This document defines sampling and key sampling terminology. It discusses the advantages and disadvantages of sampling, including that sampling allows estimates of a larger population while saving resources, but may introduce errors. It outlines principles of sampling such as the relationship between sample size and accuracy, and how variation in a population affects differences between samples and the true population. Random sampling methods like simple random sampling and stratified random sampling are described. The goals of precision and avoiding bias in sampling are also covered.
This document discusses different methods of sampling, including probability and non-probability sampling. It provides details on specific sampling techniques such as simple random sampling, stratified sampling, cluster sampling, systematic sampling, snowball sampling, convenience sampling, and judgment sampling. The key aspects covered are how each technique selects samples and when each is most appropriate to use.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
Presentation on the characteristic of scientific research 1Junesh Acharya
The document discusses scientific research. It defines research as the systematic analysis and recording of controlled observations that can lead to generalizations and theories. Scientific research has several key characteristics: it pursues truth through logical consideration, is objective and replicable, reliable and valid, rigorous, and testable/generalizable. The research process involves realizing a problem, formulating a hypothesis, designing a study, collecting and analyzing data, and generalizing findings. Overall, scientific research uses scientific methods and tools to systematically study and explain variables in an objective, replicable manner.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
The document discusses sampling design and methods. It begins by defining key terms like population, sampling frame, and sample size. It then describes different sampling techniques including probability methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability methods such as convenience sampling, judgment sampling, quota sampling, and snowball sampling. The document provides examples and explanations of how each sampling method works. It concludes by noting some factors to consider when determining sample size.
This document defines sampling and key sampling terminology. It discusses the advantages and disadvantages of sampling, including that sampling allows estimates of a larger population while saving resources, but may introduce errors. It outlines principles of sampling such as the relationship between sample size and accuracy, and how variation in a population affects differences between samples and the true population. Random sampling methods like simple random sampling and stratified random sampling are described. The goals of precision and avoiding bias in sampling are also covered.
This document discusses different methods of sampling, including probability and non-probability sampling. It provides details on specific sampling techniques such as simple random sampling, stratified sampling, cluster sampling, systematic sampling, snowball sampling, convenience sampling, and judgment sampling. The key aspects covered are how each technique selects samples and when each is most appropriate to use.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
Presentation on the characteristic of scientific research 1Junesh Acharya
The document discusses scientific research. It defines research as the systematic analysis and recording of controlled observations that can lead to generalizations and theories. Scientific research has several key characteristics: it pursues truth through logical consideration, is objective and replicable, reliable and valid, rigorous, and testable/generalizable. The research process involves realizing a problem, formulating a hypothesis, designing a study, collecting and analyzing data, and generalizing findings. Overall, scientific research uses scientific methods and tools to systematically study and explain variables in an objective, replicable manner.
This document discusses different sampling methods used in research. It begins by defining sampling and its purposes. It then covers probability sampling methods like simple random sampling, systematic sampling, and stratified sampling. It also discusses non-probability sampling and provides examples. For each method, it describes the process, advantages, and disadvantages. The key takeaway is that sampling allows researchers to make inferences about a population while reducing time and costs compared to a census. Probability methods are preferred when possible due to their ability to estimate sampling errors.
The document discusses sampling design and methods. It begins by defining key terms like population, sampling frame, and sample size. It then describes different sampling techniques including probability methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also covers non-probability methods such as convenience sampling, judgment sampling, quota sampling, and snowball sampling. The document provides examples and explanations of how each sampling method works. It concludes by noting some factors to consider when determining sample size.
This document defines research and discusses research methodology. It states that research is a systematic and organized way to find answers to questions. It involves following a set of procedures and steps in a planned and structured manner. The document discusses different types of research including quantitative, qualitative, descriptive, comparative, and action research. It provides definitions of research and methodology, stating that methodology encompasses the methods as well as the social and philosophical context. The document emphasizes that research should be focused on problems that encourage enthusiasm and interest in order to make meaningful contributions.
