathematical formula for the measurement of the degree of relationship between two variables in 1890 was Karl Pearson. Karl Pearson's Coefficient of Correlation is also known as Product Moment Correlation or Simple Correlat 6 min read

Chapter 10: Index Number

• Index Number | Meaning, Characteristics, Uses and Limitations What is Index Number?We are a part of a fast-paced economy. Numerous changes in the size of the population, output, money supply, income, and price of commodities are taking place continuously in an ever-changing environment. Economic changes have their effects on the volume of economic activity, in 8 min read
• Methods of Construction of Index Number What is Index Number? A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base ye 5 min read
• Unweighted or Simple Index Numbers: Meaning and Methods A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 5 min read
• Methods of calculating Weighted Index Numbers A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 4 min read
• Fisher's Index Number as an Ideal Method A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 3 min read
• Fisher's Method of calculating Weighted Index Number A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 2 min read
• Paasche's Method of calculating Weighted Index Number A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 2 min read
• Laspeyre's Method of calculating Weighted Index Number A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 2 min read
• Laspeyre's, Paasche's, and Fisher's Methods of Calculating Index Number A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 3 min read
• Consumer Price Index (CPI) or Cost of Living Index Number: Construction of Consumer Price Index|Difficulties and Uses of Consumer Price Index A statistical measure that helps in finding out the percentage change in the values of different variables, such as the price of different goods, production of different goods, etc., over time is known as the Index Number. The percentage change is determined by taking a base year as a reference. Thi 9 min read
• Methods of Constructing Consumer Price Index (CPI) The index reflecting the average increase in the cost of the commodities consumed by a class of people and helping them maintain the same standard of living in the current year as in the base year is known as Consumer Price Index (CPI). The main aim behind their design is the measurement of the effe 3 min read
• Wholesale Price Index (WPI) | Meaning, Uses, Merits, and Demerits What is Wholesale Price Index(WPI)?The two most practical and widely used metrics used to assess market inflation in a country are the Wholesale Price Index and the Consumer Price Index. The WPI concentrates on the wholesale level, whereas the CPI concept concentrates on the retail level. The Wholes 6 min read
• Index Number of Industrial Production : Characteristics, Construction & Example The index number was first constructed by an Indian Statistician, Carli in 1764. It was used for the first time to compare the prices of the year 1750 with that of the year 1500.  An index number is a statistical tool used to measure changes in the magnitude of a group of related variables. An index 3 min read
• Inflation and Index Number The index number was first constructed by an Indian Statistician, Carli in 1764. It was used for the first time to compare the prices of the year 1750 with that of the year 1500. An index number is a statistical tool for measuring changes in the magnitude of a group of related variables. An index nu 5 min read

Important Formulas in Statistics for Economics

• Important Formulas in Statistics for Economics | Class 11 Statistics for Economics is a field that helps in the study, collection, analysis, interpretation, and organization of data for different ultimate objectives. Statistics help a user in gathering and analyzing huge numerical data easily and efficiently. For this, it provides various statistical tools 15 min read
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Data Collection by Experiments

Previous video 1.8: data collection by observations, next video 1.10: data collection by survey, 21,501 views.

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”

An example of the experimental method is a public clinical trial of a drug. For instance, to test the efficacy of a new drug effective in treating blood pressure, one needs to perform an experimental data collection. The new drug is given to a small number of randomly selected volunteers who suffer from chronic high blood pressure. One group of subjects is treated with specific doses of drugs or treatment methods, and a control group may be given a placebo. The subjects are monitored for a few weeks. The symptoms of disease treatment and after-effects of the drug are observed, and the data is collected. As this process involves modifying the subjects, it is categorized under the experimental method.

Another example is studying the effect of a particular fertilizer on the plant's growth. For this purpose, a few plants are taken and subjected to treatment with the new fertilizer. The growth of the plants is monitored daily for a few weeks, and the data is collected.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental design is a structured approach used to conduct scientific experiments. It enables researchers to explore cause-and-effect relationships by controlling variables and testing hypotheses. This guide explores the types of experimental designs, common methods, and best practices for planning and conducting experiments.

