The document discusses a class on applied statistics for research. It introduces key concepts like population, sample, variables, and parameters. It explains the quantitative paradigm and different types of variables. The goals are to provide basic statistical tools for students' investigations and define terms to develop the course unit. Examples are given of nominal, ordinal, discrete, and continuous variables. Frequency distributions and graphical representations are also explained.
This document provides guidance on APA 7 citation style. It discusses in-text citations, reference lists, and basic rules for different citation scenarios. In-text citations can appear parenthetically or narratively and include the author's last name and year. The reference list is on a separate page in alphabetical order by author's last name. It includes the full details of cited works to allow readers to find sources. Proper citation format varies depending on the number of authors and whether the author is known.
This document provides guidance on improving academic writing skills such as using transitions, developing arguments, and engaging in metacommentary. It discusses connecting sentences, using transition words to show relationships between ideas, repeating key words and ideas to build cohesion, and explaining your points to the reader to aid interpretation. The document also provides examples of techniques like metacommentary that help guide the reader through a text.
The document provides an introduction to HTML, CSS, and JavaScript. It includes sections on:
- What HTML, CSS, and JavaScript are and their purposes. HTML is for describing web pages, CSS is for styling elements, and JavaScript is for creating dynamic content.
- Basic HTML page structure including common tags like <html>, <head>, <body>.
- Key CSS concepts like selectors, properties, values, and the box model.
- Core JavaScript concepts including the DOM, jQuery, AJAX, and the differences between JavaScript and jQuery.
- Examples are provided throughout to demonstrate uses of each technology.
Academic referencing harvard style of referencing-md ziauddin
This document provides an introduction to the Harvard style of referencing. It defines key terms like citation and references, and explains the importance of referencing in academic writing. The document outlines the different types of sources that can be referenced, such as books, journal articles, websites and newspapers. It provides examples of how to cite sources in text and structure reference list entries for different materials according to the Harvard style.
This document provides an overview of topics related to statistics that will be covered in the first class of a master's program course. It defines key terms like population, sample, variable, and parameter. It also distinguishes between descriptive and inferential statistics and different types of variables and data. Examples of statistical concepts are presented, including how to construct frequency distributions and common graphical representations of data in Excel. The last pages provide details on data collection and organizing raw versus grouped data.
This document provides an overview of statistics topics to be covered in the first week of a master's course. It defines key terms like population, sample, variable, and parameter. It also distinguishes between descriptive and inferential statistics and different types of variables and data. Examples are provided to illustrate qualitative and quantitative variables. Common methods for data collection and representation like frequency distributions and graphs are discussed. The last pages provide guidance on how statistics relates to research methodology and an example exercise to classify variables.
This document provides an overview of key concepts in applied statistics for research. It defines statistics, data, population, sample, parameter, variables, and sampling. It discusses descriptive and inferential statistics. It also presents examples of qualitative and quantitative variables and different data collection techniques used in quantitative and qualitative research paradigms. Finally, it discusses ungrouped and grouped data and different ways of presenting statistical data.
This document provides an introduction to a course on statistical methods in nursing. It outlines the general objectives of understanding the nature and definition of statistics, its brief historical development, distinguishing samples from populations, types of variables, and the importance of statistics in research. It includes a pre-test to assess students' basic knowledge of statistical concepts before beginning the lessons.
This document provides guidance on APA 7 citation style. It discusses in-text citations, reference lists, and basic rules for different citation scenarios. In-text citations can appear parenthetically or narratively and include the author's last name and year. The reference list is on a separate page in alphabetical order by author's last name. It includes the full details of cited works to allow readers to find sources. Proper citation format varies depending on the number of authors and whether the author is known.
This document provides guidance on improving academic writing skills such as using transitions, developing arguments, and engaging in metacommentary. It discusses connecting sentences, using transition words to show relationships between ideas, repeating key words and ideas to build cohesion, and explaining your points to the reader to aid interpretation. The document also provides examples of techniques like metacommentary that help guide the reader through a text.
The document provides an introduction to HTML, CSS, and JavaScript. It includes sections on:
- What HTML, CSS, and JavaScript are and their purposes. HTML is for describing web pages, CSS is for styling elements, and JavaScript is for creating dynamic content.
- Basic HTML page structure including common tags like <html>, <head>, <body>.
- Key CSS concepts like selectors, properties, values, and the box model.
- Core JavaScript concepts including the DOM, jQuery, AJAX, and the differences between JavaScript and jQuery.
- Examples are provided throughout to demonstrate uses of each technology.
Academic referencing harvard style of referencing-md ziauddin
This document provides an introduction to the Harvard style of referencing. It defines key terms like citation and references, and explains the importance of referencing in academic writing. The document outlines the different types of sources that can be referenced, such as books, journal articles, websites and newspapers. It provides examples of how to cite sources in text and structure reference list entries for different materials according to the Harvard style.
This document provides an overview of topics related to statistics that will be covered in the first class of a master's program course. It defines key terms like population, sample, variable, and parameter. It also distinguishes between descriptive and inferential statistics and different types of variables and data. Examples of statistical concepts are presented, including how to construct frequency distributions and common graphical representations of data in Excel. The last pages provide details on data collection and organizing raw versus grouped data.
This document provides an overview of statistics topics to be covered in the first week of a master's course. It defines key terms like population, sample, variable, and parameter. It also distinguishes between descriptive and inferential statistics and different types of variables and data. Examples are provided to illustrate qualitative and quantitative variables. Common methods for data collection and representation like frequency distributions and graphs are discussed. The last pages provide guidance on how statistics relates to research methodology and an example exercise to classify variables.
This document provides an overview of key concepts in applied statistics for research. It defines statistics, data, population, sample, parameter, variables, and sampling. It discusses descriptive and inferential statistics. It also presents examples of qualitative and quantitative variables and different data collection techniques used in quantitative and qualitative research paradigms. Finally, it discusses ungrouped and grouped data and different ways of presenting statistical data.
This document provides an introduction to a course on statistical methods in nursing. It outlines the general objectives of understanding the nature and definition of statistics, its brief historical development, distinguishing samples from populations, types of variables, and the importance of statistics in research. It includes a pre-test to assess students' basic knowledge of statistical concepts before beginning the lessons.
Esta guía está elaborada con el propósito de que los alumnos que adeudan
Estadística Descriptiva de la Licenciatura en Pedagogía cuenten con un
material de consulta que apoye el desarrollo de los contenidos temáticos, para
que avancen de forma independiente en el aprendizaje de esta asignatura y en
su preparación para el examen extraordinario
Week 9
Data Analysis
Resources
Readings
Yegidis, B. L., Weinbach, R. W., & Myers, L. L. (2018).
Research methods for social workers
(8th ed.). New York, NY: Pearson.
o Chapter 13, “Analyzing Data” (pp. 295–297, “The Data in Perspective”)
Bauer, S., Lambert, M. J., & Nielsen, S. L. (2004). Clinical significance methods: A comparison of statistical techniques.
Journal of Personality Assessment, 82
, 60–70.
Gibson, F. H. (2003).
Indigent client perceptions of barriers to marriage and family therapy
(Dissertation, University of Louisiana at Monroe).
Plummer, S.-B., Makris, S., & Brocksen S. M. (Eds.). (2014).
Social work case studies: Foundation year.
Baltimore, MD: Laureate International Universities Publishing. [Vital Source e- reader].
o Social Work Research: Program Evaluation
Data Analysis Techniques
In order to make decisions about the value of any research study for practice, it is important to understand the general processes involved in analyzing research data. By now, you have examined enough research studies to be aware that there are some common ways that data are reported and summarized in research studies. For example, the sample is often described by numbers of participants and by certain characteristics of those participants that help us determine how representative the sample is of a population. The information about the sample is commonly reported in tables and graphs, making use of frequency distributions, measures of central tendency, and dispersion. Information about the variables (or concepts) of interest when quantified are also reported in similar manner.
Although the actual data analysis takes place after data have been collected, from the initial planning of a research study, the researcher needs to have an awareness of the types of questions that can be answered by particular data analysis techniques.
For this Discussion, review the case study entitled "Social Work Research: Measuring Group Success." Consider the data analysis described in that case. Recall the information presented in the earlier chapters of your text about formulating research questions to inform a hypotheses or open-ended exploration of an issue.
Discussion 1
Relationship Between Purpose of Study and Data Analysis Techniques 1 page paper
Post
an explanation of the types of descriptive and/or inferential statistics you might use to analyze the data gathered in the case study. Also explain how the statistics you identify can guide you in evaluating the applicability of the study’s findings for your own practice as a social worker. Please use the Resources to support your answer.
Week 9
Data Analysis
Discussion 2
Research studies often compare variables, conditions, times, and/or groups of participants to evaluate relationships between variables or differences between groups or times. For example, if researchers are interested in knowing whether an intervention produces change in the ...
