The document discusses the three main aspects used to describe data distributions: central tendency, spread, and symmetry. It defines each one as follows:
- Central tendency tells you where the data are clustering or centering, and looks for words like average, mean, median, most common, or center point.
- Spread tells you how spread out the data are from one another, and looks for words like spread, difference, deviation, range, or variation.
- Symmetry tells you the shape of the distribution, and looks for words describing the shape or whether most data are in the center, off to one side, or evenly distributed.
Central tendency spread - symmetry (2.0)Ken Plummer
The document discusses central tendency, spread, and symmetry when analyzing distributions. It explains that a question deals with central tendency if it focuses on measures of centeredness like average or median. A question deals with spread if it focuses on measures of dispersion like range or variation. A question deals with symmetry if it focuses on the shape of the distribution and whether it is skewed or normal. Examples of distributions with different shapes are provided to illustrate these concepts.
Central tendency spread - symmetry (4.0)Ken Plummer
The document discusses different types of descriptions for distributions including central tendency, spread, and symmetry. It provides examples of questions that would require analyzing the central tendency, spread, or symmetry of a distribution to answer. The document is intended to help the reader identify which type of distribution description is most relevant for answering different types of questions.
This document provides an overview of an ACT preparation course. It begins with an introduction and table of contents. It then covers what the ACT is, the different sections of the ACT, how the ACT is scored, and what will be covered in this preparation course. The course aims to improve scores through full-length practice tests, PowerPoint slides on content and strategies, and targeted exercises. Students are advised to take thorough notes, complete all assignments, and practice frequently in order to maximize their preparation.
The document discusses strategies for the science section of the ACT exam. It explains that the science section contains 6 passages with 6-7 questions each, for a total of 40 questions to be completed in 35 minutes. There are three main types of passages: data representation, research summaries, and conflicting viewpoints. The document advises test takers to classify the passage types and answer the easiest questions first using different strategies for different passages in order to maximize scores within the time limit.
How To Write A Three Part Thesis Statement by Mrs. ScruggsWendy Scruggs
The document provides guidance on constructing a good thesis statement, noting it should be arguable, have three parts, and use parallel structure. It recommends choosing a topic that requires proving, then identifying three key points to argue. As an example thesis, it suggests arguing social networks are good for society by showing they connect people, educate people, and entertain people. Parallel structure is achieved by using the same verb form to start each key point.
The document outlines strategies for different types of science passages that may appear on the ACT exam. It discusses strategies for data representation, research summary, and conflicting viewpoints passages. For data representation passages, it recommends starting with the questions rather than reading the passage, using the question to find the relevant part of the data to answer it. For research summary passages, it suggests reading the background information and notes before proceeding to questions. For conflicting viewpoints passages, the strategy is to identify which questions refer to each viewpoint and answer those separately before combining information.
The document summarizes what the author learned about quantitative data analysis, line graphs, and applying line graphs to experiments from science class. The author learned to be specific, use correct units, and understand questions in order to accurately answer them. They also learned about independent and dependent variables, parts of line graphs like trend lines, and how to create and analyze line graphs from experimental data. The author was able to apply these skills by creating and analyzing their own line graphs in class assignments and experiments.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
Central tendency spread - symmetry (2.0)Ken Plummer
The document discusses central tendency, spread, and symmetry when analyzing distributions. It explains that a question deals with central tendency if it focuses on measures of centeredness like average or median. A question deals with spread if it focuses on measures of dispersion like range or variation. A question deals with symmetry if it focuses on the shape of the distribution and whether it is skewed or normal. Examples of distributions with different shapes are provided to illustrate these concepts.
Central tendency spread - symmetry (4.0)Ken Plummer
The document discusses different types of descriptions for distributions including central tendency, spread, and symmetry. It provides examples of questions that would require analyzing the central tendency, spread, or symmetry of a distribution to answer. The document is intended to help the reader identify which type of distribution description is most relevant for answering different types of questions.
This document provides an overview of an ACT preparation course. It begins with an introduction and table of contents. It then covers what the ACT is, the different sections of the ACT, how the ACT is scored, and what will be covered in this preparation course. The course aims to improve scores through full-length practice tests, PowerPoint slides on content and strategies, and targeted exercises. Students are advised to take thorough notes, complete all assignments, and practice frequently in order to maximize their preparation.
The document discusses strategies for the science section of the ACT exam. It explains that the science section contains 6 passages with 6-7 questions each, for a total of 40 questions to be completed in 35 minutes. There are three main types of passages: data representation, research summaries, and conflicting viewpoints. The document advises test takers to classify the passage types and answer the easiest questions first using different strategies for different passages in order to maximize scores within the time limit.
How To Write A Three Part Thesis Statement by Mrs. ScruggsWendy Scruggs
The document provides guidance on constructing a good thesis statement, noting it should be arguable, have three parts, and use parallel structure. It recommends choosing a topic that requires proving, then identifying three key points to argue. As an example thesis, it suggests arguing social networks are good for society by showing they connect people, educate people, and entertain people. Parallel structure is achieved by using the same verb form to start each key point.
The document outlines strategies for different types of science passages that may appear on the ACT exam. It discusses strategies for data representation, research summary, and conflicting viewpoints passages. For data representation passages, it recommends starting with the questions rather than reading the passage, using the question to find the relevant part of the data to answer it. For research summary passages, it suggests reading the background information and notes before proceeding to questions. For conflicting viewpoints passages, the strategy is to identify which questions refer to each viewpoint and answer those separately before combining information.
