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This document discusses different methods for correlating and comparing data sets. It describes four types of correlation - Pearson, point-biserial, Spearman rank-order, and chi-square test of independence - that determine how variables relate. The Pearson correlation is used for interval/ratio scales, point-biserial for dichotomous and continuous variables, Spearman for ranked data, and chi-square for categorical frequencies. Methods for comparing means include t-tests for independent and dependent samples to analyze two groups, and ANOVA to analyze three or more groups.
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Repeated Measures Designs
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Explain the advantages and drawbacks of using data from non-independent groups.
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iStockphoto/Thinkstock
tan81004_07_c07_163-192.indd 163 2/22/13 3:41 PM
CHAPTER 7Introduction
Chapter Outline
7.1 Dependent Groups Designs
Reconsidering the t and F ratios
An Example
A Matched Pairs Example
Comparing the Paired-Samples t-Test to the Independent Samples t-Test
The Power of the Dependent Groups Test
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The Alternate Approaches to Dependent t-Tests
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Transform the data into readable text
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iStockphoto/Thinkstock
tan81004_07_c07_163-192.indd 163 2/22/13 3:41 PM
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Chapter Outline
7.1 Dependent Groups Designs
Reconsidering the t and F ratios
An Example
A Matched Pairs Example
Comparing the Paired-Samples t-Test to the Independent Samples t-Test
The Power of the Dependent Groups Test
The Dependent Groups t-Test on Excel
The Alternate Approaches to Dependent t-Tests
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A Within-Subjects F Example
Calculating the Within-Subjects F
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Another Within-Subjects F Example
A Within-Subjects F in Excel
Chapter Summary
Introduction
Some of the most critical questions in management relate to change over time. For exam-ple, managers are deeply interested in assessing sales growth, shifts in shopping trends,
improvements in employee attitudes, increases in employee performance, and decreases in
absenteeism or turnover. They are also often keen to find out the influence of various
managerial decisions and business strategies on these and many other change-oriented
outcomes. However, none of the analyses completed to this point address these change-
related questions, because these analyses do not accommodate repeated measures of the
same variables within the same group of subjects over time. For instance, the t-tests and
ANOVAs discussed so far compared independent groups, groups that have completely
separate subjects. Each subject was only measured once on each variable of interest. The
same group of subjects was not measured repeatedly on the same variables to assess
change over time.
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The document discusses various statistical concepts including:
- The functions of statistics such as expressing facts numerically and establishing relationships between facts.
- The importance of statistics to fields like administration, economics, research, and education.
- Common measures of central tendency including the mean, median, and mode.
- The difference between theoretical and empirical probabilities.
- Types of correlation like positive, negative, simple, and multiple correlation.
- Key statistical tests including t-tests, chi-square, F-tests, and measures of accuracy, precision, and confidence intervals.Chapter 13 Data Analysis Inferential Methods and Analysis of Time Series
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The document provides information on statistical techniques for comparing means between groups, including t-tests, analysis of variance (ANOVA), and their assumptions and applications. T-tests are used to compare two groups, while ANOVA allows comparison of three or more groups and controls for increased Type I error rates. Steps for conducting t-tests, ANOVA, and post-hoc tests using SPSS are outlined along with examples and interpretations.this activity is designed for you to explore the continuum of an a.docx
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Addictive behavior appears in stages.油The earliest stage is non-use, which finally leads up to out-of-control dependence.油The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks,油use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
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Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
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Objective is to develop theory from data
Emphasizes peoples actions and voices as the main sources of d.Quantitative analysis using SPSS
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油
This document provides an overview of quantitative analysis techniques using SPSS, including data manipulation, transformation, and cleaning methods. It also covers univariate, bivariate, and other statistical analysis methods for exploring relationships between variables and differences between groups. Specific techniques discussed include computing new variables, recoding, selecting cases, imputing missing values, aggregating data, sorting, merging files, descriptive statistics, correlations, regressions, t-tests, ANOVA, non-parametric tests, and more.Parametric test SANJAY.pptx
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- 1. CHAPTER 5
- 3. CORRELATING TWO DATA SETS TYPES OF CORRELATION
- 4. Determines how one variables varies relatively with the other. Causal relationship means that a change in one variable affects the other variable.
- 5. Pearson Product-Moment Correlation Point-Biserial Correlation Spearman Rank-Order Correlation Chi-Square Test of Independence
- 6. Pearson product-moment correlation, or Pearsons r, is used to determine the relationship between data in the interval or ratio scale.
- 7. When a variable in an interval or ratio scale (e.g., achievement in mathematics) is correlated with naturally-occurring dichotomous variable like gender (male or female).
- 8. When ranking attributes is more meaningful than its interval or ratio measures, such as in giving academic honors, the participants are ranked
- 9. This test is utilized when correlating two data sets expressed in frequency counts and when the variables are categorical.
- 10. COMPARING TWO GROUP MEANS - Before comparing two sets of data, the fact that data may be independent or dependent must be considered. - The purpose of comparing two group means is to know whether they are statistically equal or different.
- 11. t-Test for Independent Samples - To determine if there is a significant difference between the means of two independent groups of participant, the t-test for independent samples is used.
- 12. - The t-Test must be used to confirm if there exists a statistical difference t-Test for Dependednt Samples or Paired Sample t-Test - The t-Test for dependent samples and the t-Test for independednt samples are used and interpreted in the same manner.
- 13. - The only difference is that in the t-Test for dependent samples, the two sets of data compared come from the group of individuals or research elements.
- 14. COMPARING THREE OR MORE GROUP MEANS - The F-test, commonly known as analysis of variance (ANOVA), is used to compare three or more means. - It requires a further statistical analysis when a significant F-value is calculated.
- 15. THANK YOU