Spss basics tutorial
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This document outlines statistical analysis techniques that can be performed in SPSS, including loading and preparing data, descriptive statistics like frequencies and cross tabulations, inferential statistics like t-tests, ANOVA, correlation, and reliability analysis, and exporting results. Chi-square tests examine relationships between categorical variables while t-tests compare means and ANOVA examines differences in means between groups. Correlation analyzes relationships between interval variables.
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Using SPSS: A Tutorial
The document provides instructions for using various statistical analyses and tests in IBM's SPSS software. It discusses how to perform descriptive analyses, summarize data, check assumptions, conduct t-tests, ANOVA, correlations, regressions and other inferential analyses. It also provides guidance on interpreting outputs and reporting results from SPSS analyses.
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SPSS (Statistical Package for the Social Sciences) is software used for data analysis. It can process questionnaires, report data in tables and graphs, and analyze means, chi-squares, regression, and more. Originally its own company, SPSS is now owned by IBM and integrated into their software portfolio. The document provides an overview of using SPSS, including entering data from questionnaires, different question/response formats, and descriptive statistical analysis functions in SPSS like frequencies, cross-tabs, and graphs.
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SPSS (Statistical Package for the Social Sciences) is statistical software used for data management and analysis. It allows users to process questionnaires, report data in tables and graphs, and analyze data through various tests like means, chi-square, and regression. Originally called SPSS Inc., it is now owned by IBM and known as IBM SPSS Statistics. The document provides an introduction to SPSS and outlines how to define variables, enter data, select cases, run descriptive statistics like frequencies and crosstabs, and manipulate output files.
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This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
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The document provides instructions for using various statistical analyses and tests in IBM's SPSS software. It discusses how to perform descriptive analyses, summarize data, check assumptions, conduct t-tests, ANOVA, correlations, regressions and other inferential analyses. It also provides guidance on interpreting outputs and reporting results from SPSS analyses.
Introduction to spss
SPSS (Statistical Package for the Social Sciences) is software used for data analysis. It can process questionnaires, report data in tables and graphs, and analyze means, chi-squares, regression, and more. Originally its own company, SPSS is now owned by IBM and integrated into their software portfolio. The document provides an overview of using SPSS, including entering data from questionnaires, different question/response formats, and descriptive statistical analysis functions in SPSS like frequencies, cross-tabs, and graphs.
An Introduction to SPSS
SPSS (Statistical Package for the Social Sciences) is statistical software used for data management and analysis. It allows users to process questionnaires, report data in tables and graphs, and analyze data through various tests like means, chi-square, and regression. Originally called SPSS Inc., it is now owned by IBM and known as IBM SPSS Statistics. The document provides an introduction to SPSS and outlines how to define variables, enter data, select cases, run descriptive statistics like frequencies and crosstabs, and manipulate output files.
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This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
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This document provides an overview of methods for data analysis. It discusses data, descriptive statistics such as measures of central tendency and dispersion, inferential statistics including hypothesis testing and probability, and statistical software packages with a focus on SPSS. SPSS allows users to easily input, manage, and analyze data to obtain summary statistics and perform inferential analyses like t-tests, ANOVA, and regression. Outputs can be copied into reports.
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SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
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(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
ANOVA
ANOVA
• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
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Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
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SPSS is a statistical software package used for data management and analysis. It allows users to enter and manage large amounts of data, perform a wide range of statistical analyses, and output results in tables and graphs. The main SPSS windows are the Data Editor, used to enter and view data, and the Viewer, which displays output of statistical analyses. Common analysis techniques demonstrated in the document include independent and paired t-tests to compare group means. The document provides guidance on using SPSS for questionnaire design and statistical analysis to efficiently analyze social science and business data.
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This document provides an overview of data analysis using SPSS. It discusses key concepts like variables, measurement scales, data types, statistical terminology, and the steps involved in data analysis using SPSS. The document defines nominal, ordinal, interval and ratio scales of measurement. It also describes the nature of data as categorical or metric, and the types of categorical and metric data. Furthermore, it outlines tasks like data preparation, coding, cleaning and the appropriate use of statistical tools for analysis in SPSS.
SPSS an intro...