The document discusses research methodology, outlining key components such as qualitative versus quantitative methods, the research onion model, and tips for postgraduate research. It provides examples of research problems and breaks down the components of the research onion model into qualitative and quantitative categories. The document emphasizes that research methodology is determined by factors like the research problem, ontology, epistemology, and choice of design/role of concepts, and that the type of data collected depends on whether a quantitative, qualitative, or mixed methods approach is used.
This document discusses research design and different types of research designs. It defines research design as the conceptual structure and plan for conducting research to answer research questions. The main types of research designs covered are exploratory, descriptive, diagnostic, and experimental. Exploratory design is used when little is known about a topic to discover variables and relationships. Descriptive design aims to describe phenomena by observing behaviors. Diagnostic design involves problem identification and finding causes. Experimental design tests hypotheses by manipulating variables and measuring outcomes. The document provides details on each design type, including their purposes and methodologies.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
The document discusses t-tests, which are used to compare means between groups. It describes the assumptions of t-tests, the different types of t-tests including independent samples t-tests and dependent samples t-tests, and the steps to conduct t-tests by hand and using SPSS. It provides examples of conducting one-sample t-tests, independent samples t-tests, and dependent samples t-tests, including interpreting the results. It also discusses how to increase statistical power by increasing the difference between means, decreasing variance, increasing sample size, and increasing the alpha level.
This document discusses sampling methods used in research. It defines sampling as obtaining information from a subset of a larger population. The key sections cover the sampling process, types of sampling including probability and non-probability methods, sources of sampling error, and factors to consider when determining sample size such as the nature of the population, number of variables, desired accuracy level, and available finances. Probability methods like simple random and stratified sampling aim to give all population members an equal chance of selection, while non-probability techniques like convenience and snowball sampling do not. Sample size is an important factor in controlling random error.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
This document provides an overview of various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It defines key terms like population, sample, mean, median, mode, and standard deviation. It explains the stages of hypothesis testing including creating the null and alternative hypotheses, determining the significance level, and deciding which statistical test to use based on the type of data and number of samples. Specific tests covered include the z-test, t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test.
This document discusses various methods for collecting primary and secondary data. It describes observation, interviews, questionnaires, and schedules as the main methods for collecting primary data. It provides details on the types, advantages, and disadvantages of each method. It also discusses other techniques like surveys, audits, and panels. For secondary data, it notes that this involves using already available data from sources like governments, organizations, and past research. The key methods are summarized in 3 sentences or less.
This document discusses various sampling techniques used in survey research. It covers probability sampling methods like simple random sampling, systematic random sampling, stratified sampling, cluster sampling, multistage sampling, and probability proportionate to size sampling. It also discusses non-probability sampling techniques like purposive sampling, snowball sampling, deviant case sampling, sequential sampling, and theoretical sampling. The document contrasts quantitative and qualitative survey methods and covers concepts like sampling frames, population parameters, point and interval estimation, and determining minimum sample sizes.
The document discusses various data collection methods used in marketing research, including questionnaires, surveys, interviews, and observation. It provides classifications of data collection methods based on the method of communication (personal interview, telephone interview, mail survey) and based on the structure and disguising of questions (structured-nondisguised, non-structured-nondisguised, etc.). Specific techniques like focus groups and depth interviews are also summarized. The advantages and limitations of different data collection methods are presented.
This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.
This document provides an overview of different types of research designs, including exploratory, descriptive, diagnostic, and hypothesis-testing designs. It defines what a research design is and lists key features of a good research design such as minimizing bias. For each type of design, it provides a brief definition and highlights important aspects to consider, such as the objective, data collection methods, sample selection, and data analysis. The overall purpose is to introduce and compare different approaches to research design.
1. Sampling is selecting a subset of a population to make inferences about the whole population. It involves defining the population, specifying a sampling frame and sampling unit, choosing a sampling method, determining sample size, and selecting the sample.
2. There are two main types of sampling methods - probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection is unknown. Common probability methods include simple random sampling, systematic sampling, and stratified sampling. Common non-probability methods include quota sampling, snowball sampling, and convenience sampling.