Experimental Design

Experimental design refers to the process of planning a study to test a hypothesis, where variables are manipulated to observe their effects on outcomes. By carefully controlling conditions, researchers can determine whether specific factors cause changes in a dependent variable.

Key Characteristics of Experimental Design :

• Manipulation of Variables : The researcher intentionally changes one or more independent variables.
• Control of Extraneous Factors : Other variables are kept constant to avoid interference.
• Randomization : Subjects are often randomly assigned to groups to reduce bias.
• Replication : Repeating the experiment or having multiple subjects helps verify results.

Purpose of Experimental Design

The primary purpose of experimental design is to establish causal relationships by controlling for extraneous factors and reducing bias. Experimental designs help:

• Test Hypotheses : Determine if there is a significant effect of independent variables on dependent variables.
• Control Confounding Variables : Minimize the impact of variables that could distort results.
• Generate Reproducible Results : Provide a structured approach that allows other researchers to replicate findings.

Types of Experimental Designs

Experimental designs can vary based on the number of variables, the assignment of participants, and the purpose of the experiment. Here are some common types:

1. Pre-Experimental Designs

These designs are exploratory and lack random assignment, often used when strict control is not feasible. They provide initial insights but are less rigorous in establishing causality.

• Example : A training program is provided, and participants’ knowledge is tested afterward, without a pretest.
• Example : A group is tested on reading skills, receives instruction, and is tested again to measure improvement.

2. True Experimental Designs

True experiments involve random assignment of participants to control or experimental groups, providing high levels of control over variables.

• Example : A new drug’s efficacy is tested with patients randomly assigned to receive the drug or a placebo.
• Example : Two groups are observed after one group receives a treatment, and the other receives no intervention.

3. Quasi-Experimental Designs

Quasi-experiments lack random assignment but still aim to determine causality by comparing groups or time periods. They are often used when randomization isn’t possible, such as in natural or field experiments.

• Example : Schools receive different curriculums, and students’ test scores are compared before and after implementation.
• Example : Traffic accident rates are recorded for a city before and after a new speed limit is enforced.

4. Factorial Designs

Factorial designs test the effects of multiple independent variables simultaneously. This design is useful for studying the interactions between variables.

• Example : Studying how caffeine (variable 1) and sleep deprivation (variable 2) affect memory performance.
• Example : An experiment studying the impact of age, gender, and education level on technology usage.

5. Repeated Measures Design

In repeated measures designs, the same participants are exposed to different conditions or treatments. This design is valuable for studying changes within subjects over time.

• Example : Measuring reaction time in participants before, during, and after caffeine consumption.
• Example : Testing two medications, with each participant receiving both but in a different sequence.

Methods for Implementing Experimental Designs

• Purpose : Ensures each participant has an equal chance of being assigned to any group, reducing selection bias.
• Method : Use random number generators or assignment software to allocate participants randomly.
• Purpose : Prevents participants or researchers from knowing which group (experimental or control) participants belong to, reducing bias.
• Method : Implement single-blind (participants unaware) or double-blind (both participants and researchers unaware) procedures.
• Purpose : Provides a baseline for comparison, showing what would happen without the intervention.
• Method : Include a group that does not receive the treatment but otherwise undergoes the same conditions.
• Purpose : Controls for order effects in repeated measures designs by varying the order of treatments.
• Method : Assign different sequences to participants, ensuring that each condition appears equally across orders.
• Purpose : Ensures reliability by repeating the experiment or including multiple participants within groups.
• Method : Increase sample size or repeat studies with different samples or in different settings.