This document provides an introduction to using SPSS (Statistical Package for the Social Sciences) for data analysis. It discusses the four main windows in SPSS - the data editor, output viewer, syntax editor, and script window. It also covers the basics of managing data files, including opening SPSS, defining variables, and sorting data. Several basic analysis techniques are introduced, such as frequencies, descriptives, and linear regression. Examples are provided for how to conduct these analyses and interpret the outputs.
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This document provides an introduction and overview of SPSS (Statistical Package for the Social Sciences). It discusses what SPSS is, the research process it supports, how questionnaires are translated into SPSS, different question and response formats, and levels of measurement. It also briefly outlines some of SPSS's data editing, analysis, and output features.
spss teaching
This document provides an overview of the Statistical Package for Social Sciences (SPSS) software. It describes the main components and windows in SPSS, including the data window, variable view window, output window, and chart editor window. It also outlines several statistical techniques that can be performed in SPSS, such as descriptive statistics, correlations, t-tests, and chi-square tests of independence. SPSS is a tool that allows users to manage and analyze data, as well as generate graphs and conduct a wide range of statistical procedures.
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Spss and software Application
Statistics is the study of collecting, organizing, analyzing, and interpreting data. It was introduced in 1791 in English by Sir John Sinclair when he published the first volume of a statistical account of Scotland. SPSS is a widely used software package for statistical analysis and data management. It allows users to easily enter and manage data, conduct statistical analyses, and display results in graphs and tables.
Lecture 1
This document provides an overview of methods for data analysis. It discusses data, descriptive statistics such as measures of central tendency and dispersion, inferential statistics including hypothesis testing and probability, and statistical software packages with a focus on SPSS. SPSS allows users to easily input, manage, and analyze data to obtain summary statistics and perform inferential analyses like t-tests, ANOVA, and regression. Outputs can be copied into reports.
Evaluation Spss
SPSS is a popular statistical analysis software that is known for its ease of use. It has strong graphical capabilities and supports a variety of statistical analyses. However, it lacks some more advanced statistical procedures and has limited data management tools. While suitable for many tasks, some users may outgrow it over time and require more specialized software like SAS or Stata for complex or cutting-edge analyses. Overall, SPSS is best suited for users performing basic to intermediate statistical analysis and reporting.
SPSS-SYNTAX
This document provides an overview of a 2-day workshop on SPSS syntax that will be held on October 28th and 29th, 2010. The workshop will be organized by the Indian Institute of Psychometry in Kolkata and led by Dr. Debdulal Dutta Roy of the Psychology Research Unit at the Indian Statistical Institute in Kolkata. Topics that will be covered include an introduction to SPSS, its features and interfaces, how to write SPSS syntax for data management and analysis tasks, how to check data quality using syntax, and how to perform statistical analyses like correlations and descriptive statistics using syntax. Assignments involving practicing these skills with sample data will also be part of the workshop.
Introduction to spss
SPSS is a widely used statistical software package for analyzing social science and medical data. It provides drop down menus and templates to make data analysis and presentation user-friendly compared to other statistical software. SPSS has tools for importing, cleaning, transforming, and analyzing data. Key functions include sorting cases, merging datasets, recoding variables, and checking for outliers and normal distribution of variables.
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This document provides instructions for entering data into SPSS from Excel, cleaning data in SPSS, and formatting variables. It discusses:
1. The three main windows in SPSS and how to enter data directly or import from Excel.
2. Guidelines for structuring data in Excel for easy import into SPSS, including naming variables, encoding categories, including IDs, and placing each variable in its own column.
3. Methods for cleaning data in SPSS, such as recoding variables, creating new variables, computing variables from existing ones, and labeling and formatting variables.
Spss beginners
SPSS is statistical software used by researchers to perform statistical analysis. It was first released in 1968 as the Statistical Package for the Social Sciences. SPSS is now owned by IBM and allows users to manage and analyze data, perform statistical tests, and produce graphs and reports. Researchers use SPSS to clean, code, and enter data, choose appropriate statistical tests to analyze the data, and interpret the results.
Introduction to spss 1
At the end of this Lesson (Part 1) the students should be able to know the following
Introduction
Data Entry
Variable and Value Label
Entering Data
File management
Descriptive statistics
Editing and modifying the data
Unit 4 editing and coding (2)
This document discusses data analysis and various techniques used in data analysis such as data editing, coding, classification, tabulation, and statistical analysis. It describes different types of statistical tests like z-test, t-test, chi-square test, and their uses. It also discusses various types of tables, diagrams, and graphical representations that are used to present statistical data in a meaningful way. Key types of diagrams mentioned include bar charts, pie charts, histograms and scatter plots. Rules for properly constructing tables and graphs are also provided.