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
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LET in the NET (facebook)
http://bit.ly/LETndNET
This document provides an overview of quantitative research. It defines quantitative research as research that collects and analyzes numerical data using statistical or computational techniques. The key characteristics of quantitative research outlined include using structured instruments to collect data, analyzing data from large samples, establishing relationships between variables, and generalizing results. Both the strengths and weaknesses of quantitative research are discussed, with strengths including allowing for broader studies and objective results, and weaknesses including potentially overlooking unique cases and contextual factors. Examples of quantitative variables that can be measured are also provided.
Characteristic of a Quantitative Research PPT.pptxJHANMARKLOGENIO1
The document discusses quantitative research, including its definition, characteristics, strengths, and weaknesses. It notes that quantitative research seeks objective and accurate measurement through clearly defined research questions and structured instruments. Data is collected in numerical form from large sample sizes to allow for replication and generalization. Strengths include objectivity and the ability to analyze large amounts of data, while weaknesses include high costs and the inability to explore contextual factors.
Stastistics in Physical Education - SMK.pptxshatrunjaykote
• It is a specific branch of mathematics that deals with analysis of data collected on various population groups
• Statistics involves mathematical abilities more than addition, subtraction, division and multiplication which are repeated many times in a logical fashion.
• for fuller details of statistical tests may refer to Chandha (1992); Vincent (1995); Hopkin et al. (1996); Sincrich et al. (2002); Triola (2002)
• Understanding of basic statistics is indispensable for dealing with the process of evaluation of test and measurement.
• The statistical concepts facilities proper and effective interpretation of test scores or measurements taken by the coach or a physical educator
• While a computer assists the teacher or the coach in saving the huge time needed for enormous calculations, but the meaning of results is made clear only through the understanding of relevant statistical test concepts.
• Tests act as seed to measurements, the statistical tests act as seed to the construction of all other types of tests and are also essential for the testing of validity, reliability and objectivity of all tests.
The information which we can deduce from test and measurement is based on our statistical ability. It is the statistical tools which enable us to do the following important functions:
1. Organize and tabulate date (presentation of facts in a definite form)
2. Analysis data
3. Synthesize data (classification / combination of facts)
4. Compare groups of data
5. Simplification of unwieldy and complex data
6. Proper interpretation of a data
7. testing of hypotheses
8. understand the relationship and association between different parameters, make predications and take decisions.
9. Construction of physical, psychomotor and written tests
10. Evaluation of individual measurements
11. selection of sportsperson
12. Monitoring of training and teaching effects and testing the need for individualization of training and teaching.
13. Meaning: The word “statistics” is a plural form of ‘statistic’. The term statistic is uncommon to that an extent that many of the students of statistics may be unaware of its singular form. The word statistics has been taken from German word ‘statistik’ meaning a political state. Since, facts and figures were required in olden days mainly by kings for their administration. Therefore, in the beginning. It was also known as the ‘Science of Kings’ (Chadha, 1992). Subsequently, its scope has greatly widened and statistics now refers to a huge body of methods, symbols and formulae dealing with phenomena that can be described numerically providing quantitative arrays of information
14. Statistic is numerical value which characterizes a group of scores. For example the average height characterizes the entire sample whose all subjects’ heights have been measured to calculate the average height. A number of such characterizing values refer to the plural form of above mentioned statistic and thus, give rise to the more commonly used
Statistics can be used to analyze data, make predictions, and draw conclusions. It has a variety of applications including predicting disease occurrence, weather forecasting, medical studies, quality testing, and analyzing stock markets. There are two main branches of statistics - descriptive statistics which summarizes and presents data, and inferential statistics which analyzes samples to make conclusions about populations. Key terms include population, sample, parameter, statistic, variable, data, qualitative vs. quantitative data, discrete vs. continuous data, and the different levels of measurement. Important figures in the history of statistics mentioned are William Petty, Carl Friedrich Gauss, Ronald Fisher, and James Lind.
This document provides an introduction to biostatistics. It defines biostatistics as the application of statistical tools and concepts to data from biological sciences and medicine. The two main branches of statistics are described as descriptive statistics, which involves organizing and summarizing sample data, and inferential statistics, which involves generalizing from samples to populations. Several key statistical concepts are also defined, including populations, samples, variables, data types, levels of measurement, and common sampling methods. The objectives are to demonstrate knowledge of these fundamental statistical terms and concepts.
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
The document discusses basics of statistics including key concepts like population, sample, parameters, and statistics. It provides definitions for population as the collection of all individuals or items under consideration, and sample as the part of the population selected for a study. Parameters describe unknown characteristics of the population, while statistics describe known characteristics of the sample and are used to infer parameters. The document also distinguishes between descriptive statistics, which summarize and organize data, and inferential statistics, which draw conclusions about populations from samples.
Course Objectives Students will develop skills in 1. selecting anco4spmeley
Course Objectives Students will develop skills in: 1. selecting and using appropriate methods for evaluation of interventions and program processes and outcomes; 2. applying knowledge of human behavior and the social environment, person-in-environment, and other multidisciplinary theoretical frameworks in the evaluation of processes and outcomes; 3. demonstrating how to critically analyze, monitor, and evaluate intervention and program processes and outcomes; 4. applying evaluation findings to improve practice effectiveness at the micro, mezzo, and macro levels. Required Text(s) Grinnell, R. M., Gabor, P. A., & Unrau, Y. A. (2016). Program evaluation for social workers: Foundations of evidence-based programs (7th Ed.). New York: Oxford. Locke, L. F., Silverman, S. J., & Spirduso, W. W. (Eds.). (2010). Reading and understanding research (3rd Ed.). Thousand Oaks, CA: Sage. Grading ASSIGNMENT PERCENTAGE OF TOTAL GRADE SUBMISSION DATE Common Assignment: Research Proposal* 40% Dec 4th Required Assignment: Oral or written presentation of research findings 40% Dec 4th/11th Other: participation, and other assignments (e.g., discussion board, quizzes, exercises, etc.) 20% *See Appendix A for common assignment and/or grading rubric COURSE OUTLINE Module 1 Overview of the Research Process Module Topics 1. Review of concepts and methods of research 2. Introduction to evaluation and intervention research 3. Importance of evidence-based practice Readings Cheung, M., Ma, A. K., Thyer, B. A., & Webb, A. E. (2015). Research-practice integration in real practice settings: Issues and suggestions. Research on Social Work Practice, 25(4), 523-530. Drisko, J. W., & Grady, M. D. (2015). Evidence-based practice in social work: A contemporary perspective. Clinical Social Work Journal, 43(3), 274-282. doi:10.1007/s10615-015-0548-z Module 2 Overview of Intervention Research Module Topics 1. Definition of intervention research 2. Overview of intervention research 3. Manualized evidence-based practice 4. Common factors Cabassa, L. J. (2016). Implementation science: Why it matters for the future of social work. Journal of Social Work, 52(S1), 538-550. doi.org/10.1080/10437797.2016.1174648 Fraser, M. W., & Galinsky, M. J. (2010). Steps in intervention research: Designing and developing social programs. Research on Social Work Practice, 20(5), 459-466. doi/pdf/10.1177/1049731509358424 Goldstein, N. E. S., Kemp, K. A., Leff, S. S., & Lochman, J. E. (2012). Guidelines for adapting manualized interventions for new target populations: A step-wise approach using anger management as a model. Clinical Psychology, 19(4), 385-401. doi:10.1111/cpsp.12011 Module 3 Designing and Conducting Intervention Research Module Topics 1. Designing and refining an intervention 2. Theory of change 3. Preparing a logic model 4. Conducting an intervention research study 5. Testing efficacy 6. Testing effectiveness in practice settings Fraser, M. W., & Galinsky, M ...
PPT Group 4 Sifat dan Model Analitis Penelitian Kuantitatif.pdfAnggela20
This document discusses qualitative research analysis. It provides an overview of the nature of qualitative data analysis, including that it is inductive, naturalistic, subjective, holistic, humanistic, and a posteriori. It then discusses two models of qualitative data analysis: 1) the constant comparative method which involves comparing events and categories, and 2) Miles and Huberman's interactive model which involves three stages of data reduction, display, and conclusion drawing. It provides details on the steps involved in each stage of Miles and Huberman's model.
Questions On Quantitative And Qualitative ResearchKimberly Brooks
This document discusses quantitative and qualitative research methods. It provides pros and cons of quantitative research specifically. Some pros of quantitative research include using numerical data which eliminates misrepresentation, and results being repeatable. Cons include the time and expense required for advanced certification or degrees involving quantitative research methods. Both quantitative and qualitative research have benefits and limitations depending on the type of research and goals.