The document summarizes what the author learned about quantitative data analysis, line graphs, and applying line graphs to experiments from science class. The author learned to be specific, use correct units, and understand questions in order to accurately answer them. They also learned about independent and dependent variables, parts of line graphs like trend lines, and how to create and analyze line graphs from experimental data. The author was able to apply these skills by creating and analyzing their own line graphs in class assignments and experiments.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
This document provides information on the design element of shape. It discusses the three basic types of shapes - geometric, organic, and abstract. Geometric shapes are simple and well-known forms, organic shapes are found in nature, and abstract shapes have recognizable forms but are not realistic representations. The document also explores how shapes can be used in design to add interest, organize elements, and direct the eye. Positive and negative shapes as well as the spatial characteristics and psychology of different shapes are examined. Examples of shape use in design are presented, and an exercise for exploring shapes called "The Shape Game" is described.
The document summarizes blog entries and notes from a science class about quantitative data analysis and line graphs. Over several days, the student learned how to accurately answer questions using specific quantities and units, analyze graphs to identify patterns and relationships, and create line graphs to display experimental data. The student was able to apply these skills to class assignments involving data collection, graphing, and revising models based on analysis of patterns in the data.
Pranay Gupta learned about quantitative data analysis, line graphs, and applying line graphs to experiments through analyzing papers and data in class. Key lessons included being specific, using correct units, understanding questions, and accurately plotting and labeling line graphs to show trends and patterns in data. Mistakes like inconsistent tick marks and inaccurate data lines were identified. Applying these skills allows understanding various real-world graphs outside the classroom.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
This document discusses how to write a thesis statement that lists points to preview the ideas in the body paragraphs. It recommends determining an overall stance on the topic and supporting arguments. As an example, it presents a stance that school uniforms should be required and lists three sub-topics or reasons to support this: that uniforms improve student-teacher relations, encourage solidarity, and enhance school safety. It advises combining these sub-topics into a parallel structure thesis statement and writing at least one body paragraph in support of each sub-topic.
The reading section of the ACT contains 4 passages with 10 questions each, for a total of 40 questions. Test-takers have 35 minutes to complete the section. There are three main types of questions: key ideas and details, craft and structure, and integration of knowledge and ideas. To improve performance, students should practice regular reading, try different reading strategies, and take many practice reading sections. Timing is important, with 8 minutes allotted for each passage. Students should read each passage in 2-3 minutes and answer questions in the remaining 5-6 minutes. When reading, the focus should be on understanding the main idea and creating a mental map rather than memorizing details.
The blog entries summarize what the student learned about quantitative data analysis, line graphs, and applying line graphs to experiments over several class sessions. The student learned about using labels, units, the delta symbol, and organizing files. They also learned about independent and dependent variables on line graphs, parts of line graphs, and how to create and analyze line graphs from experimental data. The student discussed applying these skills to classwork and potential uses outside the classroom.
The document describes three levels of questions that can be asked about a story or text using the fairy tale Cinderella as an example. Level 1 questions ask for explicit facts stated in the text. Level 2 questions require inferences to be made by analyzing details and motives within the text. Level 3 questions go beyond the text by asking for judgments, opinions, and synthesis of ideas related to broader themes or issues.
The document provides guidance on writing a report based on a graph or diagram for IELTS Task 1. It emphasizes considering varying vocabulary, grammatical structures, organizing information coherently, focusing on both details and the overall picture, and finding suitable comparisons. The ideal time to spend is 20 minutes. Key points include interpreting data, paraphrasing, choosing relevant details, and providing an overall statement in addition to paragraph details. Templates are given for introducing trends, comparisons, and supporting details. Guidance is also provided on focusing details for different types of charts, tables, diagrams, processes, and maps.
This document provides an overview of descriptive and inferential statistics. Descriptive statistics describe features of a data set using numerical measures like the highest/lowest scores, mode, and mean. Inferential statistics draw conclusions about a larger population based on analyzing a sample, allowing inferences to be made about the broader population. The document illustrates these concepts using a spelling test data set and questions from a parent about their child's performance and how it compares more broadly.
This document provides guidance on writing analytical assignments. It discusses developing an analytical frame of mind by using techniques like suspending judgment, defining parts and relationships, making the implicit explicit, and looking for patterns. It also covers reading analytically by paraphrasing, and responding to assignments analytically by reducing scope, studying wording for unstated questions, and beginning with questions rather than answers. The overall goal is to think and write analytically rather than descriptively by identifying issues, evaluating strengths and weaknesses, considering alternatives, and challenging logic and data.
The document provides guidance on analyzing, synthesizing, and interpreting data collected during field research. It discusses focusing data analysis, choosing relevant questions and responses, and using observations/surveys/interviews to make an argument. Students are instructed to develop headings for their results based on what they will argue and make these their argument headings. They are then told to review their own data, list arguments and evidence to support each, and begin drafting one of their argument sections.
This document outlines the instructions for a take home exam in social philosophy. It is divided into 4 parts. Part 1 involves answering 3 out of 5 questions from Set A for 45% of the grade. Part 2 involves answering 4 out of 6 questions from Set B for 20% of the grade. Part 3 is completing a final report on political ideologies for 30% of the grade. Part 4 gives bonus points to the first 5 and next 5 students who submit their exams. The exam must be typed in MS Word and follow specific formatting guidelines.
