Choosing the Correct Statistical Test in SAS, Stata and SPSS. The following table shows general guidelines for choosing a statistical analysis.
We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors). You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is (approximately) normally distributed (see What is the difference between categorical, ordinal and interval variables? This page was adapted from Choosing the Correct Statistic developed by James D.
Exploratory Research? LIKERT SCALES. Descriptive Statistics. Redpond Psychometric Centre, Madurai: When to use statistics? Manfredo etal (2003) The potential for conflict index. PCI-2 Menu System. Analogy: Jury Verdict and Hypothesis Testing Type I and Type II errors. Effect sample size - p-value - power analysis. The significance test yields a p-value that gives the likelihood of the study effect, given that the null hypothesis is true.
For example, a p-value of .02 means that, assuming that the treatment has no effect, and given the sample size, an effect as large as the observed effect would be seen in only 2% of studies. The p-value obtained in the study is evaluated against the criterion, alpha. If alpha is set at .05, then a p-value of .05 or less is required to reject the null hypothesis and establish statistical significance. Lecture FDR. Improving Conclusion Validity. « PreviousHomeNext » Improving Conclusion Validity.
Statistical Power. « PreviousHomeNext » There are four interrelated components that influence the conclusions you might reach from a statistical test in a research project.
The logic of statistical inference with respect to these components is often difficult to understand and explain. This paper attempts to clarify the four components and describe their interrelationships. The four components are: P Value Statement from APA, May, 2016. Rice Virtual Lab in Statistics (RVLS) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grant DUE 9751307.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Last updated: 12/3/108 Permissions Permission is granted to link to any portion of the Rice Virtual Lab. The applets in the simulations/demonstrations are hereby in the public domain and can therefore be used without restriction. The following are zip archives. If you wish to copy and/or distribute any portion of the Case Studies, or the Analysis Lab, please contact David Lane. Credits Feedback Your comments are welcome.
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R Resources. Graphic Display of Quantitative Data. Laureate Tutorial: Survey Design and Research. Laureate Tutorial: Multivate Methods. Threats to validity of Research Design. This design controls for all of the seven threats to validity described in detail so far.
An explanation of how this design controls for these threats is below. History--this is controlled in that the general history events which may have contributed to the O1 and O2 effects would also produce the O3 and O4 effects. This is true only if the experiment is run in a specific manner--meaning that you may not test the treatment and control groups at different times and in vastly different settings as these differences may effect the results.
Rather, you must test simultaneously the control and experimental groups. Intrasession history must also be taken into consideration. The factors described so far effect internal validity. University of Nebraska–Lincoln.
Guide for Novice Researchers. Effect sizes. Null hypothesis testing and effect sizes.
Effect Size - Statistics Solutions. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale.
For instance, if we have data on the height of men and women and we notice that, on average, men are taller than women, the difference between the height of men and the height of women is known as the effect size. The greater the effect size, the greater the height difference between men and women will be. Statistic effect size helps us in determining if the difference is real or if it is due to a change of factors.
Dr. Andy Field: Lectures on You Tube. Upload Andy Field Loading...
Working... ► Play all Lectures. Andy Fields: Index of PDF documents. Andy field discovering statistics using spss third edition 20091. BMDS - HaPI Database. Getting started with replication-based dissertations. Overview of Designs. Research Methods. AllPsych > Research Methods Research Methods By AllPsych Editor. Social Reserch Methods Home. Available Research Designs Based on Example Research Questions – SOBT. Quantitative Research Designs Avoid research questions with yes or no answers, as they are not particularly interesting. Qualitative Research Designs Design Science References Adeyemo, O. Firari, F. IIER 16: Mackenzie and Knipe - research dilemmas: Paradigms, methods and methodology. Issues In Educational Research, Vol 16, 2006[ Contents Vol 16 ] [ IIER Home ] Noella Mackenzie and Sally KnipeCharles Sturt University In this article the authors discuss issues faced by early career researchers, including the dichotomy, which many research textbooks and journal articles create and perpetuate between qualitative and quantitative research methodology despite considerable literature to support the use of mixed methods.
Quantitative Methodology. Key Elements of a Research Proposal - Quantitative Design. How to structure quantitative research questions. STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure, manipulate and/or control. If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable, should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal, dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables).
It also explains the difference between independent and dependent variables, which you need to understand to create quantitative research questions. To provide a brief explanation; a variable is not only something that you measure, but also something that you can manipulate and control for. Testing Stistical Assumptions.
Explorable. Basic Experimental Design. Sid Sytsma Website Administrator's Note: I have always considered Sid Sytsma's short article on experimental design one of the best short pieces on the subject I have ever seen, and provided a link to it from my Lutherie Information Website. Professor Sytsma retired and no longer felt the need to retain his site, and when this happened I asked if I could please host this article on my own site. Recently I have been informed that a number of other folks have seen the value of Professor Sytsma's article and have provided links to it, but unfortunately a number of these links attribute this work to me. Please, if you do link to this page, give credit where credit is due.
This wonderful article is by Sid Sytsma. . - R.M. Chapter 9. Chapter 9 Experimental Research. Reporting Results of Statistical Tests. MertensHandout.
MANOVA Collection. You Tube: Dr. Todd Grande SPSS. Cross Validated; Research Q & A. Sensemaking. In information science the term is most often written as "sense-making. " In both cases, the concept has been used to bring together insights drawn from philosophy, sociology, and cognitive science (especially social psychology).
STATISTICS. How to structure quantitative research questions. Online Statistics Education: A Free Resource for Introductory Statistics. Sample Size - Why is Statistical Power So Important? - The Problem with Surveys in Research. Social Research Update 35: The importance of pilot studies. Social Research Update is published quarterly by the Department of Sociology, University of Surrey, Guildford GU7 5XH, England.
Subscriptions for the hardcopy version are free to researchers with addresses in the UK. Apply to SRU subscriptions at the address above, or email firstname.lastname@example.org. A PDF version of this article is available here. Probability and statistics EBook - Socr. It's (Beyond) Time to Drop the Terms Causal-Comparative and Correlational Research. MegaStat: Free Download and Tutorials. Click the link below to download the MegaStat software. Welcome to Seeing Statistics. Statistical Software.
The complete list of NCSS procedures and functions - Statistical software Guide. UDEMY: Best Online Courses for SPSS. Penn State: Welcome to STAT 501 Online Tutorials. Printer-friendly version. Statistical Power Assessment: G*Power Analysis tool. Top 27 Free Data Analysis Software. Statlets. Statistic Applets for Teaching Topics in Introductory Courses. Real time world statistics. Gapminder: Unveiling the beauty of statistics for a fact based world view. Free Download. Click on an icon below for a free download of either of the following files. Real Statistics Resource Pack: contains a variety of supplemental functions and data analysis tools not provided by Excel. Accessing Real Statistics Tools. On this webpage we present a number of ways for accessing the Real Statistics data analysis tools. Welcome to SurveyMonkey!
Resources to help you learn and use G*Power. Universität Düsseldorf: G*Power. GPower: www.stat.purdue.edu/~jennings/stat582/software/GPower.docx. GPowerManual: Universität Düsseldorf: G*Power.