Sampling methods in research methodology.pptx

Sampling methods in research methodology.pptx

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  SAMPLING METHODS IN RESEARCHMETHODOLOGY Dr. Satavisha Kundu Post Graduate Student, Department Of Conservative Dentistry And Endodontics, Manipal College Of Dental Sciences, Manipal.
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  CONTENTS 1. Sampling –Definition 2. Objectives of Sampling 3. Types of Sampling Methods 4. Comparison between Probability and Non-Probability Sampling 5. Difference between Sampling Error and Sampling Bias 6. Advantages and Limitations of Sampling 7. Applications in Dental and Medical Research 8. Overview of randomization and its types 9. External validity 10.Ethical Considerations in Sampling 11.Conclusion 12.References
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  WHAT IS SAMPLING?( DEFINITION ) Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and a cost-effective method and hence forms the basis of any research design.
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  KEY OBJECTIVES OFSAMPLING : 1.Reduce Costs and Time: Sampling allows for efficient data collection by focusing on a representative subset. 2.Improve Accuracy: Smaller, well-designed samples can lead to more accurate, focused data collection. 3.Ensure Representativeness: By carefully selecting a sample, researchers can ensure that the findings are relevant to the larger population. Muhammad Hassan, 2024
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  SAMPLING METHODS The entireset of cases from which researcher sample is drawn in called the POPULATION. Since, researchers neither have time nor the resources to analysis the entire population so they apply sampling technique to reduce the number of cases.
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  CHOOSE SAMPLING TECHNIQUE
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  PROBABILITY SAMPLING: • Inprobability sampling, every member of the population has a predetermined chance of being chosen for inclusion in the sample. • In the context of this study, the population is considered to be relatively homogeneous due to the fact that each member of the population is a potential respondent for the research • Has the greatest freedom from bias but may represent the most costly sample in terms of time and energy for a given level of sampling error (Brown, 1947).
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  TYPES OF PROBABILITYSAMPLING
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  • The simplerandom sampling means that every case of the population has an equal probability of inclusion in sample. • Example: A school administrator randomly selects 50 students from a list of all students to survey about cafeteria satisfaction. Methods of doing SRS 1. Lottery method ( chit – mix – pick ) 2. Random Number Table (Table of random nos./ computer) Types 3. With Replacement (SRSWR) 4. Without Replacement (SRSWOR) (more common) 1. SIMPLE RANDOM SAMPLING ( SRS )
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  2. SYTEMATIC SAMPLING •A starting point is randomly selected, and then every kth individual is chosen from a list. This method is often used when there’s a fixed pattern or order in the population list. • Example: A researcher wants to survey a population of 1,000 people and decides to select every 10th person on a sorted list after a random start. • The determination of sample size and calculation of sampling interval can be achieved through the use of a specific formula K = N/n represents the Kth element, where , • N = total population, n = sample size, K = interval (gap) TYPES 1. Linear Systematic Sampling (most common) 2. Circular Systematic Sampling
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  3. STRATIFIED RANDOMSAMPLING • Stratified sampling is where the population is divided into strata (or subgroups) and a random sample is taken from each subgroup. A subgroup is a natural set of items. Subgroups might be based on company size, gender or occupation (to name but a few). Stratified sampling is often used where there is a great deal of variation within a population. Its purpose is to ensure that every stratum is adequately represented (Ackoff, 1953). • Example: In a study on employee satisfaction, researchers divide employees into departments (e.g., sales, HR, finance) and randomly select employees from each department. TYPES : 1. Proportionate Stratified Sampling (most important) 2. Disproportionate Stratified Sampling
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  4. CLUSTER SAMPLING •Cluster sampling is where the whole population is divided into clusters or groups. Subsequently, a random sample is taken from these clusters, all of which are used in the final sample (Wilson, 2010). Cluster sampling is advantageous for those researchers whose subjects are fragmented over large geographical areas as it saves time and money (Davis, 2005). • Example: In a national health study, a researcher randomly selects specific cities (clusters) and surveys all residents within those cities. • MULTI-STAGE SAMPLING, also referred to as multi-stage cluster sampling, is a sophisticated variant of cluster sampling that facilitate primary data collection by dividing large clusters of population into smaller clusters through several stages. • Eg. School Survey: Select schools → select classes → select students.
