A sample is a subset of a population that is selected for study and analysis. The population refers to the entire group that you want to draw conclusions about, while the sample represents a smaller, manageable portion of that population.
Concept of Sample:-
Let’s say you’re a researcher interested in studying the average height of students in a particular school. The population in this case would be all the students enrolled in that school. However, it might be impractical or time-consuming to measure the height of every single student in the school.
Instead, you decide to select a sample of students to represent the entire population. You randomly select 50 students from the school’s enrollment list. These 50 students constitute your sample.
After measuring the height of each student in the sample, you calculate the average height. This average height is an estimate of the average height of all students in the school. By using a sample instead of measuring every student, you save time and resources while still obtaining valuable information about the population.
In this example:
- Population: All students enrolled in the school.
- Sample: The 50 students randomly selected for measurement.
- Inference: The average height of the sample is used to estimate the average height of the entire student population.
Definition of Sample:-
A sample is a subset or smaller group of individuals or items selected from a larger population for the purpose of studying or analyzing characteristics, behaviors, or attributes of the entire population. Samples are typically chosen in such a way that they are representative of the population from which they are drawn, allowing researchers to make inferences or draw conclusions about the population as a whole based on the characteristics observed in the sample.
In simple terms, a sample is like a small group of people or things that we look at to learn about a bigger group. For example, if we want to know how tall people are in a city, we might measure the height of a few people from different neighborhoods instead of measuring everyone. The people we measure are our “sample,” and we use their heights to guess how tall everyone else might be. So, a sample helps us understand a larger group without having to look at every single member of that group.
Definition of Sample by Authors:-
- “A sample is a subset of individuals or objects taken from a population of interest.”Author: Ken Black, “Business Statistics: For Contemporary Decision Making”
- “A sample is a subset of a population, selected for study, that is intended to represent the entire population.”Author: Paul D. Berger, “Experimental Design: An Introduction to the Design, Analysis, and Modeling of Experiments”
- “A sample is a group of subjects selected from a population of interest in such a way that the sample is representative of the population and the results obtained from the sample can be generalized to the population.”Author: Frank E. Hagan, “Research Methods in Criminal Justice and Criminology”
Sampling is the process of selecting a smaller group (sample) from a larger population to make inferences about the larger population.
Sample size refers to the number of individuals, items, or observations included in a sample selected from a larger population for research or analysis purposes.
- “Sample size is the number of observations or individuals in a sample drawn from a larger population for the purpose of statistical analysis.”
- “Sample size refers to the number of subjects or data points included in a sample for a research study or experiment. It is determined based on factors such as the desired level of statistical power, the variability of the population, and the level of precision required to detect effects of interest.”
Let’s say you want to know the favorite ice cream flavor of students in a school with 500 students. Instead of asking every student, you randomly select 50 students and ask them their favorite flavor. These 50 students make up your sample, and the number 50 represents your sample size. After collecting the data from these 50 students, you use it to estimate the favorite ice cream flavor for all 500 students in the school.
- Population: All the students in a school with 500 students.
- Sample: A subset of 50 students randomly selected from the population.
- Sample Size: 50 students (the number of individuals included in the sample).
Sampling procedure refers to the systematic method or technique used to select individuals, items, or data points from a larger population for inclusion in a sample. This process involves specific steps or rules for selecting sample members to ensure that the sample is representative of the population and suitable for conducting research or analysis. The sampling procedure is essential for obtaining reliable and valid results from the sample, as it determines how individuals or items are chosen to participate in the study or experiment.
Types of Sampling:-
- Simple Random Sampling: In this procedure, each member of the population has an equal chance of being selected for the sample. This is typically done using random number generators or randomization techniques.
- Stratified Sampling: With this procedure, the population is divided into distinct subgroups or strata based on certain characteristics (e.g., age, gender, income level). Samples are then randomly selected from each stratum in proportion to its size in the population. Stratified sampling ensures representation of different subgroups within the population.
- Cluster Sampling: In cluster sampling, the population is divided into clusters or groups, such as geographical areas, schools, or households. A random sample of clusters is then selected, and all individuals or items within the selected clusters are included in the sample. Cluster sampling is useful when it’s difficult to obtain a complete list of individuals in the population.
- Systematic Sampling: This procedure involves selecting individuals or items from a population at regular intervals, using a predetermined sampling interval. For example, every nth individual on a list may be selected. Systematic sampling is simple and convenient but may introduce bias if there is a pattern or periodicity in the population.
- Convenience Sampling: In convenience sampling, individuals or items are chosen based on their accessibility and convenience to the researcher. This method is quick and easy but may not be representative of the entire population, as it relies on the availability of subjects.
- Snowball Sampling: This procedure starts with an initial group of participants, who are then asked to refer additional participants. The process continues iteratively, with new participants referring more participants. Snowball sampling is useful when the population of interest is difficult to reach or identify, such as in studies of marginalized or hidden populations.
Types of Data:-
- Primary Data: Primary data are collected firsthand by the researcher directly from the source. This data is original and specific to the research objectives. Primary data collection methods include surveys, interviews, observations, experiments, and focus groups. Researchers gather primary data to address specific research questions or objectives.