3. Sources of error in sampling include sampling errors, which arise from differences between the sample and population, and non-sampling
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
This document discusses the steps involved in conducting research. It begins by defining research and outlining its purposes such as building knowledge and increasing public awareness. It then describes the basic structure of a research paper as introduction, methods, results and discussion. The next sections explain each step of conducting research in detail, including identifying the research problem, literature review, specifying the research purpose and questions, developing hypotheses, choosing an appropriate methodology, collecting and verifying data, analyzing and interpreting results. Both qualitative and quantitative research methods are discussed. The importance of verification strategies in ensuring the reliability and validity of research findings is also highlighted.
This document discusses sampling and sample size in statistics. It defines key terms like population, sample, sampling unit, sampling frame, and sampling schemes. It explains that sampling allows researchers to generalize results from a subset of the population. The main advantages of sampling are that it is less costly, takes less time, and can provide more accurate results than studying the entire population. The document also discusses different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It notes that sample size depends on several factors and must result in a truly representative sample with small errors.
This document provides an overview of sampling techniques used in research. It defines key terms like population, target population, sampling, and elements. It also describes different sampling methods like probability sampling (simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, sequential sampling) and non-probability sampling (purposive sampling, convenient sampling, consecutive sampling, quota sampling, snowball sampling). The document explains the steps involved in the sampling process and factors to consider for good sampling. It highlights the merits and demerits of different sampling methods.
THIS IS VENKATESH .E WORKING AS ASSISTANT PROFESSOR AND THIS CONTENT OF SAMPLING WILL HELP TO THE M.SC I YER NURSING AND B.SC NURSING IV YEAR STUDENTS.THIS CONTENT WAS PREPARED AND REFERRED BY MY TEACHER G.ASHA KUMARI ,ASSOCIATE PROFESSOR. I HOPE THIS WILL ENHANCE KNOWLEDGE OF STUDENTS.
This document defines research and discusses research methodology. It states that research is a systematic and organized way to find answers to questions. It involves following a set of procedures and steps in a planned and structured manner. The document discusses different types of research including quantitative, qualitative, descriptive, comparative, and action research. It provides definitions of research and methodology, stating that methodology encompasses the methods as well as the social and philosophical context. The document emphasizes that research should be focused on problems that encourage enthusiasm and interest in order to make meaningful contributions.
The document discusses research methodology, outlining key components such as qualitative versus quantitative methods, the research onion model, and tips for postgraduate research. It provides examples of research problems and breaks down the components of the research onion model into qualitative and quantitative categories. The document emphasizes that research methodology is determined by factors like the research problem, ontology, epistemology, and choice of design/role of concepts, and that the type of data collected depends on whether a quantitative, qualitative, or mixed methods approach is used.
This document discusses research design and different types of research designs. It defines research design as the conceptual structure and plan for conducting research to answer research questions. The main types of research designs covered are exploratory, descriptive, diagnostic, and experimental. Exploratory design is used when little is known about a topic to discover variables and relationships. Descriptive design aims to describe phenomena by observing behaviors. Diagnostic design involves problem identification and finding causes. Experimental design tests hypotheses by manipulating variables and measuring outcomes. The document provides details on each design type, including their purposes and methodologies.
There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling involves methods where the probability of selection of each individual is known, such as simple random sampling, systematic random sampling, stratified random sampling, and cluster random sampling. Simple random sampling involves selecting a sample that gives each individual an equal chance of being selected by identifying the population, determining sample size, listing all population members, assigning them numbers, selecting numbers at random from a table, and including individuals in the sample if their number is selected. The advantages are it is easy to conduct and requires minimum population knowledge, while disadvantages include needing all population member names and potential over or under representation.
The document discusses t-tests, which are used to compare means between groups. It describes the assumptions of t-tests, the different types of t-tests including independent samples t-tests and dependent samples t-tests, and the steps to conduct t-tests by hand and using SPSS. It provides examples of conducting one-sample t-tests, independent samples t-tests, and dependent samples t-tests, including interpreting the results. It also discusses how to increase statistical power by increasing the difference between means, decreasing variance, increasing sample size, and increasing the alpha level.