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  NON- PROBABILITY SAMPLING: •In non-probability sampling, individuals are selected based on specific characteristics or convenience rather than random selection. This method is suitable for exploratory research where generalizability is less critical. • Non probability sampling is often associated with case study research design and qualitative research. With regards to the latter, case studies tend to focus on small samples and are intended to examine a real-life phenomenon, not to make statistical inferences in relation to the wider population (Yin, 2003).
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  TYPES OF NON-PROBABILITYSAMPLING
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  1. CONVENIENCE SAMPLING •Convenience sampling is selecting participants because they are often readily and easily available. Typically, convenience sampling tends to be a favored sampling technique among students as it is inexpensive, fast and an easy option compared to other sampling techniques (Ackoff, 1953). • For example, using friends or family as part of sample is easier than targeting unknown individuals. Example: A psychology student surveys classmates because they are easily accessible and available for quick data collection.
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  2. PURPOSIVE ORJUDGMENTAL SAMPLING • Purposive or judgmental sampling is a strategy in which particular settings persons or events are selected deliberately in order to provide important information that cannot be obtained from other choices (Maxwell, 1996). It is where the researcher includes cases or participants in the sample because they believe that they warrant inclusion. • Participants are selected based on specific criteria or characteristics relevant to the study’s purpose. • Example: In a study on the effects of leadership training, a researcher selects participants who hold managerial positions to gain insights specific to leaders.
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  Purposive sampling: • Improvesrelevance to research objective • Enhances data quality in qualitative studies
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  1. Both arenon-probability methods with risk of bias. 2. Convenience is used for ease (often quantitative), while purposive is used for targeted selection (mainly qualitative). 3. Choice depends on study objective.
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  3. SNOWBALL SAMPLING •Snowball sampling is a non-random sampling method that uses a few cases to help encourage other cases to take part in the study, thereby increasing sample size. This approach is most applicable in small populations that are difficult to access due to their closed nature, e.g. secret societies and inaccessible professions (Breweton and Millward, 2001). • Types : 1. Linear Snowball (A → B → C → D → E) 2. Exponential Non-Discriminative 3. Exponential Discriminative
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  QUOTA SAMPLING • Quotasampling is a non-random sampling technique in which participants are chosen on the basis of predetermined characteristics so that the total sample will have the same distribution of characteristics as the wider population (Davis, 2005). • Example: A researcher studying consumer preferences might set a quota to survey 50 men and 50 women in a shopping mall • Types 1. Controlled Quota 2. Uncontrolled Quota
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   Compares probabilityvs non-probability sampling Probability sampling methods enhance representativeness, whereas non- probability methods are more feasible but prone to bias.
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  DIFFERENCE BETWEEN SAMPLINGERROR AND SAMPLING BIAS ASPECT SAMPLING ERROR SAMPLING BIAS Definition Difference between sample result and true population value due to chance variation Systematic error caused by unfair selection of participants Cause Small sample size or random variation Faulty sampling method or unequal selection Nature Random Systematic Occurs When Sample does not perfectly represent population by chance Certain groups are overrepresented or underrepresented Can It Be Measured? Yes Difficult to measure exactly Can It Be Reduced? Yes Yes, by proper study design Main Solution Increase sample size Use proper sampling technique Example Randomly selecting unusually healthy people by chance Excluding rural population from survey Kultar Singh et al., 2022
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  TYPE EXPLANATION Self-selection bias Peoplevolunteer themselves, often having similar characteristics Coverage bias Non-participants differ from participants Undercoverage bias Some groups are poorly represented Advertising / Pre-screening bias Recruitment method influences participant selection TYPES OF SAMPLING BIAS COMMON TYPES OF SAMPLING ERRORS TYPE EXPLANATION Population specification error Wrong population is identified Sampling frame error Wrong subgroup is targeted Selection error Participants choose themselves to participate Sampling mistake Sample fails to represent population properly Kultar Singh et al., 2022
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  METHOD PURPOSE Define targetpopulation clearly Ensures correct participant selection Use proper sampling frame Makes sample closer to population Use simple questionnaires Improves response rate Follow-up non-respondents Reduces missing data bias Proper study design Minimizes systematic errors METHOD BENEFIT Increase sample size Improves representativeness Use proportional sampling Reflects population structure accurately Understand population demographics Helps proper sample selection HOW TO MINIMIZE SAMPLING ERROR HOW TO PREVENT SAMPLING BIAS Kultar Singh et al., 2022
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  Random sampling: • Equal chance →most reliable Stratified sampling: • reduces sampling error Convenience: • Cheap but not representation Random sampling allows estimation of population parameters with known confidence, unlike non- probability methods.