This document is the introduction chapter of a textbook on probability. It introduces fundamental probability concepts like outcome spaces, events, and probability as a function of events. Probability is first discussed in the context of equally likely outcomes, where each outcome has the same probability of occurring. Two common interpretations of probability - as the long-run frequency of an event occurring or as a subjective measure of uncertainty - are also introduced. The chapter then covers probability distributions, conditional probability, independence, Bayes' rule, and sequences of events.
The document discusses several psychometric scale formats used in psychology questionnaires, including the Likert format, category format, checklists, and Q-sorts. The Likert format uses a series of attitude questions with options like strongly disagree to strongly agree. It is commonly used and was developed by Rensis Likert. The category format is similar but has more response options. Checklists and Q-sorts involve rating adjectives or statements on scales of how well they describe oneself or others.
The document discusses formative and summative assessments. It provides keys to developing quality classroom assessments, including having clear purpose and targets, sound design, effective communication, and student involvement. Seven strategies for formative assessment are outlined to help students answer where they are going, where they are now, and how to close gaps. Guidelines are offered for developing various assessment methods and test question design.
Central spread - symmetry (jejit + indepth)Ken Plummer
The document discusses different categories of methods for analyzing data distributions: central tendency, spread, and symmetry. Central tendency describes where data are clustered around the average or center. Spread describes how far data are from the middle. Symmetry describes the overall shape of the distribution. The document provides examples of questions that would require analyzing the central tendency, spread, or symmetry and instructs the reader to identify which one is most relevant. It emphasizes that every distribution has all three characteristics but the analysis may focus on one aspect.
Central spread - symmetry (jejit + indepth)Ken Plummer
The document discusses different categories of methods for analyzing data distributions: central tendency, spread, and symmetry. Central tendency describes where data are clustered around the average or center. Spread describes how far data are from the middle. Symmetry describes the overall shape of the distribution. The document provides examples of questions that would require analyzing the central tendency, spread, or symmetry and instructs the reader to identify which one is most relevant. It emphasizes that every distribution has all three characteristics but the analysis may focus on one aspect.
The document discusses central tendency and skewness. In Demo #1, it explains that the median is the best measure of central tendency for a positively skewed distribution because it is not influenced by outliers. In Demo #2, it states the mode is best for a multimodal distribution because it indicates the most frequent values. Demo #3 explains that if the mean is lower than the median, the distribution is negatively skewed.
The document presents 7 practice problems about calculating different statistics from data sets. The problems involve comparing average cyberbullying incidents between grade levels, comparing test score ranges between therapy groups, calculating average blood pressure, examining exam score distributions, determining income variation across a school district, and describing comfort level distributions for different faculty groups. Central tendency, spread, and symmetry are the key statistical concepts addressed.
This document provides information on the design element of shape. It discusses the three basic types of shapes - geometric, organic, and abstract. Geometric shapes are simple and well-known forms, organic shapes are found in nature, and abstract shapes have recognizable forms but are not realistic representations. The document also explores how shapes can be used in design to add interest, organize elements, and direct the eye. Positive and negative shapes as well as the spatial characteristics and psychology of different shapes are examined. Examples of shape use in design are presented, and an exercise for exploring shapes called "The Shape Game" is described.
The document summarizes blog entries and notes from a science class about quantitative data analysis and line graphs. Over several days, the student learned how to accurately answer questions using specific quantities and units, analyze graphs to identify patterns and relationships, and create line graphs to display experimental data. The student was able to apply these skills to class assignments involving data collection, graphing, and revising models based on analysis of patterns in the data.
Pranay Gupta learned about quantitative data analysis, line graphs, and applying line graphs to experiments through analyzing papers and data in class. Key lessons included being specific, using correct units, understanding questions, and accurately plotting and labeling line graphs to show trends and patterns in data. Mistakes like inconsistent tick marks and inaccurate data lines were identified. Applying these skills allows understanding various real-world graphs outside the classroom.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
The document summarizes a student's blog entries about learning quantitative data analysis and line graphs in their science class. Over several entries, the student discusses key lessons learned each day, such as how to accurately answer questions using proper units and definitions, how to construct and interpret line graphs, and how to apply these skills in class experiments and analyses. The student also considers how these skills can be used outside the classroom, such as accurately communicating quantitative information to others.
This document discusses how to write a thesis statement that lists points to preview the ideas in the body paragraphs. It recommends determining an overall stance on the topic and supporting arguments. As an example, it presents a stance that school uniforms should be required and lists three sub-topics or reasons to support this: that uniforms improve student-teacher relations, encourage solidarity, and enhance school safety. It advises combining these sub-topics into a parallel structure thesis statement and writing at least one body paragraph in support of each sub-topic.
The reading section of the ACT contains 4 passages with 10 questions each, for a total of 40 questions. Test-takers have 35 minutes to complete the section. There are three main types of questions: key ideas and details, craft and structure, and integration of knowledge and ideas. To improve performance, students should practice regular reading, try different reading strategies, and take many practice reading sections. Timing is important, with 8 minutes allotted for each passage. Students should read each passage in 2-3 minutes and answer questions in the remaining 5-6 minutes. When reading, the focus should be on understanding the main idea and creating a mental map rather than memorizing details.
