Unit 3 lesson 1 version 2
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This document provides an overview of Lesson 1 in a course on working with SPSS. It outlines how the lesson will demonstrate opening SPSS and PowerPoint simultaneously, navigating to different screens in SPSS including the variable and data views, using SPSS to conduct descriptive statistics and frequency analyses, graphing histograms and scatterplots, and computing Pearson's r correlation.
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Competency 1: Analyze the computation, application, strengths, and limitations of various statistical tests.
Develop a conclusion including strengths and limitations of correlation.
Competency 2: Analyze the decision-making process of data analysis.
Analyze the assumptions of correlation.
Competency 3: Apply knowledge of hypothesis testing.
Develop a research question, null hypothesis, alternative hypothesis, and alpha level.
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Interpret the correlation output.
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Apply the appropriate SPSS procedures to check assumptions and calculate the correlations.
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Develop a context for the data set, including a definition of required variables and scales of measurement.
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Competency 1: Analyze the computation, application, strengths, and limitations of various statistical tests.
Develop a conclusion including strengths and limitations of correlation.
Competency 2: Analyze the decision-making process of data analysis.
Analyze the assumptions of correlation.
Competency 3: Apply knowledge of hypothesis testing.
Develop a research question, null hypothesis, alternative hypothesis, and alpha level.
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Interpret the correlation output.
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Apply the appropriate SPSS procedures to check assumptions and calculate the correlations.
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Competency Map
CHECK YOUR PROGRESS
Use this online tool to track your performance and progress through your course.
Toggle Drawer
ContextRead
Assessment 2 Context [DOC]
for important information on the following topics:
SHOW LESS
Interpreting correlation: Magnitude and sign.
Assumptions of correlation.
Hypothesis testing of correlation.
Effect size in correlation.
Alternative correlation coefficients.
Correlation—application.
Proper reporting of correlations.
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Probability values.
Effect size.
Toggle Drawer
Questions to ConsiderAs you prepare to complete this assessment, you may want to think about other related issues to deepen your understanding or broaden your viewpoint. You are encouraged to consider the questions below and discuss them with a fellow learner, a work associate, an interested friend, or a member of your professional community. Note that these questions are for .
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The document provides information on the New Seven quality control tools. It begins with an overview of the seven tools: Affinity Diagrams, Relations Diagrams, Tree Diagrams, Matrix Diagrams, Arrow Diagrams, Process Decision Program Charts, and Matrix Data Analysis. It then discusses the history and development of the New Seven tools. The rest of the document focuses on providing details on how to construct each of the New Seven tools through examples and illustrations. It explains the advantages and applications of each tool to help organize information, identify relationships, develop strategies, and control schedules.
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2. Histograms and P-P plots were created for hygiene variables each day, showing some non-normality. Descriptive statistics also calculated, with skewness and kurtosis indicating violation of normality assumption.
3. Levene's test showed violation of homogeneity of variance on some variables when split by university, meaning test results could be invalidated by assumption violations. Options when assumptions are violated and cases where violations may not impact intended analyses are discussed.
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This document discusses the paired samples t-test, which is used to compare the means of the same sample measured under two different conditions. It provides examples of how to conduct a paired samples t-test in SPSS and explains the factors that affect the calculation of the t value, such as the difference between the means, variability of scores, sample size, and significance level. The document is part of a lesson on comparing two sample means for an educational research statistics course.
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Psyc 354 Massive Success / snaptutorial.com
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Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
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3. A confidence interval tells us that there is a 95% chance the population mean falls within the calculated interval, and repeating the sampling process would result in the population mean being captured 95% of the time.
Unit 2 lesson 2
This document discusses covariance and the Pearson r correlation coefficient. Covariance measures the relationship between two variables, indicating whether it is positive or negative. Pearson r standardizes the covariance to produce a value between -1 and 1 that represents how well one variable can predict the other. It answers whether a relationship exists, its direction, and strength. A value of 0 means no relationship; higher positive or negative values indicate a stronger linear relationship. Pearson r cannot be compared between studies and the covariance determines its sign.
Unit 2 lesson 1
This document appears to be slides from a lesson on reviewing homework on bivariate relationships and statistics concepts. It contains sample bivariate data and works through calculating and interpreting various statistics like the mean, standard deviation, skewness, kurtosis, covariance, and Pearson's r correlation coefficient. It also discusses how these statistics are affected by additive and multiplicative constants.
Unit 1 lesson 5
This document discusses measures of distribution in statistics, including skewness, kurtosis, and the normal distribution. Skewness measures the asymmetry of a distribution and whether it is positively or negatively skewed. Kurtosis measures the peakedness of a distribution relative to a normal distribution. The normal distribution is symmetric with a skewness of 0 and kurtosis of 0. Examples of skewness and kurtosis are provided to illustrate different distribution shapes.
Unit 1 lesson 4
This document discusses different measures of spreadoutness in statistics, including range, standard deviation, z-scores, and standard error of the mean. It explains that range is the simplest measure but has limitations, while standard deviation measures how far scores are from the mean in the data's own units. Z-scores indicate an individual score's distance from the mean in units of standard deviations and allow comparison across data sets with different means and standard deviations. The standard error of the mean represents sampling error and is used to construct confidence intervals.