This document discusses sampling methods used in research. It defines sampling as obtaining information from a subset of a larger population. The key sections cover the sampling process, types of sampling including probability and non-probability methods, sources of sampling error, and factors to consider when determining sample size such as the nature of the population, number of variables, desired accuracy level, and available finances. Probability methods like simple random and stratified sampling aim to give all population members an equal chance of selection, while non-probability techniques like convenience and snowball sampling do not. Sample size is an important factor in controlling random error.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
This document provides an overview of various statistical tests used for hypothesis testing, including parametric and non-parametric tests. It defines key terms like population, sample, mean, median, mode, and standard deviation. It explains the stages of hypothesis testing including creating the null and alternative hypotheses, determining the significance level, and deciding which statistical test to use based on the type of data and number of samples. Specific tests covered include the z-test, t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Friedman test.
This document discusses various methods for collecting primary and secondary data. It describes observation, interviews, questionnaires, and schedules as the main methods for collecting primary data. It provides details on the types, advantages, and disadvantages of each method. It also discusses other techniques like surveys, audits, and panels. For secondary data, it notes that this involves using already available data from sources like governments, organizations, and past research. The key methods are summarized in 3 sentences or less.
This document discusses various sampling techniques used in survey research. It covers probability sampling methods like simple random sampling, systematic random sampling, stratified sampling, cluster sampling, multistage sampling, and probability proportionate to size sampling. It also discusses non-probability sampling techniques like purposive sampling, snowball sampling, deviant case sampling, sequential sampling, and theoretical sampling. The document contrasts quantitative and qualitative survey methods and covers concepts like sampling frames, population parameters, point and interval estimation, and determining minimum sample sizes.
The document discusses various data collection methods used in marketing research, including questionnaires, surveys, interviews, and observation. It provides classifications of data collection methods based on the method of communication (personal interview, telephone interview, mail survey) and based on the structure and disguising of questions (structured-nondisguised, non-structured-nondisguised, etc.). Specific techniques like focus groups and depth interviews are also summarized. The advantages and limitations of different data collection methods are presented.
This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.
This document provides an overview of different types of research designs, including exploratory, descriptive, diagnostic, and hypothesis-testing designs. It defines what a research design is and lists key features of a good research design such as minimizing bias. For each type of design, it provides a brief definition and highlights important aspects to consider, such as the objective, data collection methods, sample selection, and data analysis. The overall purpose is to introduce and compare different approaches to research design.
1. Sampling is selecting a subset of a population to make inferences about the whole population. It involves defining the population, specifying a sampling frame and sampling unit, choosing a sampling method, determining sample size, and selecting the sample.
2. There are two main types of sampling methods - probability sampling, where every unit has a known chance of selection, and non-probability sampling, where the probability of selection is unknown. Common probability methods include simple random sampling, systematic sampling, and stratified sampling. Common non-probability methods include quota sampling, snowball sampling, and convenience sampling.
3. Sources of error in sampling include sampling errors, which arise from differences between the sample and population, and non-sampling
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
This document discusses the steps involved in conducting research. It begins by defining research and outlining its purposes such as building knowledge and increasing public awareness. It then describes the basic structure of a research paper as introduction, methods, results and discussion. The next sections explain each step of conducting research in detail, including identifying the research problem, literature review, specifying the research purpose and questions, developing hypotheses, choosing an appropriate methodology, collecting and verifying data, analyzing and interpreting results. Both qualitative and quantitative research methods are discussed. The importance of verification strategies in ensuring the reliability and validity of research findings is also highlighted.
This document discusses sampling and sample size in statistics. It defines key terms like population, sample, sampling unit, sampling frame, and sampling schemes. It explains that sampling allows researchers to generalize results from a subset of the population. The main advantages of sampling are that it is less costly, takes less time, and can provide more accurate results than studying the entire population. The document also discusses different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It notes that sample size depends on several factors and must result in a truly representative sample with small errors.
This document provides an overview of sampling techniques used in research. It defines key terms like population, target population, sampling, and elements. It also describes different sampling methods like probability sampling (simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, sequential sampling) and non-probability sampling (purposive sampling, convenient sampling, consecutive sampling, quota sampling, snowball sampling). The document explains the steps involved in the sampling process and factors to consider for good sampling. It highlights the merits and demerits of different sampling methods.