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  WHEN TO USEEACH SAMPLING METHOD( PROBABILITY) 1.Simple Random Sampling: Use when you need a fully representative sample, especially if the population is homogeneous and a sampling frame is available. 2.Stratified Sampling: Best when studying specific subgroups within a population, as it ensures representation across key characteristics. 3.Systematic Sampling: Suitable when you have a large population list and need a simple yet systematic approach, especially if the list has no inherent order. 4.Cluster Sampling: Useful for large, geographically dispersed populations; ideal when it’s impractical to survey individuals directly.
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  NON - PROBABILITY 1.ConvenienceSampling: Ideal for exploratory studies, pilot tests, or when time and resources are limited. 2.Quota Sampling: Use when studying demographic or categorical diversity, especially when you need specific representation within the sample. 3.Snowball Sampling: Ideal for reaching hidden, hard-to-reach, or marginalized populations. 4.Purposive Sampling: Best when studying a specific, well-defined population or a unique group that directly relates to the research question.
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  EXAMPLES OF SAMPLINGIN RESEARCH STUDIES 1.Education Study 1. Objective: Investigate student study habits across grade levels. 2. Sampling Method: Stratified sampling, where students are divided into grades (strata) and randomly sampled from each grade. 2.Healthcare Study 1. Objective: Examine patient satisfaction in a hospital network. 2. Sampling Method: Cluster sampling, where hospitals (clusters) are selected, and all patients within selected hospitals are surveyed.
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  CONTD… 3. Consumer Research 1.Objective: Understand shopping preferences among young adults. 2. Sampling Method: Convenience sampling, where young adults at a popular mall are surveyed. 4. Social Study 3. Objective: Study the experiences of refugees in a new country. 4. Sampling Method: Snowball sampling, where initial participants (refugees) refer others in their community.
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  TIPS FOR CHOOSINGTHE RIGHT SAMPLING METHOD 1.Define Your Research Goals: Clarify whether you need a representative sample or a specific target group to meet the objectives. 2.Consider Resources: Time, budget, and accessibility influence the feasibility of sampling methods. 3.Evaluate Population Characteristics: Large, diverse populations may require stratified or cluster sampling, while homogeneous populations might benefit from simple random sampling. 4.Assess Generalizability: If generalizing results to a larger population is important, prioritize probability sampling methods. 5.Address Ethical Concerns: Ensure ethical considerations for sensitive populations, especially when using snowball or purposive sampling.
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  APPLICATIONS IN DENTALRESEARCH 1. Clinical Trials (Randomized Controlled Trials) • Sampling is used to select and allocate participants into control and experimental groups. • Random sampling ensures equal chance of selection and reduces bias. • Testing new materials, drugs, techniques • Example : • Comparing bond strength of fibre posts • Testing new irrigants or sealers 2. Epidemiological Studies •Studying disease prevalence in large population •Example: • Dental caries prevalence in school children • Periodontal disease in rural vs urban populations Common Sampling used: •Stratified sampling → age groups, gender •Cluster sampling → selecting schools/villages
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  4. Rare Conditions/ Special Groups Used when the target population is specific or limited. •Example: • Oral cancer patients • TMJ disorders • Medically compromised patients Sampling used: •Purposive sampling → selecting specific cases •Snowball sampling → patient referrals 3. Hospital-Based Studies Sampling is used to select patients from outpatient or inpatient departments for clinical research. Example: Evaluating success rates of root canal treatments in a dental college setup. Convenience sampling is commonly used due to ease of access.