The blog entries summarize what the student learned about quantitative data analysis, line graphs, and applying line graphs to experiments over several class sessions. The student learned about using labels, units, the delta symbol, and organizing files. They also learned about independent and dependent variables on line graphs, parts of line graphs, and how to create and analyze line graphs from experimental data. The student discussed applying these skills to classwork and potential uses outside the classroom.
The document describes three levels of questions that can be asked about a story or text using the fairy tale Cinderella as an example. Level 1 questions ask for explicit facts stated in the text. Level 2 questions require inferences to be made by analyzing details and motives within the text. Level 3 questions go beyond the text by asking for judgments, opinions, and synthesis of ideas related to broader themes or issues.
The document provides guidance on writing a report based on a graph or diagram for IELTS Task 1. It emphasizes considering varying vocabulary, grammatical structures, organizing information coherently, focusing on both details and the overall picture, and finding suitable comparisons. The ideal time to spend is 20 minutes. Key points include interpreting data, paraphrasing, choosing relevant details, and providing an overall statement in addition to paragraph details. Templates are given for introducing trends, comparisons, and supporting details. Guidance is also provided on focusing details for different types of charts, tables, diagrams, processes, and maps.
This document provides an overview of descriptive and inferential statistics. Descriptive statistics describe features of a data set using numerical measures like the highest/lowest scores, mode, and mean. Inferential statistics draw conclusions about a larger population based on analyzing a sample, allowing inferences to be made about the broader population. The document illustrates these concepts using a spelling test data set and questions from a parent about their child's performance and how it compares more broadly.
This document provides guidance on writing analytical assignments. It discusses developing an analytical frame of mind by using techniques like suspending judgment, defining parts and relationships, making the implicit explicit, and looking for patterns. It also covers reading analytically by paraphrasing, and responding to assignments analytically by reducing scope, studying wording for unstated questions, and beginning with questions rather than answers. The overall goal is to think and write analytically rather than descriptively by identifying issues, evaluating strengths and weaknesses, considering alternatives, and challenging logic and data.
The document provides guidance on analyzing, synthesizing, and interpreting data collected during field research. It discusses focusing data analysis, choosing relevant questions and responses, and using observations/surveys/interviews to make an argument. Students are instructed to develop headings for their results based on what they will argue and make these their argument headings. They are then told to review their own data, list arguments and evidence to support each, and begin drafting one of their argument sections.
This document outlines the instructions for a take home exam in social philosophy. It is divided into 4 parts. Part 1 involves answering 3 out of 5 questions from Set A for 45% of the grade. Part 2 involves answering 4 out of 6 questions from Set B for 20% of the grade. Part 3 is completing a final report on political ideologies for 30% of the grade. Part 4 gives bonus points to the first 5 and next 5 students who submit their exams. The exam must be typed in MS Word and follow specific formatting guidelines.
This document is the introduction chapter of a textbook on probability. It introduces fundamental probability concepts like outcome spaces, events, and probability as a function of events. Probability is first discussed in the context of equally likely outcomes, where each outcome has the same probability of occurring. Two common interpretations of probability - as the long-run frequency of an event occurring or as a subjective measure of uncertainty - are also introduced. The chapter then covers probability distributions, conditional probability, independence, Bayes' rule, and sequences of events.
The document discusses several psychometric scale formats used in psychology questionnaires, including the Likert format, category format, checklists, and Q-sorts. The Likert format uses a series of attitude questions with options like strongly disagree to strongly agree. It is commonly used and was developed by Rensis Likert. The category format is similar but has more response options. Checklists and Q-sorts involve rating adjectives or statements on scales of how well they describe oneself or others.
The document discusses formative and summative assessments. It provides keys to developing quality classroom assessments, including having clear purpose and targets, sound design, effective communication, and student involvement. Seven strategies for formative assessment are outlined to help students answer where they are going, where they are now, and how to close gaps. Guidelines are offered for developing various assessment methods and test question design.
Central spread - symmetry (jejit + indepth)Ken Plummer
The document discusses different categories of methods for analyzing data distributions: central tendency, spread, and symmetry. Central tendency describes where data are clustered around the average or center. Spread describes how far data are from the middle. Symmetry describes the overall shape of the distribution. The document provides examples of questions that would require analyzing the central tendency, spread, or symmetry and instructs the reader to identify which one is most relevant. It emphasizes that every distribution has all three characteristics but the analysis may focus on one aspect.
Central spread - symmetry (jejit + indepth)Ken Plummer
The document discusses different categories of methods for analyzing data distributions: central tendency, spread, and symmetry. Central tendency describes where data are clustered around the average or center. Spread describes how far data are from the middle. Symmetry describes the overall shape of the distribution. The document provides examples of questions that would require analyzing the central tendency, spread, or symmetry and instructs the reader to identify which one is most relevant. It emphasizes that every distribution has all three characteristics but the analysis may focus on one aspect.
The document discusses central tendency and skewness. In Demo #1, it explains that the median is the best measure of central tendency for a positively skewed distribution because it is not influenced by outliers. In Demo #2, it states the mode is best for a multimodal distribution because it indicates the most frequent values. Demo #3 explains that if the mean is lower than the median, the distribution is negatively skewed.