The research team met again to consider data sources. A research.docxkathleen23456789
The research team met again to consider data sources. A research consultant facilitated the discussion and identified issues to be addressed in order for the results to be credible. Three key areas needed further study before they went into the field. These areas included:
1. How is the program
positioned
in the community, particularly regarding trust, diversity, and access? (Or as one team member said, “How do we see ‘them’? And, how do we think they ‘see’ us?”)
This issue initiated an action plan for an organizational self-study to produce
reflexive
data before, during, and after field data collection.
2. What data sources would best answer the research questions?
Multiple sources
, including families who had used program services as well as those that did not; field observations (going out into neighborhoods to become acquainted with local, non-professional resources); and the materials collected from the self-study.
3. How many participants should be included in the sample?
The consultant clarified that the purpose of the sample was not to generalize to the target population—so bigger is not better. Rather, the team was encouraged to focus on selecting
typical cases
—homogenous, sub-groups—in order to efficiently
saturate
and develop a “solid understanding” (Guest, Bunce & Johnson, 2006, p. 77) of the phenomenon of the childcare experience in this community. The saturation goal means that the sample process is emergent and may change as the data become available.
As you can see in the ongoing scenario, before venturing out into the field, researchers must consider how they will manage credibility of the data. As a qualitative researcher, you too will have to address the sources of data as well as your credibility.
For this week, you will examine research questions, explore qualitative research design, and consider purposeful sampling and saturation as a qualitative researcher.
The answer lies in how clearly you articulate the criteria for selecting data sources; (b) your ability to purposefully select cases; and (c) the extent to which those cases are “information-rich… for in-depth study” (Patton, 2015, p. 264) with respect to the purpose of the study.
As you prepare for this week’s Discussion, consider turning your attention to the variety of purposeful sampling strategies you may consider in developing your research plan. Also consider that qualitative researchers seek a threshold or cut-off point for when to stop collecting data. There is no
magic number
(although there are guidelines). Rather, saturation occurs as an interface between the researcher and the data and (b) between data collection and data analysis to determine when enough is enough.
For this Discussion, you will critique a sampling strategy used in a research article.
To prepare for this Discussion:
· Review the Guest, Bunce, and Johnson article; the Yob and Brewer article; and the Learning Resources related to sampling and saturation for this .
The Importance Of Quantitative Research DesignsNicole Savoie
The document discusses quantitative and qualitative research designs. It states that qualitative research aims to understand the reasons and motivations behind issues, while quantitative research focuses on measuring trends and generalizing results from samples to populations. As examples, it provides details about two studies, one using a qualitative design to understand family relationships and support for mothers, and the other using a quantitative design but does not provide details about the specific study. It also provides background information on the samples and methods used in the qualitative study.
Recapitulation of Basic Statistical Concepts .pptxFranCis850707
The document provides definitions and explanations of basic statistical concepts. It defines statistics as concerning the collection, organization, analysis, interpretation and presentation of data. It distinguishes between populations, which are entire sets of items from which data is drawn, and samples, which are subsets of populations that are used when a population is too large. It describes descriptive statistics, which describe properties of sample and population data, and inferential statistics, which use descriptive statistics to test hypotheses and draw conclusions about populations from samples.
Quantitative research was the dominant research paradigm in education until the 1980s when debates increased between quantitative and qualitative approaches. Some researchers argued their approach was superior, with some purists arguing the approaches could not be combined due to differing worldviews. A research paradigm encompasses ontology, epistemology, and methodology. Quantitative research aims to quantify data, test theories through hypotheses, and use statistics to support or refute hypotheses. It emphasizes objectivity, generalizability, and identifying causal relationships through controlled experiments and standardized procedures.
The document discusses planning a research study, including identifying the target population and sample, deciding on the appropriate level and size of sampling, and selecting appropriate data collection methods. Some key points covered are:
1) Researchers must identify the target population and determine whether to study individuals, organizations, or a combination. They must also decide how many people or organizations to include in the sample.
2) Researchers select a sampling method depending on rigor needed, population characteristics, and participant availability. Common methods include simple random sampling, stratified sampling, cluster sampling, and convenience/snowball sampling.
3) Researchers identify what data needs to be collected to measure the study variables. Common methods are tests, questionnaires, interviews, observations
El documento describe los escenarios de aprendizaje para una formación multicanal. Define los sistemas multimodales de educación universitaria y los escenarios de aprendizaje como espacios digitales donde participan actores con el objetivo de aprender. Explica la enseñanza multicanal considerando la audiencia, los canales accesibles, el modelo de aprendizaje y evaluación, y el rol de los docentes. Además, describe la evaluación multidimensional y los elementos de un módulo de aprendizaje personalizado e independiente para la formación en línea
Esta guía está elaborada con el propósito de que los alumnos que adeudan
Estadística Descriptiva de la Licenciatura en Pedagogía cuenten con un
material de consulta que apoye el desarrollo de los contenidos temáticos, para
que avancen de forma independiente en el aprendizaje de esta asignatura y en
su preparación para el examen extraordinario
Week 9
Data Analysis
Resources
Readings
Yegidis, B. L., Weinbach, R. W., & Myers, L. L. (2018).
Research methods for social workers
(8th ed.). New York, NY: Pearson.
o Chapter 13, “Analyzing Data” (pp. 295–297, “The Data in Perspective”)
Bauer, S., Lambert, M. J., & Nielsen, S. L. (2004). Clinical significance methods: A comparison of statistical techniques.
Journal of Personality Assessment, 82
, 60–70.
Gibson, F. H. (2003).
Indigent client perceptions of barriers to marriage and family therapy
(Dissertation, University of Louisiana at Monroe).
Plummer, S.-B., Makris, S., & Brocksen S. M. (Eds.). (2014).
Social work case studies: Foundation year.
Baltimore, MD: Laureate International Universities Publishing. [Vital Source e- reader].
o Social Work Research: Program Evaluation
Data Analysis Techniques
In order to make decisions about the value of any research study for practice, it is important to understand the general processes involved in analyzing research data. By now, you have examined enough research studies to be aware that there are some common ways that data are reported and summarized in research studies. For example, the sample is often described by numbers of participants and by certain characteristics of those participants that help us determine how representative the sample is of a population. The information about the sample is commonly reported in tables and graphs, making use of frequency distributions, measures of central tendency, and dispersion. Information about the variables (or concepts) of interest when quantified are also reported in similar manner.
Although the actual data analysis takes place after data have been collected, from the initial planning of a research study, the researcher needs to have an awareness of the types of questions that can be answered by particular data analysis techniques.
For this Discussion, review the case study entitled "Social Work Research: Measuring Group Success." Consider the data analysis described in that case. Recall the information presented in the earlier chapters of your text about formulating research questions to inform a hypotheses or open-ended exploration of an issue.
Discussion 1
Relationship Between Purpose of Study and Data Analysis Techniques 1 page paper
Post
an explanation of the types of descriptive and/or inferential statistics you might use to analyze the data gathered in the case study. Also explain how the statistics you identify can guide you in evaluating the applicability of the study’s findings for your own practice as a social worker. Please use the Resources to support your answer.
Week 9
Data Analysis
Discussion 2
Research studies often compare variables, conditions, times, and/or groups of participants to evaluate relationships between variables or differences between groups or times. For example, if researchers are interested in knowing whether an intervention produces change in the ...
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
If you happen to like this powerpoint, you may contact me at flippedchannel@gmail.com
I offer some educational services like:
-powerpoint presentation maker
-grammarian
-content creator
-layout designer
Subscribe to our online platforms:
FlippED Channel (Youtube)
http://bit.ly/FlippEDChannel
LET in the NET (facebook)
http://bit.ly/LETndNET
This document provides an overview of quantitative research. It defines quantitative research as research that collects and analyzes numerical data using statistical or computational techniques. The key characteristics of quantitative research outlined include using structured instruments to collect data, analyzing data from large samples, establishing relationships between variables, and generalizing results. Both the strengths and weaknesses of quantitative research are discussed, with strengths including allowing for broader studies and objective results, and weaknesses including potentially overlooking unique cases and contextual factors. Examples of quantitative variables that can be measured are also provided.
Characteristic of a Quantitative Research PPT.pptxJHANMARKLOGENIO1
The document discusses quantitative research, including its definition, characteristics, strengths, and weaknesses. It notes that quantitative research seeks objective and accurate measurement through clearly defined research questions and structured instruments. Data is collected in numerical form from large sample sizes to allow for replication and generalization. Strengths include objectivity and the ability to analyze large amounts of data, while weaknesses include high costs and the inability to explore contextual factors.
Stastistics in Physical Education - SMK.pptxshatrunjaykote
• It is a specific branch of mathematics that deals with analysis of data collected on various population groups
• Statistics involves mathematical abilities more than addition, subtraction, division and multiplication which are repeated many times in a logical fashion.