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  5. Questionnaire &Survey Studies Sampling is used to collect data on knowledge, attitude, and practices. •Example: • Patient satisfaction after treatment • Awareness about oral hygiene Sampling used: •Quota sampling → equal males/females •Convenience sampling 6. In-vitro Studies Sampling involves selection of extracted teeth or specimens based on specific criteria. •Example: • Testing bond strength, microleakage , fracture resistance. Sampling used: •Teeth selected based on criteria → This is basically purposive sampling
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  RANDOMIZATION 1. Randomization ensuresthat each patient has an equal chance of receiving any of the treatments under study. 2. It generates comparable intervention groups, which are alike in all important aspects except for the intervention each group receives. 3. It eliminates selection bias and balances the groups with respect to known and unknown confounding variables. 4. It also forms the basis for statistical tests used in analyzing the data. KP Suresh, 2011.
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  1. SIMPLE RANDOMIZATION •Randomizationbased on a single sequence of random assignments. •Maintains complete randomness of assignment of a subject to a particular group. •Methods include: • Flipping a coin • Shuffled deck of cards • Throwing a dice • Random number tables or computer-generated random numbers •Simple and easy to implement. •May result in unequal number of participants among groups in small sample size studies. 2. Covariate Adaptive Randomization •Recommended for small to moderate size clinical research. •A new participant is assigned by considering: • Specific covariates • Previous assignments of participants •Uses minimization method to reduce imbalance among covariates. •Helps achieve treatment balance when several prognostic factors are present TYPES OF RANDOMIZATION :
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  2. BLOCK RANDOMIZATION •Designedto randomize subjects into groups that result in equal sample sizes. •Ensures balance in sample size across groups over time. •Blocks are small and balanced with predetermined group assignments. •Block size should be a multiple of the number of groups. •Helps achieve balance, especially in small clinical trials. •Covariate imbalance may still occur between groups. 3. STRATIFIED RANDOMIZATION •Used to control and balance the influence of covariates. •Achieves balance among groups in terms of baseline characteristics. •Separate blocks are generated for each combination of covariates. •Simple randomization is then performed within each block. •Useful in smaller clinical trials. •Becomes difficult when many covariates must be controlled. KP Suresh, 2011.
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  EXTERNAL VALIDITY Definition External validityrefers to the generalization of research findings, either from a sample to a larger population or to settings and populations other than those studied. Generalization External validity is related to the “generalizability of research findings.” It asks whether findings will hold “beyond the particulars of time, place, and methodology.” Two Meanings of External Validity 1. Generalizing from a sample to a larger population 2. Generalizing across settings, times, and populations Campbell and Stanley Definition To what populations, settings, treatment variables, and measurement variables can this effect be generalized? JEFFREY W. LUCAS , 2003 .
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  Theory and ExternalValidity General knowledge is theoretical knowledge. Findings alone cannot produce general knowledge without theory. Role of Theory The key component of external validity is theory. External validity depends on the relationship between theory and methods. Experiments and External Validity Experiments are often considered lower in external validity because: • They use convenience samples • Conditions are artificial • Mostly undergraduate students are used Author’s Argument The article states that if findings do not generalize, it may indicate “a shortcoming of the theory” rather than a problem with experimental methodology. Sampling and External Validity Probability sampling may help in generalizing to a larger population, but no methodological procedures can allow for generalization across settings and populations.