Steps to Conduct an Experimental Design

• Clearly state what you intend to discover or prove through the experiment. A strong hypothesis guides the experiment’s design and variable selection.
• Independent Variable (IV) : The factor manipulated by the researcher (e.g., amount of sleep).
• Dependent Variable (DV) : The outcome measured (e.g., reaction time).
• Control Variables : Factors kept constant to prevent interference with results (e.g., time of day for testing).
• Choose a design type that aligns with your research question, hypothesis, and available resources. For example, an RCT for a medical study or a factorial design for complex interactions.
• Randomly assign participants to experimental or control groups. Ensure control groups are similar to experimental groups in all respects except for the treatment received.
• Randomize the assignment and, if possible, apply blinding to minimize potential bias.
• Follow a consistent procedure for each group, collecting data systematically. Record observations and manage any unexpected events or variables that may arise.
• Use appropriate statistical methods to test for significant differences between groups, such as t-tests, ANOVA, or regression analysis.
• Determine whether the results support your hypothesis and analyze any trends, patterns, or unexpected findings. Discuss possible limitations and implications of your results.

Examples of Experimental Design in Research

• Medicine : Testing a new drug’s effectiveness through a randomized controlled trial, where one group receives the drug and another receives a placebo.
• Psychology : Studying the effect of sleep deprivation on memory using a within-subject design, where participants are tested with different sleep conditions.
• Education : Comparing teaching methods in a quasi-experimental design by measuring students’ performance before and after implementing a new curriculum.
• Marketing : Using a factorial design to examine the effects of advertisement type and frequency on consumer purchase behavior.
• Environmental Science : Testing the impact of a pollution reduction policy through a time series design, recording pollution levels before and after implementation.

Experimental design is fundamental to conducting rigorous and reliable research, offering a systematic approach to exploring causal relationships. With various types of designs and methods, researchers can choose the most appropriate setup to answer their research questions effectively. By applying best practices, controlling variables, and selecting suitable statistical methods, experimental design supports meaningful insights across scientific, medical, and social research fields.

• Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research . Houghton Mifflin Company.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference . Houghton Mifflin.
• Fisher, R. A. (1935). The Design of Experiments . Oliver and Boyd.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics . Sage Publications.
• Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences . Routledge.

About the author

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Researcher, Academic Writer, Web developer

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AP® Statistics

Data collection methods: what to know for ap® statistics.

• The Albert Team
• Last Updated On: March 1, 2022

Introduction

When faced with a research problem, you need to collect, analyze and interpret data to answer your research questions. Examples of research questions that could require you to gather data include how many people will vote for a candidate, what is the best product mix to use and how useful is a drug in curing a disease. The research problem you explore informs the type of data you’ll collect and the data collection method you’ll use. In this article, we will explore various types of data, methods of data collection and advantages and disadvantages of each. After reading our review, you will have an excellent understanding of when to use each of the data collection methods we discuss.

Types of Data

Quantitative Data

Data that is expressed in numbers and summarized using statistics to give meaningful information is referred to as quantitative data . Examples of quantitative data we could collect are heights, weights, or ages of students. If we obtain the mean of each set of measurements, we have meaningful information about the average value for each of those student characteristics.

Qualitative Data

When we use data for description without measurement, we call it qualitative data . Examples of qualitative data are student attitudes towards school, attitudes towards exam cheating and friendliness of students to teachers. Such data cannot be easily summarized using statistics.

Primary Data

When we obtain data directly from individuals, objects or processes, we refer to it as primary data . Quantitative or qualitative data can be collected using this approach. Such data is usually collected solely for the research problem to you will study. Primary data has several advantages. First, we tailor it to our specific research question, so there are no customizations needed to make the data usable. Second, primary data is reliable because you control how the data is collected and can monitor its quality. Third, by collecting primary data, you spend your resources in collecting only required data. Finally, primary data is proprietary, so you enjoy advantages over those who cannot access the data.

Despite its advantages, primary data also has disadvantages of which you need to be aware. The first problem with primary data is that it is costlier to acquire as compared to secondary data. Obtaining primary data also requires more time as compared to gathering secondary data.

Secondary Data

When you collect data after another researcher or agency that initially gathered it makes it available, you are gathering secondary data . Examples of secondary data are census data published by the US Census Bureau, stock prices data published by CNN and salaries data published by the Bureau of Labor Statistics.

