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Test for equality of variance

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Test for equality of variances: Parametric and nonparametric Levene's test in SPSS

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Equality of variances is an assumption for statistical methods such as Analysis of Variance (ANOVA)-a parametric method-and the Kruskal-Wallis one-way analysis-a non-parametric method. We must be able to test for equality of variances in both normally distributed data and non-normally distributed data.

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Test of normality of data

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Normality test using SPSS: How to check whether data are normally distributed

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Reliability test Part 1

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Conduct and interpret an internal consistency reliability analysis through Cronbach's alpha, the corrected item-total correlations and the inter-item correlation matrix. Guidelines for identifying adequate levels of reliability are also provided. http://how2stats.blogspot.com/2011/09/internal-consistency-reliability.html

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Reliability test Part 2

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In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data ( here's an overview of statistical data types) . This topic is usually discussed in the context of academic teaching and less often in the "real world." If you are brushing up on this concept for a statistics test, thank a psychologist researcher named Stanley Stevens for coming up with these terms.

These four data measurement scales (nominal, ordinal, interval, and ratio) are best understood with example, as you'll see below.

Nominal

Let's start with the easiest one to understand. Nominal scales are used for labeling variables, without any quantitative value. "Nominal" scales could simply be called "labels." Here are some examples, below. Notice that all of these scales are mutually exclusive (no overlap) and none of them have any numerical significance. A good way to remember all of this is that "nominal" sounds a lot like "name" and nominal scales are kind of like "names" or labels.

Note: a sub-type of nominal scale with only two categories (e.g. male/female) is called " dichotomous." If you are a student, you can use that to impress your teacher.

Bonus Note #2: Other sub-types of nominal data are "nominal with order" (like "cold, warm, hot, very hot") and nominal without order (like "male/female").

Ordinal

With ordinal scales, the order of the values is what's important and significant, but the differences between each one is not really known. Take a look at the example below. In each case, we know that a #4 is better than a #3 or #2, but we don't know-and cannot quantify-how much better it is. For example, is the difference between "OK" and "Unhappy" the same as the difference between "Very Happy" and "Happy?" We can't say.

Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.

"Ordinal" is easy to remember because is sounds like "order" and that's the key to remember with "ordinal scales"-it is the order that matters, but that's all you really get from these.

Advanced note: The best way to determine central tendency on a set of ordinal data is to use the mode or median; a purist will tell you that the mean cannot be defined from an ordinal set.

Interval

Interval scales are numeric scales in which we know both the order and the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees.

Interval scales are nice because the realm of statistical analysis on these data sets opens up. For example, central tendency can be measured by mode, median, or mean; standard deviation can also be calculated.

Like the others, you can remember the key points of an "interval scale" pretty easily. "Interval" itself means "space in between," which is the important thing to remember-interval scales not only tell us about order, but also about the value between each item.

Here's the problem with interval scales: they don't have a "true zero." For example, there is no such thing as "no temperature," at least not with celsius. In the case of interval scales, zero doesn't mean the absence of value, but is actually another number used on the scale, like 0 degrees celsius. Negative numbers also have meaning. Without a true zero, it is impossible to compute ratios. With interval data, we can add and subtract, but cannot multiply or divide.

Confused? Ok, consider this: 10 degrees C + 10 degrees C = 20 degrees C. No problem there. 20 degrees C is not twice as hot as 10 degrees C, however, because there is no such thing as "no temperature" when it comes to the Celsius scale. When converted to Fahrenheit, it's clear: 10C=50F and 20C=68F, which is clearly not twice as hot. I hope that makes sense. Bottom line, interval scales are great, but we cannot calculate ratios, which brings us to our last measurement scale...

Ratio

Ratio scales are the ultimate nirvana when it comes to data measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero-which allows for a wide range of both descriptive and inferential statistics to be applied. At the risk of repeating myself, everything above about interval data applies to ratio scales, plus ratio scales have a clear definition of zero. Good examples of ratio variables include height, weight, and duration.

Ratio scales provide a wealth of possibilities when it comes to statistical analysis. These variables can be meaningfully added, subtracted, multiplied, divided (ratios). Central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales.

Summary

In summary, nominal variables are used to " name," or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one. Finally, Ratio scales give us the ultimate-order, interval values, plus the ability to calculate ratios since a "true zero" can be defined.

That's it! I hope this explanation is clear and that you know understand the four types of data measurement scales: nominal, ordinal, interval, and ratio! Go get em!