Measurement of scales
This document discusses different types of measurement variables. It begins by defining measurement variables as unknown attributes that can take quantitative or qualitative values. It then describes the four main types of measurement variables: nominal, ordinal, interval, and ratio. For each type, it provides examples and characteristics. Nominal variables categorize data without order, ordinal variables rank data, interval variables use equal distances on a scale, and ratio variables have a true zero point.
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Discussion Forum Instructions:
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• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
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• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
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• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
variance of the random error so the ratio is assumed to be close to 1.
Null Hypothesis
Alternate Hypothesis
One-Way ANOVA
One-Way ANOVA
One-Way ANOVA Excel Output
Treatme
(Individuals With Disabilities Act Transformation Over the Years)D
(Individuals With Disabilities Act Transformation Over the Years)
Discussion Forum Instructions:
1. You must post at least three times each week.
2. Your initial post is due Tuesday of each week and the following two post are due before Sunday.
3. All post must be on separate days of the week.
4. Post must be at least 150 words and cite all of your references even it its the book.
Discussion Topic:
Describe how the lives of students with disabilities from culturally and/or linguistically diverse backgrounds have changed since the advent of IDEA. What do you feel are some things that can or should be implemented to better assist with students that have disabilities? Tell me about these ideas and how would you integrate them?
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• Analysis of Variance
• Statistical method to analyzes variances to determine if the means from more than
two populations are the same
• compare the between-sample-variation to the within-sample-variation
• If the between-sample-variation is sufficiently large compared to the within-sample-
variation it is likely that the population means are statistically different
• Compares means (group differences) among levels of factors. No
assumptions are made regarding how the factors are related
• Residual related assumptions are the same as with simple regression
• Explanatory variables can be qualitative or quantitative but are categorized
for group investigations. These variables are often referred to as factors
with levels (category levels)
ANOVA Assumptions
• Assume populations , from which the response values for the groups
are drawn, are normally distributed
• Assumes populations have equal variances
• Can compare the ratio of smallest and largest sample standard deviations.
Between .05 and 2 are typically not considered evidence of a violation
assumption
• Assumes the response data are independent
• For large sample sizes, or for factor level sample sizes that are equal,
the ANOVA test is robust to assumption violations of normality and
unequal variances
ANOVA and Variance
Fixed or Random Factors
• A factor is fixed if its levels are chosen before the ANOVA investigation
begins
• Difference in groups are only investigated for the specific pre-selected factors
and levels
• A factor is random if its levels are choosen randomly from the
population before the ANOVA investigation begins
Randomization
• Assigning subjects to treatment groups or treatments to subjects
randomly reduces the chance of bias selecting results
ANOVA hypotheses statements
One-way ANOVA
One-Way ANOVA
Hypotheses statements
Test statistic
=
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
𝑊𝑖𝑡ℎ𝑖𝑛 𝐺𝑟𝑜𝑢𝑝 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒
Under the null hypothesis both the between and within group variances estimate the
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Null Hypothesis
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Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. 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, 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.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
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
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
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This document provides an overview of t-tests and analysis of variance (ANOVA) statistical methods. It defines different types of t-tests including one-sample, independent-samples, and paired t-tests. It also explains one-way and factorial ANOVA, and the concepts of main effects and interaction effects. Formulas for computing ANOVA are presented along with post-hoc tests and considerations around type I error inflation with multiple tests.
Statistical Tools
This document provides an overview of t-tests and analysis of variance (ANOVA) statistical methods. It defines different types of t-tests including one-sample t-tests, independent-samples t-tests, and paired-samples t-tests. It also explains one-way ANOVA, within-subjects ANOVA, factorial ANOVA, main effects and interaction effects in ANOVA. Key concepts such as confidence intervals, significance tests, and assumptions of normality are discussed. Examples are provided to illustrate how to set up null and alternative hypotheses and interpret statistical significance.
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Analysis of variance (ANOVA) is a method for testing the hypothesis that there is no difference between two or more population means.
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