The document presents 7 practice problems about calculating different statistics from data sets. The problems involve comparing average cyberbullying incidents between grade levels, comparing test score ranges between therapy groups, calculating average blood pressure, examining exam score distributions, determining income variation across a school district, and describing comfort level distributions for different faculty groups. Central tendency, spread, and symmetry are the key statistical concepts addressed.
This document discusses non-experimental research designs such as surveys, correlational studies, and quasi-experiments. It notes that these designs are sometimes necessary when fully controlled experiments are not possible due to limitations in the issue being studied or available resources. Surveys involve collecting self-report data through questionnaires or interviews, while correlational designs examine relationships between two or more variables. Quasi-experiments are similar to true experiments but have an inherent confounding variable because the researcher cannot directly manipulate the independent variable. The document provides details on how to properly design and conduct survey research, including best practices for question construction, response scales, sampling methods, and data analysis.
Survey Methodology and Questionnaire Design Theory Part IQualtrics
Do you know what's going on in your respondents' heads as they take your survey? How can you design your questionnaire to collect better data? Understanding the answers to these questions can help you design surveys that collect high quality insights you can depend on.
Dave Vannette, principal research scientist at Qualtrics, shares his best hacks for designing surveys that will help you get quality data. In this presentation, Dave also highlights what your respondents are thinking when they take your surveys, and how your survey design can affect the responses you collect.
This document provides an overview of best practices for writing effective surveys and questionnaires. It discusses key concepts like the difference between surveys and censuses, and surveys and questionnaires. It outlines common issues like sampling, design, analysis, question wording, response methods, and question ordering that should be considered. Best practices are presented such as clearly defining the research goal, verifying that a survey is needed, pretesting the questionnaire, and getting feedback from others. The goal is to help people construct surveys that accurately measure constructs and avoid biases.
This document provides an overview of best practices for writing effective surveys and questionnaires. It discusses key concepts like the difference between surveys and censuses, and surveys and questionnaires. It outlines common issues like sampling, design, analysis, question wording, response methods, and question ordering that should be considered. Best practices are presented such as clearly defining the research goal, verifying that a survey is needed, pretesting the questionnaire, and getting feedback from others. The goal is to help people construct surveys that accurately measure constructs and avoid biases.
This document discusses research methods and instrument design. It covers sampling procedures, data gathering, research instruments, and statistical analysis. It focuses on questionnaire design, providing tips for writing clear, unbiased questions. These tips include using simple language, short questions, common terms, and scales. The document emphasizes pretesting questionnaires to identify issues before full data collection.
The document discusses survey questionnaires. A questionnaire is a form used to collect data in a survey by asking respondents questions. It serves four purposes: to collect appropriate data, make data comparable and analyzable, minimize bias, and make questions engaging. The document provides guidance on developing a questionnaire, including deciding what information is needed, defining the target population, formulating questions to answer research questions, organizing questions logically, consulting experts, piloting the questionnaire, and adhering to ethical standards. It also discusses open-ended versus closed-ended questions and examples of each.
These introductory statistics slides will give you a basic understanding of statistics, types of statistics, variable and its types, the levels of measurements, data collection techniques, and types of sampling.
Questionnaires are useful tools for researchers to collect data directly from people. They can be used to measure knowledge, preferences, and beliefs. However, respondents must be willing to cooperate and provide honest answers. There are different types of question formats, including direct vs indirect, specific vs non-specific, and questions of fact vs opinion. When constructing a questionnaire, researchers must specify the variables being investigated, choose an appropriate question and response format based on the type of data needed, and consider factors like response flexibility and potential for bias.
This document provides an overview of key aspects of survey design, including questionnaire development, question styles, response formats, and implementation issues. The objectives are to introduce rigorous planning and development of research questionnaires, including formulating questions, expanding the questionnaire based on study objectives, and finalizing the questionnaire through pre-testing and pilot testing. Proper survey design is important to gather high-quality data and ensure validity and reliability of results.
Figure 1. Father Attachment Anxiety Model Introduce ChereCheek752
Figure 1. Father Attachment Anxiety Model
Introduce your study here.
Introduction
Hypotheses go here
H1:
H2:
H3:
Method
Add your references here.
Hypotheses
Results:
What are the results of your study?
The results of this study indicated one significant indirect relationship between _____ and ______ (p= .#, co= .#) when mediated by variable. Though finding no significant correlations between either _____ and _____ (p = .#, co= .#) was contrary to previous findings, this study’s results are consistent with the existing literature which states . . .
Overall, _____ was / was not found to be significant mediator between ____ and ___. This may suggest . . .
These findings do/do not provide support for ____.
Limitations & Future Research:
Describe any limitations
What directions should future research consider?