Unit 1 lesson 3
This document discusses different types of graphs used in statistics including frequency distributions, histograms, scatterplots, and making your own scatterplot. It introduces concepts such as frequency, cumulative frequency, intervals, and bivariate relationships. The document is part of a lesson on graphing and distributions in a statistics course for educational research.
Unit 1 lesson 2
This document discusses three measures of central tendency: mode, median, and mean. The mode is the value that occurs most frequently in a data set. A data set can have more than one mode, and the scale of measurement does not need to be numeric for a mode to be computed. The median is the middle value of a data set when the values are arranged in order. It divides the data set into two equal halves. The mean is the average value of all scores and is computed by summing all values and dividing by the number of values. These three measures provide different but important information about the central or typical values in a data set.
Unit 1 lesson 1
This document discusses levels of scale in statistics, including nominal, ordinal, interval, and ratio scales. Nominal scales involve categories with no logical order, ordinal scales involve categories with a logical order but unknown differences between values, interval scales involve categories with a logical order and equal differences between values, and ratio scales involve all the properties of interval scales plus a true zero point. The different scale levels determine what mathematical operations can be performed on the data.
Lecture 10.19.10
This document provides guidance for writing chapters 4 and 5 of a dissertation. It reviews progress made so far, discusses dissertation style guidelines, and offers tips for good writing. Key points covered include conducting a literature review with the research questions in mind, analyzing studies critically rather than just summarizing, and avoiding common mistakes in research methods and data analysis. Guidelines are provided for writing quantitative and qualitative chapters, developing the dissertation proposal, and drafting the methods chapter so others can replicate the study.
Lecture 10.5.10
This document provides an overview of research methods, including qualitative and quantitative approaches. It discusses key aspects of developing a literature review, such as beginning with a broad topic and focusing on specific research questions. The document also compares different types of research (e.g. basic vs. applied), research paradigms (e.g. quantitative vs. qualitative), and study designs (e.g. experimental vs. case study). Fundamental questions about the nature of knowledge and how to choose an appropriate research method are explored.
Lecture 9.28.10
This document provides an overview of how to conduct a literature review. It discusses key aspects of developing good research questions, operational definitions, and formulating hypotheses. It also reviews important components of the literature review chapter, including reviewing theories, types of hypotheses, and evaluating primary vs. secondary sources. Finally, it outlines steps for analyzing literature, such as distinguishing between assertions and evidence, identifying trends, and evaluating references for currency and coverage of the topic. The overall document serves as a guide for graduate students on how to effectively conduct a literature review.
Lecture 9.21.10
This document provides an overview of key concepts related to developing a research study, including theory and hypothesis, research questions, definition of terms, and dissertation structure. Some main points:
- A theory pulls together observations and relationships between variables to make predictions, while a hypothesis predicts the relationship between two or more variables based on a theory.
- Good research questions are clear, feasible, significant, ethical, and have a relationship to the theory or literature. Hypotheses should be testable and come from the research questions.
- Key terms like independent and dependent variables should be operationally defined. Assumptions, limitations, and delimitations should also be outlined.
- A dissertation proposal typically includes chapters on introduction
Lecture 9.14.10
This document provides an overview of key concepts for developing a dissertation proposal, including evaluating literature reviews, research ethics, developing research questions, and defining variables. It discusses important components of Chapter 1 such as the introduction, purpose, conceptual framework, research questions, definitions of terms, and significance. It also covers types of independent and dependent variables, and attributes versus active independent variables. The goal is to help students understand essential elements for developing a strong dissertation proposal.
Lecture 9.7.10
This document discusses how to develop strong research questions. It explains that research questions should be clearly stated, significant, ethical, feasible, and indicate relationships between variables. Good questions can be answered through data collection and avoid value judgments. Examples of well-constructed questions are provided. The document also discusses how to define variables operationally and identifies characteristics of researchable versus non-researchable questions. Overall, the document provides guidance on formulating focused, meaningful research questions.
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- 1. Unit 3: Working With SPSS Lesson 1: Making Datasets EDER 6010: Statistics for Educational Research Dr. J. Kyle Roberts University of North Texas Next Slide
- 2. How This Lesson Will Work Simultaneous Workings of SPSS and Powerpoint Open both programs at the same time Next Slide
- 3. First Screen In SPSS Next Slide
- 4. Variable View Screen Next Slide
- 5. Variable View Screen (cont.) Next Slide
- 6. Data View Screen Next Slide
- 7. Basic Analysis: Descriptives Analyze Descriptive Statistics Descriptives Next Slide
- 8. Output Viewer Next Slide
- 9. Using the Frequency Function Analyze Descriptive Statistics Frequencies Frequencies for Gender Next Slide
- 10. Next Slide
- 11. Graphing in SPSS Graphs Histogram Next Slide
- 12. Graphing in SPSS Graphs Scatterplot Math 60 70 75 77 83 61 69 79 93 92 GPA = X-axis Math = Y-axis Next Slide
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- 15. Unit 3: Working With SPSS Lesson 1: Making Datasets EDER 6010: Statistics for Educational Research Dr. J. Kyle Roberts University of North Texas