THIS IS VENKATESH .E WORKING AS ASSISTANT PROFESSOR AND THIS CONTENT OF SAMPLING WILL HELP TO THE M.SC I YER NURSING AND B.SC NURSING IV YEAR STUDENTS.THIS CONTENT WAS PREPARED AND REFERRED BY MY TEACHER G.ASHA KUMARI ,ASSOCIATE PROFESSOR. I HOPE THIS WILL ENHANCE KNOWLEDGE OF STUDENTS.
This document discusses various quantitative survey methods used in research including probability and non-probability sampling. It describes survey methods like simple random sampling, stratified sampling, systematic sampling, and cluster sampling which are types of probability sampling that select participants randomly. Non-probability sampling methods like quota sampling, judgmental sampling, snowball sampling, convenience sampling, and consecutive sampling are discussed. The document also covers survey design elements like interviewer-administered questionnaires, telephone and self-administered surveys, panel studies, observation methods, and factors to consider for questionnaire design like advantages, disadvantages, and addressing non-response.
The document discusses different sampling techniques used in research. It defines sampling as selecting a representative subset of a population to make inferences about. There are two main types of sampling techniques: probability sampling and non-probability sampling. Probability sampling involves random selection so that every member of the population has an equal chance of being selected. It then describes several probability sampling techniques in detail, including simple random sampling, stratified random sampling, systematic random sampling, cluster sampling, and multi-stage sampling. For each technique it provides examples and discusses their merits and demerits.
This document discusses various sampling techniques used in research. It describes two main categories of sampling: probability sampling and non-probability sampling. Within probability sampling, it outlines simple random sampling, systematic sampling, stratified sampling, cluster sampling, and complex sampling. For each technique, it provides a brief definition and example. It also covers several types of non-probability sampling including convenience sampling, judgmental sampling, quota sampling, and snowball sampling.
This document provides an overview of sampling techniques used in research. It defines key terms like population, sample, and sampling. It discusses characteristics of good sampling like being representative and free from bias. Probability sampling techniques like simple random sampling, stratified sampling, and systematic sampling are explained. Advantages of sampling like reducing time and costs are highlighted. The document outlines the sampling process and essentials of sampling. Types of sampling and various sampling methods are also summarized.
Sampling is necessary for the researchers and nursing students....
This PPT is basically related to 4th year nursing students....
It include sampling, sample, type of population, type of sampling technique and sampling error...
Sampling is a process of selecting sample...
Sample is a representative unit of the population...
This document discusses different sampling techniques used in research. It begins by defining key terms like population, sample, sampling frame, and elements. It describes the purposes of sampling like being economical and improving data quality. It then covers probability sampling techniques like simple random sampling, stratified random sampling, systematic random sampling, and cluster sampling. The document also discusses non-probability sampling techniques like purposive sampling and convenience sampling. It provides details on how each technique is implemented and highlights their merits and demerits.
This document provides an introduction to research methodology concepts including population, sample, sampling methods, hypothesis testing, and types of errors. It defines key terms like population, sample, probability and non-probability sampling, null and alternative hypotheses. It explains probability sampling methods like simple random sampling, stratified sampling and cluster sampling. It also summarizes non-probability methods like convenience and purposive sampling. The document concludes by describing type I and type II errors and their relationship to hypothesis testing.
4. Sampling: Basic Concepts: Defining the Universe, Concepts of Statistical Population, Sample, Characteristics of a good sample. Sampling Frame, determining the sample frame, Sampling errors, Non Sampling errors, Methods to reduce the errors, Sample Size constraints, Non Response. Probability Sample: Simple Random Sample, Systematic Sample, Stratified Random Sample, Area Sampling & Cluster Sampling. Non Probability Sample: Judgment Sampling, Convenience Sampling, Purposive Sampling, Quota Sampling & Snowballing Sampling methods. Determining size of the sample: Practical considerations in sampling and sample size, (sample size determination formulae and numerical not expected)
Sampling and different ways of sampling under public opinion and survey research.Advantages and disadvantages of different sampling methods with pictures and examples.