*make sure the edges of these boxes align
Discussion
Sample
Father-Attachment Anxiety
DASSDEP
(Depression)
Externally-Oriented Thinking
Difficulty Identifying Feelings
TOSCASHA
(Shame-Proneness)
Difficulty Describing Feelings
a2=.4015, p=.0257
d2=.0896, p=.0000
d1=.0976, p=.0000
d4=-.0302, p=.1293
d3=-.0816, p=.3969
a1=.0796, p=.5416
a3=-.0605, p=.5848
c’=.0377, p=-.0102
b3=.0073, p=.3526
b1=.0378, p=.0000
b4=-.0034, p=.4792
b2=.0038, p=.1611
a1=-.1309, p=.2624
DASSDEP
(Depression)
Father-Attachment Avoidance
Externally-Oriented Thinking
Difficulty Identifying Feelings
TOSCASHA
(Shame-Proneness)
Difficulty Describing Feelings
a2=.0044, p=.9748
d2=.0905, p=.0000
d1=.1060, p=.0000
d4=-.0266, p=.1738
b3=.0094, p=.2235
b1=.0324, p=.0000
b4=-.0025, p=.6023
d3=.0680, p=.4264
a3=-.1156, p=.2408
c’=-.0122, p=.3613
b2=.0057, p=.0340
a1=.4125, p=.0073
DASSDEP
(Depression)
Mother-Attachment Anxiety
Externally-Oriented Thinking
Difficulty Identifying Feelings
TOSCASHA
(Shame-Proneness)
Difficulty Describing Feelings
a2=-.0743, p=.7256
d2=.0905, p=.0000
d1=.1060, p=.0000
d4=-.0266, p=.1738
b3=.0094, p=.2235
b1=.0324, p=.0000
b4=-.0025, p=.6023
d3=.1136, p=.3119
a3=-.0695, p=.5913
c’=.0040, p=.8191
b2=.0057, p=.0340
DASSDEP
(Depression)
Mother-Attachment Avoidance
Externally-Oriented Thinking
Difficulty Identifying Feelings
TOSCASHA
(Shame-Proneness)
Difficulty Describing Feelings
a2=.1207, p=.5041
d2=.0896, p=.0000
d1=.0976, p=.0000
d4=-.0302, p=.1293
b3=.0073, p=.3526
b1=.0378, p=.0000
b4=-.0034, p=.4792
d3=.3524, p=.0003
a1=.2611, p=.0462
a3=.2522, p=.0233
c’=.0318, p=.0318
b2=.0038, p=.1611
Concise and Informative Title
Names of Contributors
Measures
Describe your measures here.
Relationships Structures (ECR)
This nine-item questionnaire measures attachment styles in mother and father relationships. The scale analyzes both anxious and avoidant attachment styles. A higher score on this scale indicates a more insecure relationship pattern.
The Toronto Alexithymia Scale (TAS-20)
This scale measures the condition of alexithymia according to alexithymia total, diffi ...
The document discusses descriptive statistical analysis techniques used in marketing research such as measures of central tendency, variability, frequency distributions, and hypothesis testing. It provides examples of how to calculate the mean, median, mode, and range of a data set and construct a frequency distribution table. The document also demonstrates how to conduct a hypothesis test to determine if a sample provides sufficient evidence to support or reject a hypothesized population parameter value.
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
Here are some potential issues with this 11-point satisfaction scale:
- Partially labeled scales can lead to different interpretations of the scale points.
- Forced distribution with a neutral point may push respondents towards the middle who don't truly feel neutral.
- Lumping the 7+ responses together obscures variation in attitudes above satisfied.
- Subtracting below 6 from above 9 assumes equal intervals between scale points which may not reflect how respondents conceptualize satisfaction.
- Unipolar scales can't capture dissatisfaction which is important information. A bipolar scale may better measure the construct.
In summary, this scale has response option and analysis issues that could undermine the validity and reliability of the satisfaction measure.
When you are working on the Inferential Statistics Paper I want yo.docxalanfhall8953
When you are working on the Inferential Statistics Paper I want you to format your paper with the following information
I. Introduction – What are inferential statistics and what is the research problem and hypothesis of the article?
II. Methods – Who are the subjects and variables within the article?
III. Results – What is the statistical analysis used, why were these tests chosen? What were the results of these tests and what do they mean?
IV. Discussion – What were the strengths of this article? What would you have done differently in terms of variables and statistical analysis? Why?
V. Conclusion – Reiterate the introduction and include relevant information that answers the questions regarding the hypothesis.
`
Read: Chapter 3 and 4 of Statistics for the Behavioral and Social Sciences.
Participate in One discussion.
Discussion 1 –Standard Normal Distribution– This allows you to look at any data set into the standard distribution form.
Quiz – Hypothesis testing
Submit your Inferential Statics Article Critique – Read Differential Effects of a Body Image Exposure Session on Smoking Urge Between Physically Active and Sedentary Female Smokers. What is the research question and hypothesis? Identify what variables were present, what inferential statistics were used and why, and if proper research methods were used. See grading rubric for full details.
Discussion Post Expectations:
Your initial post (your answer) is due by Day 3 (Thursday) of this week for Discussion 1.
When grading the Standard Normative Distribution discussion I will be looking for your answer to contain:
Week 2 Discussion 1 Board Rubric
Earned
Weight
Content Criteria
0.5
Student identifies and defines what Standard Normative Distribution (SND) is.
Student explains why it is needed to use a SND to compare two data sets.
0.5
Student identifies the purpose of a z-score in a SND.
0.5
Student identifies the purpose of a percentage in a SND.
0.25
Student explains whether a z-score or a percentage does a better job of identifying proportion of a SND.
0.25
The student responds to at least two classmates’ initial posts by Day 7.
1
Student uses correct spelling, grammar and sentence structure.
2
5
Grading - The discussions are both worth a total of 5 points. The breakdown of the grading for this week’s assignment (per discussion assignment) will be as follows:
Posting your answer by the due date (Day 3, Thursday) is worth 4 points. These five points will be based on the information outlined within the Discussion Assignment Expectations. Content will be worth 2 points and format; spelling and grammar will be worth 2 points.