If you want to test your skills, give the brief quiz below a go (not working? Try on a desktop browser):

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Types of data measurement scales: nominal, ordinal, interval, and ratio

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In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data ( here's an overview of statistical data types) . This topic is usually discussed in the context of academic teaching and less often in the "real world."

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One sample T-test Part 1

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I demonstrate how to perform a one sample-test in SPSS using two different examples. The one sample t-test is appropriate for testing hypotheses about the difference between a sample mean and a population "mean" (or "mu") when the population standard deviation (sigma) is not known.

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One sample T-test Part 2

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(Part 2) I demonstrate how to perform a one sample-test in SPSS using two different examples. The one sample t-test is appropriate for testing hypotheses about the difference between a sample mean and a population "mean" (or "mu") when the population standard deviation (sigma) is not known.

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Paired sample T-test

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Paired Samples t Test: Using SPSS & Writing Up Your Results

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Paired sample T-test

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I demonstrate how to perform and interpret a paired samples t-test in SPSS. I also point out that many people fail to test the homogeneity of variance assumption in the paired samples t-test, but that this can be done relatively easily with a Pitman-Morgan test. paired t-test

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This manual is intended to be a guide to understanding what action research is, what are the advantages of implementing action research, and what is necessary for action research to be successful in a school. If there are any questions or concerns, please do not hesitate to contact us via e-mail.

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Action research design is an educational research involving collecting information regarding current educational programs and outcomes, analyzing the information, developing a plan to improve it, collecting changes after a new plan is implemented, and developing conclusions regarding the improvements. The main purpose of action research is to improve educational programs within schools. The four main types of action research design are individual research, collaborative research, school-wide research and district-wide research.

Individual Research

Individual action research is research conducted by one teacher or staff member. This type of research is conducted to analyze a specific task. A teacher may wonder if implementing group activities within an English class will help improve learning. The teacher alone performs research by implementing a group activity for a certain length of time. After the action is performed, the teacher analyzes the results, implements changes, or discards the program if not found to be helpful.

Collaborative Research

Collaborative research involves a group of people researching a specified topic. With collaborative research, more than one person is involved in the implementation of the new program. Typically, a group of students, larger than just one class, are tested, and the results are analyzed. Many times collaborative research involves both teachers and the principal of the school. This type of research offers the collaboration of many people working jointly on one subject. The joint collaboration often offers more benefits than an individual action research approach.

School-Wide Research

Action research programs are generally created from a problem found within an entire school. When a program is researched for an entire school, it is called school-wide action research. For this type of action research, a school may have concerns about a school-wide problem. This can be lack of parental involvement or research to increase students' performance in a certain subject. The entire staff works together through this research to study the problem, implement changes, and correct the problem or increase performance.

District-Wide Research

District-wide research is used for an entire school district. This type of action research is usually more community-based than the other types. This type may also be used to address organizational problems within the entire district. For district-wide research, staff from each school in the district, collaborates in correcting the problem or finding ways to improve the situation.

About the Author

Jennifer VanBaren started her professional online writing career in 2010. She taught college-level accounting, math and business classes for five years. Her writing highlights include publishing articles about music, business, gardening and home organization. She holds a Bachelor of Science in accounting and finance from St. Joseph's College in Rensselaer, Ind.

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What Are the Types of Action Research Design?

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Action research design is an educational research involving collecting information regarding current educational programs and outcomes, analyzing the information, developing a plan to improve it, collecting changes after a new plan is implemented, and developing conclusions regarding the improvements. The main purpose ...

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Action Research | Graduate School of Education

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Are you interested in using a teacher research process in your classroom? Explore Teacher Research and Action Research.

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Guiding School Improvement with Action Research

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Action research, explored in this book, is a seven-step process for improving teaching and learning in classrooms at all levels. Through practical examples, research tools, and easy-to-follow “impleme

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Part 2 of this video is available here, http://www.youtube.com/watch?v=ZZHvpgU7pc8 This video is a collaborative effort by Jana Duganzic, Taryn Durrant, Leya Finau, Nicola Firth and Melissa Frank. It is designed to introduce the topic of Action Reserach to primary school teachers. To demonstrate the theory, benefits and methodology of using Action Research in the classroom.

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MODEL-MODEL KAJIAN TINDAKAN Pada asasnya, setiap kajian tindakan akan melibatkan dua unsur, iaitu menyelidik dan bertindak. Kedua-dua unsu...