Non – Probability Sampling (Convenience, Purposive).Vikas Kumar
This document discusses different types of sampling methods used in social science research. It defines sampling as selecting a subset of individuals from a population to estimate characteristics of the whole population. The main types discussed are probability sampling, which gives all individuals an equal chance of selection, and non-probability sampling, which does not. Specific non-probability methods explained include convenience sampling, where samples are selected based on accessibility, and purposive sampling, where the researcher selects specific samples based on characteristics relevant to the research question.
1) Sampling involves selecting a subset of a larger population to gather data from. It allows researchers to study large populations in a more efficient manner.
2) There are two main types of sampling methods - probability sampling and non-probability sampling. Probability sampling involves random selection to ensure representativeness, while non-probability sampling relies on convenience.
3) Common probability sampling methods include simple random sampling, stratified random sampling, systematic random sampling, and cluster sampling. Non-probability methods include quota sampling, convenience sampling, and purposive sampling. The document provides details on how each method is implemented.
Sampling in research methodology.........Navya Naveen
Sampling in research methodology refers to the process of selecting a subset of a larger population to gather data from. This subset, called the sample, is then used to make inferences about the entire population.
The document discusses different sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population for investigation. There are two main types of sampling: probability sampling, where every individual has an equal chance of selection, and non-probability sampling, where not every individual has an equal chance. Some examples of probability sampling techniques provided are random sampling, systematic sampling, stratified sampling, and cluster sampling. Examples of non-probability sampling include convenience sampling, purposive sampling, snowball sampling, and quota sampling. The key difference between probability and non-probability sampling is that probability sampling allows results to be generalized to the overall population, while non-probability sampling does not due to its non-random nature.
This document discusses various sampling methods used in research. It defines a population as all people or items with the same characteristics that researchers want to generalize results to. There are two main types of sampling: probability sampling, where every unit has a known chance of selection; and non-probability sampling, where the probability of selection cannot be determined. Some common probability sampling methods described include simple random sampling, stratified sampling, and cluster sampling. The document also discusses non-probability sampling techniques like convenience sampling and snowball sampling.
This document discusses sampling design and various sampling methods used in research. It defines key concepts like population, sampling frame, and sampling unit. It also describes different types of probability sampling designs including simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling methods like convenience sampling are also briefly covered. The aims and advantages of sampling are to obtain representative results in a timely and cost-effective manner while minimizing bias.
Management development aims to improve managerial skills through training. Key techniques include on-the-job methods like coaching, mentoring, and job rotation which provide learning through real experiences. Off-the-job methods like case studies, business games, and simulations recreate work challenges in a risk-free environment. The goal is to develop capabilities in areas such as decision-making, communication, and leadership for current and future managerial roles. A variety of evaluation methods provide feedback to assess training effectiveness and ensure managers can apply new skills back on the job.
The document discusses the Employees' State Insurance (ESI) scheme in India. The key points are:
- ESI provides medical and financial benefits to workers in factories and other establishments.
- It covers over 20 workers per factory. Benefits include sickness, maternity, disability and funeral benefits paid as percentages of wages.
- Medical benefits include free healthcare for workers and their families from ESI hospitals and clinics.
- The scheme is funded through contributions of 1.75% of wages by employees and 4.75% by employers.
The Employee State Insurance (ESI) scheme provides medical and financial benefits to employees in factories and establishments with 20 or more workers. It covers medical care, sickness benefits, maternity benefits, dependents' benefits, and disablement benefits. Both employers and employees contribute monthly to the ESI scheme. Benefits include free healthcare in ESI hospitals as well as cash benefits for sickness, maternity leave, employment injury, and unemployment.
This document defines compensation and its objectives, discusses factors that influence compensation, and describes the various types of direct and indirect compensation. The key objectives of compensation are to attract, retain, and motivate qualified employees. Compensation includes direct wages/salaries as well as indirect benefits and is influenced by market rates, cost of living, productivity, and other economic factors. Common types of direct compensation are salary, wages, bonuses, and commissions, while indirect compensation includes benefits like healthcare, paid time off, and retirement plans.