Responding to two of your classmates (for each assignment) is worth 1 point. The answers must be substantive and go beyond “I agree” or “Good job” to qualify for this point.
Intellectual Elaboration:
In Wee.
QUESTION 1Question 1 Describe the purpose of ecumenical servic.docxmakdul
This document contains a summary of a research article that examines the relationship between patient satisfaction scores and inpatient admission volumes at teaching and non-teaching hospitals. The study found a statistically significant positive correlation between patient satisfaction and admissions at teaching hospitals, but a non-significant negative correlation at non-teaching hospitals. When combined, teaching and non-teaching hospitals showed a statistically significant negative correlation. The findings suggest patient satisfaction may impact admissions more at teaching hospitals. The conclusion provides recommendations for healthcare organizations to strategically focus on patient satisfaction to strengthen performance.
Diff rel gof-fit - jejit - practice (5)Ken Plummer
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
Inferential vs descriptive tutorial of when to use - Copyright UpdatedKen Plummer
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
Diff rel ind-fit practice - Copyright UpdatedKen Plummer
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updatedKen Plummer
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data practice - Copyright updatedKen Plummer
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updatedKen Plummer
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updatedKen Plummer
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
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Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
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IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
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Central spread symmetry 2.0
1. Central Tendency
Spread
Symmetry
Central Tendency, Spread, or Distribution Shape?
Tells you about where the data are
clustering or centering.
Look for words like, average, mean, median, most
common, center point etc.
Tells you about how spread out the
data are from one another.
Look for words like spread, difference, deviation,
range, variation, etc.
Tells you about the shape of the
distribution.
Look for words like shape or expressions like
• Most of the data in the center.
• A few data points to the left or right.
• All data evenly distributed.
• Shape of the distribution is very peaked.
Note – to review what a distribution is go to slide X and then return to this slide.
3. Problem #1
Question: What is the average number of cyberbullying incidents
among freshmen, sophomores, juniors, and seniors?
4. Problem #1
Question: What is the average number of cyberbullying incidents
among freshmen, sophomores, juniors, and seniors?
Instructions: Which type of description of the distribution
(below) is most relevant to the question (above)?
Central Tendency Spread Symmetry
5. Problem #1
Question: What is the average number of cyberbullying incidents
among freshmen, sophomores, juniors, and seniors?
Central Tendency Spread Symmetry
6. Problem #1
Question: What is the average number of cyberbullying incidents
among freshmen, sophomores, juniors, and seniors?
Central Tendency Spread Symmetry
Tells you about where the data are clustering or centering.
Look for words like, average, mean, median, most common, center point etc.
7. Problem #1
Question: What is the average number of cyberbullying
incidents among freshmen, sophomores, juniors, and seniors?
Central Tendency Spread Symmetry
Tells you about where the data are clustering or centering.
Look for words like, average, mean, median, most common, center point etc.
8. Problem #2
Question: What is the difference between the lowest and
highest score on a survey designed to test the impact of a new
therapy?
9. Problem #2
Question: What is the difference between the lowest and
highest score on a survey designed to test the impact of a new
therapy?
Instructions: Which type of description of the distribution
(below) is most relevant to the question (above)?
Central Tendency Spread Symmetry
10. Problem #2
Question: What is the difference between the lowest and
highest score on a survey designed to test the impact of a new
therapy?
Central Tendency Spread Symmetry
11. Problem #2
Question: What is the difference between the lowest and
highest score on a survey designed to test the impact of a new
therapy?
Central Tendency Spread Symmetry
Tells you about how spread out the data are from one another.
Look for words like spread, difference, deviation, range, variation, etc.
12. Problem #2
Question: What is the difference between the lowest and
highest score on a survey designed to test the impact of a new
therapy?
Central Tendency Spread Symmetry
Tells you about how spread out the data are from one another.
Look for words like spread, difference, deviation, range, variation, etc.
13. Problem #3
Question: The director of a health clinic has asked you to help
her analyze data from the results of patient systolic blood
pressure readings. You decide to compute the mean systolic
pressure to see where the patient results cluster.
14. Problem #3
Question: The director of a health clinic has asked you to help
her analyze data from the results of patient systolic blood
pressure readings. You decide to compute the mean systolic
pressure to see where the patient results cluster.
Instructions: Which type of description of the distribution
(below) is most relevant to the question (above)?
Central Tendency Spread Symmetry
15. Problem #3
Question: The director of a health clinic has asked you to help
her analyze data from the results of patient systolic blood
pressure readings. You decide to compute the mean systolic
pressure to see where the patient results cluster.
Central Tendency Spread Symmetry
16. Problem #3
Question: The director of a health clinic has asked you to help
her analyze data from the results of patient systolic blood
pressure readings. You decide to compute the mean systolic
pressure to see where the patient results cluster.
Central Tendency Spread Symmetry
Tells you about where the data are clustering or centering.
Look for words like, average, mean, median, most common, center point etc.
17. Problem #3
Question: The director of a health clinic has asked you to help
her analyze data from the results of patient systolic blood
pressure readings. You decide to compute the mean systolic
pressure to see where the patient results cluster.
Central Tendency Spread Symmetry
Tells you about where the data are clustering or centering.