One advantage to using secondary data is that it will save you time and money, although some data sets require you to pay for access. A second advantage is the relative ease with which you can obtain it. You can easily access secondary data from publications, government agencies, data aggregation websites and blogs. A third advantage is that it eliminates effort duplication since you can identify existing data that matches your needs instead of gather new data.

Despite the benefits it offers, secondary data has its shortcomings. One limitation is that secondary data may not be complete. For it to meet your research needs, you may need to enrich it with data from other sources. A second shortcoming is that you cannot verify the accuracy of secondary data, or the data may be outdated. A third challenge you face when using secondary data is that documentation may be incomplete or missing. Therefore, you may not be aware of any problems that happened in data collection which would otherwise influence its interpretation. Another challenge you may face when you decide to use secondary data is that there may be copyright restrictions.

Now that we’ve explained the various types of data you can collect when conducting research, we will proceed to look at methods used to collect primary and secondary data.

Methods Employed in Primary Data Collection

When you decide to conduct original research, the data you gather can be quantitative or qualitative. Generally, you collect quantitative data through sample surveys, experiments and observational studies. You obtain qualitative data through focus groups, in-depth interviews and case studies. We will discuss each of these data collection methods below and examine their advantages and disadvantages.

Sample Surveys

A survey is a data collection method where you select a sample of respondents from a large population in order to gather information about that population. The process of identifying individuals from the population who you will interview is known as sampling .

To gather data through a survey, you construct a questionnaire to prompt information from selected respondents. When creating a questionnaire, you should keep in mind several key considerations. First, make sure the questions and choices are unambiguous. Second, make sure the questionnaire will be completed within a reasonable amount of time. Finally, make sure there are no typographical errors. To check if there are any problems with your questionnaire, use it to interview a few people before administering it to all respondents in your sample. We refer to this process as pretesting.

Using a survey to collect data offers you several advantages. The main benefit is time and cost savings because you only interview a sample, not the large population. Another benefit is that when you select your sample correctly, you will obtain information of acceptable accuracy. Additionally, surveys are adaptable and can be used to collect data for governments, health care institutions, businesses and any other environment where data is needed.

A major shortcoming of surveys occurs when you fail to select a sample correctly; without an appropriate sample, the results will not accurately generalize the population.

Ways of Interviewing Respondents

Once you have selected your sample and developed your questionnaire, there are several ways you can interview participants. Each approach has its advantages and disadvantages.

In-person Interviewing

When you use this method, you meet with the respondents face to face and ask questions. In-person interviewing offers several advantages. This technique has excellent response rates and enables you to conduct interviews that take a longer amount of time. Another benefit is you can ask follow-up questions to responses that are not clear.

In-person interviews do have disadvantages of which you need to be aware. First, this method is expensive and takes more time because of interviewer training, transport, and remuneration. A second disadvantage is that some areas of a population, such as neighborhoods prone to crime, cannot be accessed which may result in bias.

Telephone Interviewing

Using this technique, you call respondents over the phone and interview them. This method offers the advantage of quickly collecting data, especially when used with computer-assisted telephone interviewing. Another advantage is that collecting data via telephone is cheaper than in-person interviewing.

One of the main limitations with telephone interviewing it’s hard to gain the trust of respondents. Due to this reason, you may not get responses or may introduce bias. Since phone interviews are generally kept short to reduce the possibility of upsetting respondents, this method may also limit the amount of data you can collect.

Online Interviewing

With online interviewing, you send an email inviting respondents to participate in an online survey. This technique is used widely because it is a low-cost way of interviewing many respondents. Another benefit is anonymity; you can get sensitive responses that participants would not feel comfortable providing with in-person interviewing.

When you use online interviewing, you face the disadvantage of not getting a representative sample. You also cannot seek clarification on responses that are unclear.

Mailed Questionnaire

When you use this interviewing method, you send a printed questionnaire to the postal address of the respondent. The participants fill in the questionnaire and mail it back. This interviewing method gives you the advantage of obtaining information that respondents may be unwilling to give when interviewing in person.