This document discusses different theories of motivation, including instinct approaches, drive-reduction approaches, arousal approaches, incentive approaches, and Maslow's hierarchy of needs. It addresses biological, cognitive, and social factors that energize behavior. Theories discussed include seeking to fulfill needs, maintaining an optimal level of arousal, desiring external rewards, and ordering needs from basic physiological ones to higher-level needs for esteem and self-actualization.
The document discusses personality from several perspectives, including psychodynamic, trait, learning, biological, and humanistic approaches. It summarizes Freud's psychodynamic theory, including the id, ego, and superego. Jung and other neo-Freudians who expanded on his work are mentioned. Trait theories that aim to identify basic personality traits are covered. The influence of learning, biological factors, genetics, and humanism on personality are outlined briefly. Freud's psychosexual stages of development and defense mechanisms are summarized.
The document discusses emotions from several perspectives. It defines emotions as feelings that influence both physiology and behavior. It describes the intrapersonal, interpersonal, and social functions of emotions. Key features of emotions are that they are expressed through nonverbal behaviors and directed by arousal. Basic emotions include anger, disgust, fear, happiness, sadness, and surprise. Emotions also combine and are influenced by both biological and social factors. Theories on the origins and experience of emotions are presented, including the James-Lange, Cannon-Bard, and Schachter-Singer theories.
The document discusses various psychological disorders from multiple perspectives. It defines abnormal behavior and outlines approaches including medical, psychoanalytic, behavioral, cognitive, humanistic, and sociocultural. Major disorders covered include anxiety disorders, somatoform disorders, dissociative disorders, mood disorders, schizophrenia, personality disorders, and childhood disorders. Characteristics and types of each category of disorder are described in detail.
The document discusses various training methods used to enhance employee skills. It compares on-the-job training, which occurs at the workplace, to off-the-job training conducted outside of work. Some common on-the-job methods include job rotation, coaching, and apprenticeships, while classroom lectures, simulations, and role plays are examples of off-the-job training. Both approaches have advantages and disadvantages depending on the type of skills and industry.
Learning involves a relatively permanent change in behavior due to experience. The three main theories of learning are classical conditioning, operant conditioning, and cognitive learning. Classical conditioning involves associating a neutral stimulus with an unconditioned stimulus to elicit a response. Operant conditioning uses reinforcement or punishment to increase or decrease a behavior. Cognitive learning occurs through observation rather than direct experience.
The document discusses performance appraisal, which it defines as the systematic, periodic and impartial rating of an employee's job performance and potential. It notes that performance appraisal aims to improve current performance, provide feedback, identify training needs, and inform decisions around rewards, promotions and career development. The document outlines different methods of performance appraisal, including rating scales, forced distribution, management by objectives and 360 degree feedback. It also discusses potential errors in performance appraisal ratings.
The document discusses several leadership theories and studies:
- The Ohio State Leadership Studies identified two dimensions of leader behavior: initiating structure and consideration.
- The Michigan Leadership Studies identified two leadership styles: job-centered leadership and employee-centered leadership.
- Other theories discussed include trait theory, behavioral theory, contingency theory, path-goal theory, situational leadership, and the managerial grid. Key figures in the development of these theories such as Fiedler, House, and Blake & Mouton are also mentioned.
Organizational behavior is the study of how individuals and groups act within organizations. It examines how human behavior, group dynamics, and structure impact productivity, communication, motivation and leadership. Organizational behavior draws from psychology, sociology, and anthropology to understand individuals, teams, and structure. Its goal is to apply research findings to improve organizational effectiveness and human well-being in workplaces. It takes a holistic, interdisciplinary approach to analyze all factors influencing organizational functioning.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
3. CENSUS VS. SAMPLING
CENSUS
• Census refers to a periodic
collection of information about the
populace from the entire
population.
SAMPLE
• Sampling is a method of collecting
information from a sample that is
representative of the entire
population.
3
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
4. RELIABILITY
CONTENT CENSUS SAMPLE
Reliability Data from the census is
reliable and accurate.
There is a margin of error
in data obtained from
sampling.