• for fuller details of statistical tests may refer to Chandha (1992); Vincent (1995); Hopkin et al. (1996); Sincrich et al. (2002); Triola (2002)
• Understanding of basic statistics is indispensable for dealing with the process of evaluation of test and measurement.
• The statistical concepts facilities proper and effective interpretation of test scores or measurements taken by the coach or a physical educator
• While a computer assists the teacher or the coach in saving the huge time needed for enormous calculations, but the meaning of results is made clear only through the understanding of relevant statistical test concepts.
• Tests act as seed to measurements, the statistical tests act as seed to the construction of all other types of tests and are also essential for the testing of validity, reliability and objectivity of all tests.
The information which we can deduce from test and measurement is based on our statistical ability. It is the statistical tools which enable us to do the following important functions:
1. Organize and tabulate date (presentation of facts in a definite form)
2. Analysis data
3. Synthesize data (classification / combination of facts)
4. Compare groups of data
5. Simplification of unwieldy and complex data
6. Proper interpretation of a data
7. testing of hypotheses
8. understand the relationship and association between different parameters, make predications and take decisions.
9. Construction of physical, psychomotor and written tests
10. Evaluation of individual measurements
11. selection of sportsperson
12. Monitoring of training and teaching effects and testing the need for individualization of training and teaching.
13. Meaning: The word “statistics” is a plural form of ‘statistic’. The term statistic is uncommon to that an extent that many of the students of statistics may be unaware of its singular form. The word statistics has been taken from German word ‘statistik’ meaning a political state. Since, facts and figures were required in olden days mainly by kings for their administration. Therefore, in the beginning. It was also known as the ‘Science of Kings’ (Chadha, 1992). Subsequently, its scope has greatly widened and statistics now refers to a huge body of methods, symbols and formulae dealing with phenomena that can be described numerically providing quantitative arrays of information
14. Statistic is numerical value which characterizes a group of scores. For example the average height characterizes the entire sample whose all subjects’ heights have been measured to calculate the average height. A number of such characterizing values refer to the plural form of above mentioned statistic and thus, give rise to the more commonly used
Statistics can be used to analyze data, make predictions, and draw conclusions. It has a variety of applications including predicting disease occurrence, weather forecasting, medical studies, quality testing, and analyzing stock markets. There are two main branches of statistics - descriptive statistics which summarizes and presents data, and inferential statistics which analyzes samples to make conclusions about populations. Key terms include population, sample, parameter, statistic, variable, data, qualitative vs. quantitative data, discrete vs. continuous data, and the different levels of measurement. Important figures in the history of statistics mentioned are William Petty, Carl Friedrich Gauss, Ronald Fisher, and James Lind.
This document provides an introduction to biostatistics. It defines biostatistics as the application of statistical tools and concepts to data from biological sciences and medicine. The two main branches of statistics are described as descriptive statistics, which involves organizing and summarizing sample data, and inferential statistics, which involves generalizing from samples to populations. Several key statistical concepts are also defined, including populations, samples, variables, data types, levels of measurement, and common sampling methods. The objectives are to demonstrate knowledge of these fundamental statistical terms and concepts.
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
The document discusses basics of statistics including key concepts like population, sample, parameters, and statistics. It provides definitions for population as the collection of all individuals or items under consideration, and sample as the part of the population selected for a study. Parameters describe unknown characteristics of the population, while statistics describe known characteristics of the sample and are used to infer parameters. The document also distinguishes between descriptive statistics, which summarize and organize data, and inferential statistics, which draw conclusions about populations from samples.
Course Objectives Students will develop skills in 1. selecting anco4spmeley
Course Objectives Students will develop skills in: 1. selecting and using appropriate methods for evaluation of interventions and program processes and outcomes; 2. applying knowledge of human behavior and the social environment, person-in-environment, and other multidisciplinary theoretical frameworks in the evaluation of processes and outcomes; 3. demonstrating how to critically analyze, monitor, and evaluate intervention and program processes and outcomes; 4. applying evaluation findings to improve practice effectiveness at the micro, mezzo, and macro levels. Required Text(s) Grinnell, R. M., Gabor, P. A., & Unrau, Y. A. (2016). Program evaluation for social workers: Foundations of evidence-based programs (7th Ed.). New York: Oxford. Locke, L. F., Silverman, S. J., & Spirduso, W. W. (Eds.). (2010). Reading and understanding research (3rd Ed.). Thousand Oaks, CA: Sage. Grading ASSIGNMENT PERCENTAGE OF TOTAL GRADE SUBMISSION DATE Common Assignment: Research Proposal* 40% Dec 4th Required Assignment: Oral or written presentation of research findings 40% Dec 4th/11th Other: participation, and other assignments (e.g., discussion board, quizzes, exercises, etc.) 20% *See Appendix A for common assignment and/or grading rubric COURSE OUTLINE Module 1 Overview of the Research Process Module Topics 1. Review of concepts and methods of research 2. Introduction to evaluation and intervention research 3. Importance of evidence-based practice Readings Cheung, M., Ma, A. K., Thyer, B. A., & Webb, A. E. (2015). Research-practice integration in real practice settings: Issues and suggestions. Research on Social Work Practice, 25(4), 523-530. Drisko, J. W., & Grady, M. D. (2015). Evidence-based practice in social work: A contemporary perspective. Clinical Social Work Journal, 43(3), 274-282. doi:10.1007/s10615-015-0548-z Module 2 Overview of Intervention Research Module Topics 1. Definition of intervention research 2. Overview of intervention research 3. Manualized evidence-based practice 4. Common factors Cabassa, L. J. (2016). Implementation science: Why it matters for the future of social work. Journal of Social Work, 52(S1), 538-550. doi.org/10.1080/10437797.2016.1174648 Fraser, M. W., & Galinsky, M. J. (2010). Steps in intervention research: Designing and developing social programs. Research on Social Work Practice, 20(5), 459-466. doi/pdf/10.1177/1049731509358424 Goldstein, N. E. S., Kemp, K. A., Leff, S. S., & Lochman, J. E. (2012). Guidelines for adapting manualized interventions for new target populations: A step-wise approach using anger management as a model. Clinical Psychology, 19(4), 385-401. doi:10.1111/cpsp.12011 Module 3 Designing and Conducting Intervention Research Module Topics 1. Designing and refining an intervention 2. Theory of change 3. Preparing a logic model 4. Conducting an intervention research study 5. Testing efficacy 6. Testing effectiveness in practice settings Fraser, M. W., & Galinsky, M ...
PPT Group 4 Sifat dan Model Analitis Penelitian Kuantitatif.pdfAnggela20
This document discusses qualitative research analysis. It provides an overview of the nature of qualitative data analysis, including that it is inductive, naturalistic, subjective, holistic, humanistic, and a posteriori. It then discusses two models of qualitative data analysis: 1) the constant comparative method which involves comparing events and categories, and 2) Miles and Huberman's interactive model which involves three stages of data reduction, display, and conclusion drawing. It provides details on the steps involved in each stage of Miles and Huberman's model.
Questions On Quantitative And Qualitative ResearchKimberly Brooks
This document discusses quantitative and qualitative research methods. It provides pros and cons of quantitative research specifically. Some pros of quantitative research include using numerical data which eliminates misrepresentation, and results being repeatable. Cons include the time and expense required for advanced certification or degrees involving quantitative research methods. Both quantitative and qualitative research have benefits and limitations depending on the type of research and goals.
The research team met again to consider data sources. A research.docxkathleen23456789
The research team met again to consider data sources. A research consultant facilitated the discussion and identified issues to be addressed in order for the results to be credible. Three key areas needed further study before they went into the field. These areas included:
1. How is the program
positioned
in the community, particularly regarding trust, diversity, and access? (Or as one team member said, “How do we see ‘them’? And, how do we think they ‘see’ us?”)
This issue initiated an action plan for an organizational self-study to produce
reflexive
data before, during, and after field data collection.
2. What data sources would best answer the research questions?
Multiple sources
, including families who had used program services as well as those that did not; field observations (going out into neighborhoods to become acquainted with local, non-professional resources); and the materials collected from the self-study.
3. How many participants should be included in the sample?
The consultant clarified that the purpose of the sample was not to generalize to the target population—so bigger is not better. Rather, the team was encouraged to focus on selecting
typical cases
—homogenous, sub-groups—in order to efficiently
saturate
and develop a “solid understanding” (Guest, Bunce & Johnson, 2006, p. 77) of the phenomenon of the childcare experience in this community. The saturation goal means that the sample process is emergent and may change as the data become available.
As you can see in the ongoing scenario, before venturing out into the field, researchers must consider how they will manage credibility of the data. As a qualitative researcher, you too will have to address the sources of data as well as your credibility.
For this week, you will examine research questions, explore qualitative research design, and consider purposeful sampling and saturation as a qualitative researcher.