Look for words like, average, mean, median, most common, center point etc.
18. Problem #4
Question: An entrance exam for mechanical engineers is very
difficult. If it is very difficult you would expect most of the scores
to bunched up on the lower end of the score distribution with a
few high scores. What is the shape of this distribution?
19. Problem #4
Question: An entrance exam for mechanical engineers is very
difficult. If it is very difficult you would expect most of the scores
to bunched up on the lower end of the score distribution with a
few high scores. What is the shape of this distribution?
Instructions: Which type of description of the distribution
(below) is most relevant to the question (above)?
Central Tendency Spread Symmetry
20. Problem #4
Question: An entrance exam for mechanical engineers is very
difficult. If it is very difficult you would expect most of the scores
to bunched up on the lower end of the score distribution with a
few high scores. What is the shape of this distribution?
Central Tendency Spread Symmetry
21. Problem #4
Question: An entrance exam for mechanical engineers is very
difficult. If it is very difficult you would expect most of the scores
to bunched up on the lower end of the score distribution with a
few high scores. What is the shape of this distribution?
Tells you about the shape of the distribution.
Look for words like shape or expressions like
• Most of the data in the center.
• A few data points to the left or right.
• All data evenly distributed.
• Shape of the distribution is very peaked.
Central Tendency Spread Symmetry
22. Problem #4
Question: An entrance exam for mechanical engineers is very
difficult. If it is very difficult you would expect most of the scores
to bunched up on the lower end of the score distribution with a
few high scores. What is the shape of this distribution?
Tells you about the shape of the distribution.
Look for words like shape or expressions like
• Most of the data in the center.
• A few data points to the left or right.
• All data evenly distributed.
• Shape of the distribution is very peaked.
Central Tendency Spread Symmetry
23. Problem #5
Question: You wish to find out the comfort level seasoned
faculty have with emerging educational technologies. You survey
15 faculty who have taught more than 25 years. Two select “no
comfort”, twelve select “minimal comfort” and two select
moderate comfort.
24. Problem #5
Question: You wish to find out the comfort level seasoned
faculty have with emerging educational technologies. You survey
15 faculty who have taught more than 25 years. Two select “no
comfort”, twelve select “minimal comfort” and two select
moderate comfort.
Instructions: Which type of description of the distribution
(below) is most relevant to the question (above)?
Central Tendency Spread Symmetry
25. Problem #5
Question: You wish to find out the comfort level seasoned
faculty have with emerging educational technologies. You survey
15 faculty who have taught more than 25 years. Two select “no
comfort”, twelve select “minimal comfort” and two select
moderate comfort.
Central Tendency Spread Symmetry
26. Problem #5
Question: You wish to find out the comfort level seasoned
faculty have with emerging educational technologies. You survey
15 faculty who have taught more than 25 years. Two select “no
comfort”, twelve select “minimal comfort” and two select
moderate comfort.
Central Tendency Spread Symmetry
Tells you about the shape of the distribution.
Look for words like shape or expressions like
• Most of the data in the center.
• A few data points to the left or right.
• All data evenly distributed.
• Shape of the distribution is very peaked.
27. Problem #5
Question: You wish to find out the comfort level seasoned
faculty have with emerging educational technologies. You survey
15 faculty who have taught more than 25 years. Two select “no
comfort”, twelve select “minimal comfort” and two select
moderate comfort.
Central Tendency Spread Symmetry
Tells you about the shape of the distribution.
Look for words like shape or expressions like
• Most of the data in the center.
• A few data points to the left or right.
• All data evenly distributed.
• Shape of the distribution is very peaked.
28. What is a Distribution?
If necessary explore this question
29. We will illustrate what a distribution is with a
data set that describes the hours students’ study
36. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
The X Axis, will be the
number of hours of
study
37. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
38. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
39. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
40. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
41. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
42. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
43. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
44. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
The Y Axis, indicates
the number of times
the same number
occurs
45. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
Number of Occurrences
46. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
47. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
48. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
49. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
50. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
51. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
52. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
53. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
54. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
55. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
56. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
57. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
58. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
59. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
60. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
61. Student Hours of
Study
Bart 1
Basheba 2
Bella 2
Bob 3
Boston 3
Bunter 3
Buxby 4
Bybee 4
Bwinda 5
Hours of Study
1 2 3 4 5
NumberofOccurrences
1
2
3
This is a
distribution
62. One way to represent a distribution like this:
63. One way to represent a distribution like this:
64. One way to represent a distribution like this:
Is like this:
65. One way to represent a distribution like this:
Is like this:
66. One way to represent a distribution like this:
Is like this:
Normal distributions have
the majority of the data in
the middle
67. One way to represent a distribution like this:
Is like this:
Normal distributions have
the majority of the data in
the middle
68. One way to represent a distribution like this:
Is like this:
With decreasing
but equal amounts
toward the tails
69. One way to represent a distribution like this:
Is like this:
With decreasing
but equal amounts
toward the tails
With decreasing
but equal amounts
toward the tails
Symmetry has to do with he shape of a distribution
When the distribution is symmetrical it has most of the values in the middle with equally decreasing values to the left and right of the distribution (as shown to the left).
A distribution is asymmetrical when it does not follow this pattern (see the bottom two images to the left).
You normally will not be asked to assess skew directly but it is an important step in determining the type of spread or central tendency statistics you will run.