Time Census is very time-
consuming.
Sampling is quick.
Cost Census is very
expensive
Sampling is inexpensive.
Convenience It is not very convenient
as the researcher has to
allocate a lot of effort in
collecting data.
Sampling is the most
convenient method of
obtaining data about the
population.
4
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
7. • Population: The aggregate of all the units pertaining to a
study. It is called as universe or target population.
• All the teaching professionals in VNR – A study on stress
management
• Element: A member of population.
• Individual staff members
• Sample: A subset of units in a population, selected to
represent all units in a population of interest.
• A list of professors who respond to the study.
• Sample unit: Element or set of elements considered for
selection in some stage of sampling.
• College wise classification, Discipline wise classification 7
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
8. • Sample frame: This is the actual list of sampling units
from which the sample, or some stage of the sample, is
selected. It is simply a list of the study population.
• Telephone directory, College address book.
• Sample design: A set of rules or procedures that specify
how a sample is to be selected. This can either be
probability or non-probability.
• Using a convenience sampling method to select the
sample professors
• Sampling: A process od drawing a sample from population.
• Sample size: The number of elements in the obtained
sample.
• 1500 college professors
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Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
15. • Heterogeneous population is divided into exclusive homogeneous group
& sample are drawn from each group.
• Each homogeneous group is called as “Strata”.
• Collection of strata is referred as “Stratum”
STRATIFIED SAMPLING
Students
College
Arts &
Science
Technical
School
Primary
Secondary
15
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
16. 16
Draw a sample from each stratum
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
18. SYSTEMATIC RANDOM SAMPLING
• Procedure:
• The first unit is selected at random from the sample frame.
• Other units selected at the regular intervals depending on the
size on the sample frame.
• It is known as “Fixed Interval method”.
• As it having the nonprobability traits, it is referred as “
Pseudo-random sampling”.
18
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
20. SYSTEMATIC SAMPLE
• EVERY KTH MEMBER ( FOR EXAMPLE: EVERY 10TH
PERSON) IS SELECTED FROM A LIST OF ALL POPULATION
MEMBERS.
Math
Alliance
Project
20
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
22. • A probability sampling method in which items are
chosen in clusters rather than individually from the
population is called “cluster sampling”.
• The clusters are termed as “Primary Units”.
• Random choice for selecting elements is two stages
is called as “Two stage cluster sample”.
22
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
23. Area Sampling
A method of sampling when no complete frame of
reference is available. The total area under
investigation is divided into small sub-areas which
are sampled at random or by some restricted random
process.
23
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
25. MULTISTAGE SAMPLING
• Multistage sampling can be a complex form of cluster sampling.
Dividing the population into groups (or clusters) then, one or more
clusters are chosen at random and everyone within the chosen cluster
is sampled.
25
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
27. DOUBLE AND MULTI PHASE
SAMPLING
• A sampling method in which certain items of
information are drawn from the whole units of a
sample and certain other items of information are
taken from the subsample.
27
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
29. Convenience sampling
• The researcher selects the most accessible population members from which
to get information.
• Sample which are very close to hand, very convenient, readily available or
willing to respond are chosen.
• It is called as “Grab/ Opportunity Sampling/Accidental sampling”
• “Collecting data from friends, acquaintances or from
colleagues”
29
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
31. JUDGEMENT SAMPLING
• The researcher uses judgement to select population
members who are good prospects for accurate
information.
• It known as “purposive of authoritative sampling”
• Collecting details regarding VVV MBA from the
students from corresponding dept.
31
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
32. QUOTA SAMPLING
• The researcher finds and collect data from a
prescribed number of people in each of several
categories.
• Out of 100 respondents, select 50 from school, 50
from college.
32
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar
33. SNOW BALL SAMPLING
• “Chain sampling, chain-referral sampling or
referral sampling”.
• A snowball sample is one in which the researcher
collects data on the few members of the target
population he or she can locate, then asks those
individuals to provide information needed to
locate other members of that population whom
they know.
• A study on homeless, requires very few people
and they would lead to reach others. 33
Dr.M.Jothilakshmi, VVVCollege, Virudhunagar