The answer lies in how clearly you articulate the criteria for selecting data sources; (b) your ability to purposefully select cases; and (c) the extent to which those cases are “information-rich… for in-depth study” (Patton, 2015, p. 264) with respect to the purpose of the study.
As you prepare for this week’s Discussion, consider turning your attention to the variety of purposeful sampling strategies you may consider in developing your research plan. Also consider that qualitative researchers seek a threshold or cut-off point for when to stop collecting data. There is no
magic number
(although there are guidelines). Rather, saturation occurs as an interface between the researcher and the data and (b) between data collection and data analysis to determine when enough is enough.
For this Discussion, you will critique a sampling strategy used in a research article.
To prepare for this Discussion:
· Review the Guest, Bunce, and Johnson article; the Yob and Brewer article; and the Learning Resources related to sampling and saturation for this .
The Importance Of Quantitative Research DesignsNicole Savoie
The document discusses quantitative and qualitative research designs. It states that qualitative research aims to understand the reasons and motivations behind issues, while quantitative research focuses on measuring trends and generalizing results from samples to populations. As examples, it provides details about two studies, one using a qualitative design to understand family relationships and support for mothers, and the other using a quantitative design but does not provide details about the specific study. It also provides background information on the samples and methods used in the qualitative study.
Recapitulation of Basic Statistical Concepts .pptxFranCis850707
The document provides definitions and explanations of basic statistical concepts. It defines statistics as concerning the collection, organization, analysis, interpretation and presentation of data. It distinguishes between populations, which are entire sets of items from which data is drawn, and samples, which are subsets of populations that are used when a population is too large. It describes descriptive statistics, which describe properties of sample and population data, and inferential statistics, which use descriptive statistics to test hypotheses and draw conclusions about populations from samples.
Quantitative research was the dominant research paradigm in education until the 1980s when debates increased between quantitative and qualitative approaches. Some researchers argued their approach was superior, with some purists arguing the approaches could not be combined due to differing worldviews. A research paradigm encompasses ontology, epistemology, and methodology. Quantitative research aims to quantify data, test theories through hypotheses, and use statistics to support or refute hypotheses. It emphasizes objectivity, generalizability, and identifying causal relationships through controlled experiments and standardized procedures.
The document discusses planning a research study, including identifying the target population and sample, deciding on the appropriate level and size of sampling, and selecting appropriate data collection methods. Some key points covered are:
1) Researchers must identify the target population and determine whether to study individuals, organizations, or a combination. They must also decide how many people or organizations to include in the sample.
2) Researchers select a sampling method depending on rigor needed, population characteristics, and participant availability. Common methods include simple random sampling, stratified sampling, cluster sampling, and convenience/snowball sampling.
3) Researchers identify what data needs to be collected to measure the study variables. Common methods are tests, questionnaires, interviews, observations
Similar to Estadística Aplicada a la Investigación (20)
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The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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1. Dra. Lila Virginia Lugo García
Santa Ana de Coro, Enero 2021
UNIVERSIDAD NACIONAL EXPERIMENTAL
“FRANCISCO DE MIRANDA”
DECANATO DE POSTGRADO
PROGRAMA MAESTRIA EN GERENCIA PÚBLICA
Sesión de Clase Semana 1
2. Estadística Aplicada a la Investigación
Es importante mencionar que el objetivo de la unidad curricular es presentar las
herramientas básicas que le permitan al estudiante utilizar las técnicas y métodos
estadístico en su investigación.
Es por ello que antes de comenzar con las definiciones y procedimientos estadísticos, se
iniciará situando al lector en los enfoques y paradigmas existentes, evidenciándose como la
estadística usualmente se aplica en el enfoque cuantitativo. En este sentido, a continuación
se presenta un esquema que presenta la finalidad del paradigma, los tipos de datos que
utiliza, la técnica de recolección y análisis de la información.
Seguidamente se presentan la definición de algunos términos que nos permitirán
desarrollar la unidad, entre ellos se tiene las concepciones de: estadística, datos, población,
muestra, variable, parámetro, muestreo así como también los tipos de estadística y variables.
Se especifica que los datos pueden estar agrupados o no, y los tipos de representaciones para
la presentación de los mismo. También se explica y se construyen la distribución de
frecuencia y las principales representaciones gráficas apoyados en Microsoft office
EXCEL para su elaboración. Además se presenta el procedimiento que permite agrupar los
datos no agrupados en intervalos de clases para realizar la respectiva distribución de
frecuencias que permite la mejor manipulación e interpretación de los mismos.
Introducción
Pág 2
3. ENFOQUE
PARADIGMAS FINALIDAD TIPOS DE
DATOS
TÉCNICA DE
RECOLECCIÓN
ANÁLISIS DE LA
INFORMACIÓN
CUANTITATIVO
POSITIVISMO Generar un saber
técnico donde se trata
de predecir y
controlar el mundo
natural y social
Predominan
los
cuantitativos
Basados en
estadísticas
Cuantitativo:
Estadística
Descriptiva e
inferencial
CUALITATIVO
INTERPRETATIVO Generar un
conocimiento
práctico enfocado en
la interpretación
humana
Predominan
los
cualitativos
Entrevista,
observación y
estudio de
casos
Cualitativo:
Inducción
Analítica y
Triangulación
CRÍTICO Generar un
conocimiento para
emancipar a las
personas
Predominan
los
cualitativos
Observación
participante
Intersubjetividad
y Dialéctica
Paradigmas de Investigación
Adaptado de: Sandin E, M. Paz (2003) Investigación Cualitativa en Educación . 1era Edición.Editorial MsGraw-Hill/ Interamericana Editores S.A de C.V. México D.F. p.34
Pág 3
4. Relación entre las Etapas de la Investigación (Positivista) y la Estadística
1) Objetivo que persigue la investigación
2) Planteamientode hipótesis o teorías
3) Método a usar para la obtención de la Información
4) Recolecciónde la información
5) Revisión, clasificación y tabulación de resultados obtenidos
6) Reduccióny análisis de los datos
7) Interpretación de los resultados
8) Publicación de Cuadros y tablas
9) Análisis de Resultados
10) Conclusionesy Recomendaciones
E
s
t
a
d
í
s
t
i
c
a
Adaptado de: Johson R. y Kuby p (2004). Estadística Elemental. Lo esencial. 3ra Edición. Editorial Thomson. México D.F
En líneas generales los anteriores pasos representan la lógica metodológica que se sigue en una investigación
cuantitativa con paradigma positivista. Cabe mencionar que este tipo de enfoques es el que se sirve de la
estadística como método para la recogida y análisis de los datos, como lo vimos en la diapositiva anterior. En el
cuadro que se presenta en la parte superior se observa como una parte de la investigación depende del buen uso
de las medidas, procedimientos y métodos estadísticos
Pág 4
5. Aspectos a tratar
1) Diferencias entre estadística y estadísticos
2) Definición de estadística
3) Población y Muestra
4) Definición de Variables y parámetros.
5) Tipos de Estadística
6) Tipos de Variables. Ejemplos
7) Recolección de Datos
8) Datos Agrupados y no agrupados
9) Tipos de Representaciones
10) Características de las tablas estadísticas
11) Distribución de frecuencia
12) Características de las gráficas estadísticas
13) Pasos para la construcción de tablas agrupadas
14) Ejercicios
Sesión de Clase
Pág 5
6. Estadísticas
Se relaciona con:
• Datos y números
• Gráficos y cuadros
Estadístico o Estadígrafo
Se relaciona con:
• Profesional
• Medidas
Diferencias entre conceptos
Existen conceptos que se tienden a confundir, muchas veces de forma ligera se
consideran como sinónimos pero en el lenguaje formal y técnico es importante
que se tengan claros. A continuación se presentan algunas definiciones que
permitirán entender las posteriores explicaciones y referencias
Pág 6
7. Explicar, dar conclusionesy
facilitar la tomade decisiones
Instrumento de
Apoyo
Diversas ciencias
Datos obtenidos de la población o muestra
Recolecta Presenta
Organiza Analiza
ESTADÍSTICA
Ciencia o Conjunto de Métodos
ES
los
que
sirve
como
QUE
Pág 7
8. Ciencia o conjunto de métodos que tiene por
objetivo recopilar, organizar, presentar, analizar e
interpretar datos obtenidos de la población o
muestra como instrumento de apoyo a otras
ciencias como instrumento de apoyo para explicar
y dar conclusiones a situaciones con la facilitar la
toma de decisiones.
Símbolos que describen un objeto, condición o
situación que por si mismo no tienen significado
alguno sino que deben ser presentados en una
forma utilizable y colocarlos en un contexto que le
de valor. Pueden ser Cualitativos y Cuantitativos
Conjunto de datos con un significado que reduce la
incertidumbre o aumenta el conocimiento de algo.