- Secondary Data: Secondary data are collected by someone else for a purpose other than the researcher’s current project. This data has been previously collected and may include sources such as government publications, academic journals, books, market reports, and databases. Researchers utilize secondary data to complement primary data or to conduct secondary analysis for new insights. Secondary data can be less costly and time-consuming to obtain compared to primary data, but researchers should critically evaluate its quality, relevance, and reliability for their research purposes.
Methods of Data Collection:-
- Surveys: Surveys involve asking a series of questions to a sample of individuals to gather information about their attitudes, opinions, behaviors, or characteristics. Surveys can be conducted through face-to-face interviews, telephone interviews, paper-and-pencil questionnaires, online surveys, or mobile applications.
- Interviews: Interviews involve direct interaction between the researcher and the respondent, where the researcher asks questions and probes for more detailed responses.
- Observations: Observational methods involve systematically watching and recording behaviors, interactions, or events in their natural settings. Observations can be recorded using field notes, audio recordings, video recordings, or photographs.
- Experiments: Experiments involve manipulating one or more variables and observing the effects on other variables under controlled conditions. Experiments can be conducted in laboratory settings (with high control over variables) or in field settings (with greater ecological validity). Experimental methods allow researchers to establish cause-and-effect relationships between variables.
- Document Analysis: Document analysis involves examining existing documents, records, texts, or artifacts to extract relevant information for research purposes. Documents can include written documents, reports, articles, archives, government records, organizational documents, social media posts, or website content. Document analysis can provide valuable historical, contextual, or textual data.
- Focus Groups: Focus groups involve gathering a small group of individuals (typically 6-12 participants) to discuss specific topics, issues, or products in a guided group discussion facilitated by a moderator.
- Sampling: Sampling methods involve selecting a subset of individuals or items from a larger population for study. Sampling methods can include simple random sampling, stratified sampling, cluster sampling, etc.
Precaution in Collection of Data:-
- Anonymity and Confidentiality: De-identify data whenever possible and ensure participant anonymity or confidentiality, as agreed upon.
- Ethical Considerations: Be mindful of any ethical implications related to sensitive topics, biased questions, or potential harm to participants.
- Validation: Implement data validation methods to ensure accuracy and consistency (e.g., range checks, format checks).
- Error Minimization: Minimize data entry errors through clear instructions, training for data collectors, and double-checking procedures.
- Standardization: Use standardized measurement tools, procedures, and definitions to ensure data comparability.
- Quality Control: Regularly monitor data quality for missing values, outliers, and inconsistencies.
Questionaire Vs Schedule:-
|Collect information from individuals
|Collect detailed information through interaction
|Written, standardized questions
|Standardized questions with open-ended and closed-ended formats, prompts, probes
|Self-administered electronically or physically
|Conducted by a trained interviewer
|More expensive due to interviewer time and potential travel
|Wide reach over large geographic areas
|Limited to interviewer availability and participant location
|Lower; individuals may choose not to respond
|Higher; interviewer can encourage participation
|Limited; closed-ended questions or short answers
|Richer, more nuanced data through open-ended questions and follow-up
|Difficult to clarify ambiguous responses
|Interviewer can clarify and ask follow-up questions
|Higher risk of respondents misinterpreting questions
|Lower risk with interviewer clarification
|No interviewer bias
|Potential for interviewer bias in behavior or wording
Precaution in Preparation of Questionaire:-
1.Clarity and Simplicity:
- Language: Use clear, concise, and unambiguous language that your target audience understands. Avoid jargon, technical terms, and double negatives.
- Question Length: Keep questions short and to the point. Avoid complex sentence structures or compound questions.
- Formatting: Use clear formatting with logical flow. Avoid overcrowding the page with information.
2. Objectivity and Neutrality:
- Avoid leading questions: Avoid phrasing questions in a way that suggests a preferred answer.
- Avoid biased wording: Do not use language that favors one perspective over another.
- Offer balanced response options: Ensure all answer choices are neutral and cover the full range of possible responses.
3. Validity and Reliability:
- Test the questionnaire: Pilot test the questionnaire with a small group to identify any confusing or ambiguous questions.
- Ask relevant questions: Ensure the questions directly address your research objectives and collect the necessary data.
- Use appropriate question types: Choose the right question type (e.g., multiple choice, open-ended) based on the information you want to gather.
4. Ethical Considerations:
- Sensitivity: Avoid asking offensive or discriminatory questions. Respect participant privacy and comfort.
- Informed consent: Clearly explain the purpose of the questionnaire and what participation involves, so respondents can make informed decisions.
5. Additional Precautions:
- Length: Keep the questionnaire concise and avoid overwhelming participants. Consider using branching logic to shorten it based on responses.
- Order: Organize questions logically, starting with simple ones and progressing to more complex ones.
- Instructions: Provide clear instructions for completing the questionnaire and answering specific questions.
- Pilot testing: Always pilot test the questionnaire with a representative sample of your target audience to identify any issues before final distribution.