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  Artificiality in Experiments Artificialsettings help researchers “eliminate variables that are not relevant” and focus only on theoretically meaningful aspects. Replication Each successful replication increases external validity. Repeated support in different settings increases confidence in theory. 5 Criteria for Assessing External Validity 1. Construct validity – measures reflect theoretical concepts 2. Relevance – study meets scope conditions 3. Reproducibility – findings repeat under same conditions 4. Consistency – findings support theory 5. Confirmatory status – theory supported in diverse tests Conclusion External validity is foremost a theoretical issue and can only be addressed by an examination of the interplay between theory and methods. JEFFREY W. LUCAS , 2003 .
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  ETHICAL CONSIDERATIONS INSAMPLING Ethical Principle Description Fair Selection of Participants No discrimination based on gender, caste, or socioeconomic status. Sample should represent the population. Avoidance of Bias Avoid selecting only “easy” or favorable cases. Use randomization to reduce bias. Informed Consent Participants must be informed about purpose, procedures, risks, and benefits before inclusion.
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  Ethical Principle Description Protectionof Vulnerable Groups Extra care for children, elderly, and medically compromised patients. Guardian consent required when needed. Privacy & Confidentiality Protect patient data and ensure no disclosure of identity. Right to Withdraw Participants can leave the study at any time without pressure or penalty. Minimizing Harm Avoid exposing participants to unnecessary risks. Ensure safety in experimental procedures. Ethical Approval Study must be approved by Institutional Ethics Committee (IEC) before starting.
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  CONCLUSION Sampling is acornerstone of research design, allowing researchers to make informed conclusions about populations through carefully selected samples. Whether using probability or non-probability sampling, understanding each method’s strengths and limitations can help researchers choose the best approach for their study. With well-chosen sampling methods, researchers can collect reliable data, make meaningful inferences, and contribute valuable insights to their fields.
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  REFERENCES • Ackoff RL.The design of social research. Chicago: University of Chicago Press; 1953. • Bartlett JE, Kotrlik JW, Higgins CC. Organizational research: determining appropriate sample size in survey research. 2001. • Brewerton P, Millward L. Organizational research methods. London: SAGE; 2001. • Brown GH. A comparison of sampling methods. J Mark. 1947;6:331–337. • Bryman A, Bell E. Business research methods. Oxford: Oxford University Press; 2003. • Davis D. Business research for decision making. Australia: Thomson South-Western; 2005. • Fowler FJ. Survey research methods. Newbury Park (CA): SAGE; 2002. • Ghauri P, Grønhaug K. Research methods in business studies. Harlow: FT/Prentice Hall; 2005. • Gill J, Johnson P, Clark M. Research methods for managers. London: SAGE; 2010. • Malhotra NK, Birks DF. Marketing research: an applied approach. Harlow: FT/Prentice Hall; 2006.
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  • Maxwell JA.Qualitative research design: an interactive approach. London: SAGE; 1996. • Wilson J. Essentials of business research: a guide to doing your research project. London: SAGE; 2010. • Yin RK. Case study research: design and methods. Newbury Park (CA): SAGE; 2003. • Zikmund WG. Business research methods. Dryden: Thomson Learning; 2002. • Creswell JW, Creswell JD. Research design: qualitative, quantitative, and mixed methods approaches. 5th ed. Thousand Oaks (CA): SAGE; 2018. • Babbie E. The practice of social research. 15th ed. Boston: Cengage Learning; 2020. • Fowler FJ. Survey research methods. 5th ed. Thousand Oaks (CA): SAGE; 2014. • Lohr SL. Sampling: design and analysis. 2nd ed. Boca Raton (FL): Chapman and Hall/CRC; 2021. • Patton MQ. Qualitative research & evaluation methods. 4th ed. Thousand Oaks (CA): SAGE; 2015. • Makwana D, Engineer P, Dabhi A, Chudasama H. Sampling methods in research: a review. Int J Trend Sci Res Dev. 2023;7(3):762. • Taherdoost H. Sampling methods in research methodology: how to choose a sampling technique for research. Int J Acad Res Manag. 2016;5(2):18–27. • Hassan M. Sampling methods: types, techniques and examples. 2024 Mar 26.
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