Estadística
Datos
Información
Pág 8
9. POBLACIÓN
MUESTRA
POBLACIÓN:
Conjunto finito o infinito de personas, cosas o
elementos que poseen la característica en común
objeto de estudio
MUESTRA:
Es cualquier subconjunto de la
población o universo.
MUESTRA REPRESENTATIVA:
Es un subconjunto que resume todas
las características de la población que
se considera para inferir importantes
conclusiones sin necesidad de trabajar
con todo el universo de datos
M
R
Pág 9
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
M
R
10. Variable
Es el atributo o característica de interés sobre cada
elemento individual de una población o muestra.
Puede medirse pues sufre cambios a través del tiempo,
de un lugar a otro, o de un individuo a otro.
Adaptado de: Lind D., Mason R., y Marchal W. (2001) Estadística para administración y economía. 3era Edición. Editorial MsGraw-Hill/ Interamericana Editores S.A de C.V.
México D.F. y Johson R. y Kuby p (2004). Estadística Elemental. Lo esencial. 3ra Edición.Editorial Thomson. México D.F
Parámetro
Valor numérico que describe a toda la población. Ejemplo:
Edad promedio de los estudiantes que ingresan a la carrera
de medicina.
Muestreo
Aleatorio:
Todos los elementos de la población tienen la misma probabilidad
de ser elegidos.
Probabilístico:
Los elementos a seleccionar se obtienen con base en la probabilidad.
De Juicio:
El investigador elige unidades que considera representativas de la
población.
Algunos tipos
Pág 10
11. TIPOS DE ESTADÍSTICA
DESCRIPTIVA
Utiliza los métodos de
recolección, agrupación,
presentación, análisis e
interpretación de datos
originados a partir de los
fenómenos de estudio.
Es decir descripciones
numéricas
INFERENCIAL
Aplica la generación de
modelos, generalizaciones
en la población inferidas a
partir de muestras.
La estadística inferencial es
inductiva porque se
proyecta de l0 especifico
(muestra) hacia l0 general
(población),
Pág 11
12. TIPOS DE VARIABLE
CUALITATIVAS
(Atributo o Categoría)
Nominal Ordinal
CUANTITATIVAS
(Numérica)
Discreta Continua
Se describe o identifica:
Solo se puede clasificar o
contar.
Se organiza de
acuerdo a una
jerarquía.
Toma valores
enteros
Toma cualquier
valor dentro de un
intervalo.
Adaptado de: Lind D., Mason R., y Marchal W. (2001) Estadística para administración y economía. 3era Edición. Editorial MsGraw-Hill/ Interamericana Editores S.A de C.V.
México D.F. y Johson R. y Kuby p (2004). Estadística Elemental. Lo esencial. 3ra Edición.Editorial Thomson. México D.F
Pág 12
13. EJEMPLOS DE VARIABLES
CUALITATIVAS CUANTITATIVAS
Variable Nominal Variable Ordinal Variable Discreta Variable Continua
Ciudad de Origen.
Estado Civil.
Tipo de
padecimiento.
Modelo de equipo.
Nivel de Satisfacción
del cliente.
Cumplimiento de
estándares de Calidad.
Frecuencia en el uso de
un laboratorio.
Número de equipos
dañados.
Número de solicitudes
rechazadas
Estudiantes reprobados
Cantidad de personas
enfermas
Peso
Estatura
Temperatura
Distancias
Adaptado de: Lind D., Mason R., y Marchal W. (2001) Estadística para administración y economía.3era Edición. Editorial MsGraw-Hill/InteramericanaEditores S.A de C.V. México D.F.
Johson R. y Kuby p (2004). Estadística Elemental. Lo esencial. 3ra Edición. Editorial Thomson.México D.F
Pág 13
14. Adaptado de: Johson R. y Kuby p (2004). Estadística Elemental. Lo esencial. 3ra Edición. Editorial Thomson. México D.F
Ejercicio práctico:
En el articulo “States of Health” que apareció en el número de junio de 1998 de la
revista “Ladies Home Journal” , se presentaron los resultados de un estudio en el
que se analizaron los datos recolectados por la Oficina Nacional de Censo de
Estados Unidos en 1997. Tales resultados revelan que para hombres como para
mujeres estadounidenses, las afecciones cardiacas siguen siendo la causa más
importante de decesos, por las cual mueren 500.000 personas al año. La edad, la
obesidad y el sedentarismo contribuyen a las afecciones cardiacas, aunque estos
tres factores varían considerablemente de un sitio a otro. Las tazas de mortalidad
más elevadas (muertes por cada 100.000 personas) se observaron en: Nueva York,
Florida, Oklahoma y Arkansas, mientras las más bajas se reportaron en Alaska,
Utah, Colorado y Nuevo México.
Determine:
a. ¿Cuál es la población?
b. ¿Cuáles son las características de interés?
c. ¿Cuál es el parámetro?
d. Clasifique las variables del estudio como de atributos o numéricas
Pág 14
15. Determine:
a. ¿Cuál es la población?
Todas las personas que fallecieron en 1997
b. ¿Cuáles son las características de interés?
• Muerte por paro cardiaco
• Estado de residencia
• Edad al fallecer
• Obesidad
• Sedentarismo
c. ¿Cuál es el parámetro?
Índice de mortalidad por cada 100.000 personas
d. Clasifique las variables del estudio como de atributos o numéricas
Atributos:
• Muerte por paro cardiaco
• Estado de residencia
• Obesidad
• Sedentarismo
Numérica:
• Edad al fallecer.
Respuesta del Ejercicio:
Pág 15
16. Adaptado de: Sampieri y Otros (2003).Metodología de la investigación. 3era Edición. Editorial McGraw-Hill/ Interamericana Editores S.A de C.V. México D.F.
Recolección de datos
Finalidad
Recoger “Buenos Datos” que permitan hacer inferencias acertadas sobre características
desconocidas de la población de la cual se obtuvo la muestra.
Cuantitativo Cualitativo
¿Cómo se
recolectan
datos?
Instrumentos de recolección:
•Cuestionarios
•Encuestas
•MEDICIONES
•Entrevistas en profundidad
•Diario de notas
•Observación participante
•Grabaciones voz y Video
¿Quién
suministra la
información?
Objeto de estudio o Sujetos de Investigación:
• Población
•Muestra
Informantes Clave
Criterios de
una buena
recolección de
datos
•Confiabilidad: Grado en que un instrumento aplicado
de manera repetida, arroja resultados similares.
Ejemplo: Coeficiente Alfa de Crobach. Rango 0-1.
•Validez: Grado en que un instrumento mide la variable.
de contenido (refleja un dominio del tema a investigar
o medir)
de constructo (apoyado en cumplimiento del marco
teórico)
de criterio (comparando con criterios externos)
Pruebas piloto.
•Triangulación
•Validez (Consentimientos
informados, Par investigativo,
talleres, etc.)
Enfoque
Características.
Pág 16
17. • Conjunto de observaciones que se presentan en su
forma original tal y como fueron recolectados.
Usualmente se presentan sin aproximaciones ni
redondeos. Cuando son pocos datos pueden trabajarse
de manera más o menos fácil, pero si son muchos se
recomiendan ordenarlos en intervalos o tabularlos es
decir representarlos en una tabla de frecuencias
DATOS NO
AGRUPADOS
• Aquellos que tienen como característica principal la
frecuencia con que se presentan es decir que
serán aquellos que se encuentran contados y
clasificados en intervalos. Esta agrupación se
realiza para el mejor manejo de ellos.
DATOS
AGRUPADOS
Procesamiento: datos no agrupados y datos agrupados
Los datos agrupados y no agrupados se les llaman a la forma como se presentan los
datos recolectados para representarlos y analizarlos.
Pág 17
18. Procesamiento: datos no agrupados y datos agrupados
DATOS NO AGRUPADOS
DATOS AGRUPADOS
Ejemplo:
Edades de los habitantes de un sector
Ejemplo:
Edades de los habitantes de una comunidad
Cabe mencionar que el segundo ejemplo corresponde a la agrupación de una Distribución de Frecuencia, sin
embargo se llaman datos agrupados a aquellos que poseen datos en intervalos con su respectiva frecuencia.
Más adelante se presentará uno de los tantos procedimientos usados para agrupar los datos
Pág 18
19. Los Datos se pueden
Representar en:
Tablas
Datos y/o
Información
Distribuciones
de frecuencias
(Numéricos)
Gráficos
Barras, Circulares o
de Líneas
Tipos de Representaciones Estadísticas
Incluye
Pueden
ser:
Existen estos dos tipos de representaciones, no son excluyentes por
el contrario son complementarias ya que pueden usar de acuerdo a
la necesidad o finalidad de la exposición de los datos.