The main limitation with mailed questionnaires is you are likely to get a low response rate. Keep in mind that inaccuracy in mailing address, delays or loss of mail could also affect the response rate. Additionally, mailed questionnaires cannot be used to interview respondents with low literacy, and you cannot seek clarifications on responses.

Focus Groups

When you use a focus group as a data collection method, you identify a group of 6 to 10 people with similar characteristics. A moderator then guides a discussion to identify attitudes and experiences of the group. The responses are captured by video recording, voice recording or writing—this is the data you will analyze to answer your research questions. Focus groups have the advantage of requiring fewer resources and time as compared to interviewing individuals. Another advantage is that you can request clarifications to unclear responses.

One disadvantage you face when using focus groups is that the sample selected may not represent the population accurately. Furthermore, dominant participants can influence the responses of others.

Observational Data Collection Methods

In an observational data collection method, you acquire data by observing any relationships that may be present in the phenomenon you are studying. There are four types of observational methods that are available to you as a researcher: cross-sectional, case-control, cohort and ecological.

In a cross-sectional study, you only collect data on observed relationships once. This method has the advantage of being cheaper and taking less time as compared to case-control and cohort. However, cross-sectional studies can miss relationships that may arise over time.

Using a case-control method, you create cases and controls and then observe them. A case has been exposed to a phenomenon of interest while a control has not. After identifying the cases and controls, you move back in time to observe how your event of interest occurs in the two groups. This is why case-control studies are referred to as retrospective. For example, suppose a medical researcher suspects a certain type of cosmetic is causing skin cancer. You recruit people who have used a cosmetic, the cases, and those who have not used the cosmetic, the controls. You request participants to remember the type of cosmetic and the frequency of its use. This method is cheaper and requires less time as compared to the cohort method. However, this approach has limitations when individuals you are observing cannot accurately recall information. We refer to this as recall bias because you rely on the ability of participants to remember information. In the cosmetic example, recall bias would occur if participants cannot accurately remember the type of cosmetic and number of times used.

In a cohort method, you follow people with similar characteristics over a period. This method is advantageous when you are collecting data on occurrences that happen over a long period. It has the disadvantage of being costly and requiring more time. It is also not suitable for occurrences that happen rarely.

The three methods we have discussed previously collect data on individuals. When you are interested in studying a population instead of individuals, you use an ecological method. For example, say you are interested in lung cancer rates in Iowa and North Dakota. You obtain number of cancer cases per 1000 people for each state from the National Cancer Institute and compare them. You can then hypothesize possible causes of differences between the two states. When you use the ecological method, you save time and money because data is already available. However the data collected may lead you to infer population relationships that do not exist.

Experiments

An experiment is a data collection method where you as a researcher change some variables and observe their effect on other variables. The variables that you manipulate are referred to as independent while the variables that change as a result of manipulation are dependent variables. Imagine a manufacturer is testing the effect of drug strength on number of bacteria in the body. The company decides to test drug strength at 10mg, 20mg and 40mg. In this example, drug strength is the independent variable while number of bacteria is the dependent variable. The drug administered is the treatment, while 10mg, 20mg and 40mg are the levels of the treatment.

The greatest advantage of using an experiment is that you can explore causal relationships that an observational study cannot. Additionally, experimental research can be adapted to different fields like medical research, agriculture, sociology, and psychology. Nevertheless, experiments have the disadvantage of being expensive and requiring a lot of time.

This article introduced you to the various types of data you can collect for research purposes. We discussed quantitative, qualitative, primary and secondary data and identified the advantages and disadvantages of each data type. We also reviewed various data collection methods and examined their benefits and drawbacks. Having read this article, you should be able to select the data collection method most appropriate for your research question. Data is the evidence that you use to solve your research problem. When you use the correct data collection method, you get the right data to solve your problem.

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12 thoughts on “data collection methods: what to know for ap® statistics”.

Great article.

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Happy to help!

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This article helps me to learn several data collection methods. Thank you!

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Grammar Review Hub

Comprehensive review of grammar skills

AP® Posters

Download updated posters summarizing the main topics and structure for each AP® exam.