Pág 19
20. Tipos de Representaciones Estadísticas
Pág 20
La adecuada
presentación pero
sobre todo facilitan
la Interpretación
de los datos
TABLA GRÁFICO
Finalidad
21. CUADROS O TABLAS
Deben Poseer
Titulo y
Fuente
Encabezado
o Rótulos
Columnas y
Programación
Tipos
Generales o
Referenciales Textos o
Resumen
Características de las Tablas Estadísticas
Pág 21
22. DISTRIBUCIÓN DE FRECUENCIAS DISCRETAS CON VARIABLE
NOMINAL
DISTRIBUCIÓN DE FRECUENCIAS CONTINUAS CON VARIABLE
NUMÉRICA
Algunos ejemplos de Tablas Estadísticas con Distribución de Frecuencia
Pág 22
DISTRIBUCIÓN DE FRECUENCIAS VARIABLES NUMÉRICAS NO
AGRUPADAS
Dato
23. CRITERIO
INTERVALO DE
CLASE
MARCA DE
CLASE
FRECUENCIA
SIMPLE
FRECUENCIA
ACUMULADA
FRECUENCIA
RELATIVA
% DE
FRECUENCIA
SIMPLE
% DE
FRECUENCIA
ACUMULADA
¿Qué es?
Clasifica los
datos en un
conjunto y esta
agrupación
depende
del total de
observaciones
Es el punto
de
referencia
de la clase
Cantidad de
datos que se
encuentran
en cada
intervalo de
clase
Es la suma
escalonada de
cada clase para
ver el
comportamient
o de los datos
en función de
la repetición de
los mismos
Es el
cociente
entre la
frecuencia
simple y el
número de
datos
Es la relación
porcentual de
la frecuencia
simple
Es la relación
porcentual de
la frecuencia
acumulada
¿Cómo se
calcula?
Cálculo por
medio de un
procedimiento
indicado
Conteo
Suma
escalonada
Elementos de una Distribución de Frecuencia
𝒙𝒊 =
𝑳𝒊 + 𝑳𝒇
𝟐
𝒇𝒓 =
𝒇𝒊
𝒏 %𝑭𝒊 =
𝑭𝒊
𝒏
∗ 𝟏𝟎𝟎
%𝒇𝒊 =
𝒇𝒊
𝒏
∗ 𝟏𝟎𝟎
LVLG-sept2020 Pág 23
24. Distribución de Frecuencia
INTERVALO
DE CLASE
MARCA DE
CLASE
FRECUENCIA
SIMPLE
FRECUENCIA
ACUMULADA
FRECUENCIA
RELATIVA
% DE
FRECUENCIA
SIMPLE
% DE
FRECUENCIA
ACUMULADA
Cálculo --->
Conteo Suma
escalonada
Li - Lf xi fi Fi Fr %fi %Fi
8,0 - 8,2 8,1 1 1 0,008 0,833 0,833
8,2 - 8,4 8,3 8 1+8= 9 0,067 6,667 7,500
8,4 - 8,6 8,5 15 9+15= 24 0,125 12,500 20,000
8,6 - 8,8 8,7 25 24+25= 49 0,208 20,833 40,833
8,8 - 9,0 8,9 31 49+31= 80 0,258 25,833 66,667
9,0 - 9,2 9,1 22 80+22= 102 0,183 18,333 85,000
9,2 - 9,4 9,3 12 102+12= 114 0,100 10,000 95,000
9,4 - 9,6 9,5 2 114+2= 116 0,017 1,667 96,667
9,6 - 9,8 9,7 4 116+4= 120 0,033 3,333 100,000
n=120 (n° de datos) 1,000 100,000
TITULO: MEDIDAS DE EL INSTRUMENTO “V” DE UNA MUESTRA DE CONTROL DE CALIDAD
EN LA FABRICA “PG” (EN MM)
𝒙𝒊 =
𝑳𝒊 + 𝑳𝒇
𝟐
𝒇𝒓 =
𝒇𝒊
𝒏 %𝑭𝒊 =
𝑭𝒊
𝒏
∗ 𝟏𝟎𝟎
%𝒇𝒊 =
𝒇𝒊
𝒏
∗ 𝟏𝟎𝟎
Pág 24
25. Interpretación de la Tabla Distribución de Frecuencia
Li - Lf xi fi Fi Fr %fi %Fi
8,0 - 8,2 8,1 1 1 0,008 0,833 0,833
8,2 - 8,4 8,3 8 9 0,067 6,667 7,500
8,4 - 8,6 8,5 15 24 0,125 12,500 20,000
8,6 - 8,8 8,7 25 49 0,208 20,833 40,833
8,8 - 9,0 8,9 31 80 0,258 25,833 66,667
9,0 - 9,2 9,1 22 102 0,183 18,333 85,000
9,2 - 9,4 9,3 12 114 0,100 10,000 95,000
9,4 - 9,6 9,5 2 116 0,017 1,667 96,667
9,6 - 9,8 9,7 4 120 0,033 3,333 100,000
n=120 1,000 100,000
MEDIDAS DE EL INSTRUMENTO “V” DE UNA MUESTRA DE CONTROL DE CALIDAD EN LA FABRICA “PG” (EN MM)
ALGUNAS LECTURAS DE LA TABLA:
1) Se observa que la mayor cantidad de datos se encuentra en el intervalo de 8,8 a 9,0
2) En este intervalo se encuentra el 31 datos que representa el 25,833% de total, es decir un poco
más de la cuarta parte del total de los datos
3) El primer intervalo que va entre 8,0 y 8,2 representa la menor cantidad de datos (sólo un dato)
y le que corresponde a 0,833%
4) Hasta el intervalo 8,8 a 9,0 va el 66,667%, es decir va más de la mitad de los datos
5) El segundo intervalo con mayor números de datos es el que va desde 8,6 a 8,8, que son 25 datos
y representa 20,833%
Pág 25o
26. GRÁFICOS
Histograma
(Gráfico de
Barra)
Polígono y Ojiva
(Gráficos de línea)
Gráfico Circular
o Pastel
Pictogramas
(Gráficos con
dibujos)
Tipos
Características
Título y Fuente
Idea Rápida y de fácil
comprensión
Elaboración
sencilla
Características de los Gráficos Estadísticos
Pág 26
27. Histograma
Es un gráfico de barra donde se coloca en el eje de las abscisas (eje horizontal o eje “x”) los límites de los
intervalos de clase y en el eje de las ordenadas (eje vertical o eje “y”) la frecuencia simple de cada clase.
Cada gráfico debe llevar su título así como la fuente de donde se tomó
Fuente: Elaboración propia a partir de los datos obtenidos en noviembre 2019
Pág 27
0
5
10
15
20
25
30
35
8,0 - 8,2 8,2 - 8,4 8,4 - 8,6 8,6 - 8,8 8,8 - 9,0 9,0 - 9,2 9,2 - 9,4 9,4 - 9,6 9,6 - 9,8
Dimensiones del instrumento “v” obtenidas de una muestra del
control de calidad de la fábrica “PG”
Series2
28. Interpretación de la Tabla Distribución de Frecuencia
Algunas lecturas del Histograma:
La mayor cantidad de datos se encuentra en el intervalo de 8,8 a 9,0
El primer intervalo que va entre 8,0 y 8,2 representa la menor cantidad de datos
La distribución de los datos se asemeja a una distribución normal, es decir va
ascendiendo para luego descender aunque presenta un leve crecimiento en el último
intervalo
Aunque el comportamiento de la distribución no es simétrica se acerca a ello
Pág 28
0
5
10
15
20
25
30
35
8,0 - 8,2 8,2 - 8,4 8,4 - 8,6 8,6 - 8,8 8,8 - 9,0 9,0 - 9,2 9,2 - 9,4 9,4 - 9,6 9,6 - 9,8
Dimensiones del instrumento “v” obtenidas de una muestra
del control de calidad de la fábrica “PG”
Series2
29. 0
5
10
15
20
25
30
35
8,0 - 8,2 8,2 - 8,4 8,4 - 8,6 8,6 - 8,8 8,8 - 9,0 9,0 - 9,2 9,2 - 9,4 9,4 - 9,6 9,6 - 9,8 9,8 - 10,0
Dimensiones del instrumento “v” obtenidas de una
muestra del control de calidad de la fábrica “PG”
Es un gráfico de línea donde se coloca en el eje de las abscisas (eje horizontal o eje “x”) la marca de clase
(punto medio entre los limites de cada intervalo de clase ) y en el eje de las ordenadas (eje vertical o eje
“y”) la frecuencia simple de cada clase.
Fuente: Elaboración propia a partir de los datos obtenidos en noviembre 2019
Pág 29
30. 0
5
10
15
20
25
30
35
8,0 - 8,2 8,2 - 8,4 8,4 - 8,6 8,6 - 8,8 8,8 - 9,0 9,0 - 9,2 9,2 - 9,4 9,4 - 9,6 9,6 - 9,8
Dimensiones del instrumento “v” obtenidas de una muestra
del control de calidad de la fábrica “PG”
Histograma
Poligono de Frecuencia
Histograma y Polígono de Frecuencia
Ambas gráficas se pueden presentar en un solo eje de coordenadas como se muestra a continuación
Fuente: Elaboración propia a partir de los datos obtenidos en noviembre 2019
Pág 30
31. 0
20
40
60
80
100
120
140
8,0 - 8,2 8,2 - 8,4 8,4 - 8,6 8,6 - 8,8 8,8 - 9,0 9,0 - 9,2 9,2 - 9,4 9,4 - 9,6 9,6 - 9,8
frecuencia
Acumilada
Marca de clase
Es un gráfico de línea donde se coloca en el eje de las abscisas (eje horizontal o eje “x”) la marca de clase (punto medio
entre los limites de cada intervalo) de clase y en el eje de las ordenadas (eje vertical o eje “y”) la frecuencia acumulada de
cada clase.
Dimensiones del instrumento “v” obtenidas de una muestra del control de calidad de la fábrica “PG”
Fuente: Elaboración propia a partir de los datos obtenidos en noviembre 2019
Pág 31
32. Gráfico Circular
Fuente: Elaboración propia a partir de los datos obtenidos en noviembre 2019
Llamado también gráfico pastel o de sectores, en el se pueden presentar los datos que se requieran como
los porcentajes o frecuencias simples en relación con los límites de clases o marcas de clases..
DIMENSIONES DEL INSTRUMENTO “V” OBTENIDAS DE UNA MUESTRA DEL CONTROL DE CALIDAD DE LA
FÁBRICA “PG”
Pág 32
1%
7%
12%
21%
26%
18%
10%
2%
3%
8,0 - 8,2
8,2 - 8,4
8,4 - 8,6
8,6 - 8,8
8,8 - 9,0
9,0 - 9,2
9,2 - 9,4
9,4 - 9,6
9,6 - 9,8
34. Procedimiento para Agrupar Datos
1) Contar el numero de datos de la población, es decir N
2) Buscar los valores máximos y mínimos (Xmáx y Xmín)
3) Calcular la Amplitud o Rango que es la resta entre el valor máximo y el
mínimo (R= Xmáx - Xmín)
4) Determinar el número de intervalo por medio de la Regla de Sturges
que es k=1+3,3* log(N)
5) Determinar la longitud del intervalo o Ancho del intervalo (c=R/k)
6) Hallar los límites inferiores de cada clase (Xmin + c)
7) Hallar los limites superiores de cada clase ( Intervalo semi – abierto, es
el mismo que el anterior, o Cerrado que será el consecutivo del limite
superior anterior)
8) Construcción de la Tabla
Pág 34
Para facilitar los cálculos se puede apoyar en la herramienta de Microsoft EXCEL.
Para ello los invito a revisar el siguiente link que les explica de manera sencilla los
procedimientos referidos a esta primera parte del encuentro.
https://www.youtube.com/watch?v=XDUndiON7fk
35. 68 84 75 82 68 90 62 88 76 93
73 79 88 73 60 93 71 59 85 75
61 65 75 87 74 62 95 78 63 72
66 78 82 75 94 77 69 74 68 60
96 78 89 61 75 95 60 79 83 71
79 62 67 97 78 85 76 65 71 75
65 80 73 57 88 78 62 76 53 74
86 67 73 81 72 63 76 75 85 77
Se solicita que realice: (Use 4 decimales)
1) Construya la tabla (intervalos de clase semi-abiertos y cerrados)
2) Realice la distribución de frecuencia para ambas tablas
3) Haga el Histograma para cada distribución.
4) Trace el polígono de frecuencia para cada distribución.
5) Realice un grafico circular para cada tabla
Los siguientes datos representan los pesos en Kg de los diferentes presentaciones de Sacos de
Cemento que ensambla la Fabrica H según la frecuencia de una línea de producción 2 en un periodo
entre las 12m y 3pm.
Ejercicio práctico:
LVLG-sept2020 Pág 35
36. 1ra Respuesta del Ejercicio:
Se ordenan los datos en forma creciente para facilitar la agrupación
53 62 65 71 73 75 77 79 85 90
57 62 66 71 74 75 78 80 85 93
59 62 67 71 74 75 78 81 86 93
60 62 67 72 74 76 78 82 87 94
60 63 68 72 75 76 78 82 88 95
60 63 68 73 75 76 78 83 88 95
61 65 68 73 75 76 79 84 88 96
61 65 69 73 75 77 79 85 89 97
Siguiendo los pasos sugeridos en la parte anterior se tienen
1) Número de datos 80, n=80
2) Valor Máximo es 97, el valor Mínimo es 53
3) R= 97- 53= 44
4) Se Determina el número de intervalo por medio de la Regla de Sturges que es k=1+3,3* log(80)≈ 8
5) La longitud o amplitud del intervalo c= 44/8, c = 5,5≈ 6
6) En ambas tablas el primer intervalo ira entre 53 a 59 (53 + 6= 59)
7) En la tabla de intervalos semi – abierto (es el mismo que el anterior) es decir el próximo intervalo será 59 a 65.
Mientras que en la otra de intervalos cerrados será 60 a 66. Con esta aclaratoria se continua construyendo todos
los intervalos de clase
8) Una vez realizado todos los intervalos de cada tabla se procede a contar los elementos contenidos en cada clase,
es decir se coloca la frecuencia de cada intervalo según los datos que incluya. Recordando que al ser semi-abierto,
el valor no cierra en el límite derecho, es decir entre [53 a 59), se cuentan los valores de 53,54,55,56,57 y 58 no se
incluye el 59 porque corresponde al próximo intervalo. Pero en el cerrado incluye todos los valores incluso el 59
Pág 36
37. INTERVALOS Frecuencia
simple
Li - Lf fi
1 53 59 3
2 59 65 14
3 65 71 10
4 71 77 22
5 77 83 13
6 83 89 10
7 89 95 6
8 95 101 2
n= 80
Tabla n° 1 de Intervalos semi-abierto
INTERVALOS Frecuencia
simple
Li Lf fi
1 53 59 3
2 60 66 15
3 67 73 15
4 74 80 25
5 81 87 10
6 88 94 8
7 95 101 4
n= 80
Tabla n° 2 de Intervalos cerrados
Según los pasos realizados anteriormente queda las siguientes tablas:
Pág 37
1ra Respuesta del Ejercicio:
40. Pág 40
3ra Respuesta del Ejercicio:
0
5
10
15
20
25
53-59 59-65 65-71 71-77 77-83 83-89 89-95 95-101
Histograma que representa una muestra de los Sacos de Cemento (Kg) de la
Fabrica H en la línea de producción 2
Histograma
Histograma de la Tabla n°1
Fuente: Lugo, 2020
41. 0
5
10
15
20
25
30
53-59 60-66 67-73 74-80 81-87 88-94 95-101
Histograma que representa una muestra de los Sacos de
Cemento (Kg) de la Fabrica H en la línea de producción
2
Histograma
3ra Respuesta del Ejercicio:
Pág 41
Histograma de la Tabla n°2
Fuente: Lugo, 2020
42. Pág 42
4ta Respuesta del Ejercicio:
0
5
10
15
20
25
56 62 68 74 80 86 92 98
Polígono de Frecuencia que representa una muestra de los
Sacos de Cemento (Kg) de la Fabrica H en la línea de
producción 2
Polígono de
Frecuencia
Polígono de Frecuencia de la Tabla n°1
Fuente: Lugo, 2020
43. Pág 43
4ta Respuesta del Ejercicio:
0
5
10
15
20
25
30
56 63 70 77 84 91 98
Polígono de Frecuencia que representa una muestra de los Sacos de Cemento
(Kg) de la Fabrica H en la línea de producción 2
Poligono de
frecuencia
Polígono de Frecuencia de la Tabla n°2
Fuente: Lugo, 2020
44. LVLG-sept2020 Pág 44
5ta Respuesta del Ejercicio:
4%
17%
12%
27%
16%
13%
8% 3%
Gráfico que representa una muestra de los Sacos de Cemento (Kg) de
la Fabrica H en la línea de producción 2
53-59 59-65 65-71 71-77 77-83 83-89 89-95 95-101
Gráfico Pastel de la Tabla n°1
Fuente: Lugo, 2020
45. 5ta Respuesta del Ejercicio:
4%
19%
19%
31%
12%
10%
5%
Gráfico que representa una muestra de los Sacos de Cemento (Kg)
de la Fabrica H en la línea de producción 2
53-59 60-66 67-73 74-80 81-87 88-94 95-101
Gráfico Pastel de la Tabla n°2
Fuente: Lugo, 2020
Pág 45