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Pearson Product-Moment Correlation - When you should run this test, the range of values the coefficient can take and how to measure strength of association.

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Understand when to use the Pearson product-moment correlation, what range of values its coefficient can take and how to measure strength of association.

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Many students, both in the undergraduate and graduate levels, have difficulty discriminating the theoretical from the conceptual framework. This requires a good understanding of both frameworks in order to conduct a good investigation. This article explains the two concepts in easily understandable language. Read on to find out.

Many graduating college students and even graduate students have difficulty coming up with the conceptual framework and the theoretical framework of their thesis, a required section in thesis writing that serves as the students' map on their first venture into research. The conceptual framework is almost always confused with the theoretical framework of the study.
What is the difference between the conceptual and the theoretical framework?
A conceptual framework is the researcher's idea on how the research problem will have to be explored. This is founded on the theoretical framework, which lies on a much broader scale of resolution. The theoretical framework dwells on time tested theories that embody the findings of numerous investigations on how phenomena occur.

The theoretical framework provides a general representation of relationships between things in a given phenomenon. The conceptual framework, on the other hand, embodies the specific direction by which the research will have to be undertaken. Statistically speaking, the conceptual framework describes the relationship between specific variables identified in the study. It also outlines the input, process and output of the whole investigation. The conceptual framework is also called the research paradigm.
Examples of the Theoretical and the Conceptual Framework
The difference between theoretical framework and conceptual framework can be further clarified by the following examples on both concepts:

Theoretical Framework: Stimulus elicits response.

Conceptual Framework: New teaching method improves students' academic performance.

Notice in the illustrative example that the theoretical framework basically differs from the conceptual framework in terms of scope. The theoretical framework describes a broader relationship between things. When stimulus is applied, response is expected. The conceptual framework is much more specific in defining this relationship. The conceptual framework specifies the variables that will have to be explored in the investigation. In this example, the variable "teaching method" represents stimulus while the "students' academic performance" represents the response. The variables make clear the kind of statistical treatment that will have to be used to analyze the relationship.
Image Source How can students come up with their theoretical and conceptual framework?
In order to come up with their own set of theoretical and conceptual framework that will guide the conduct of the research, students have to review literature pertaining to their chosen research topic. Students need to read a lot and find out what has been studied so far in their respective fields and come up with their own synthesis of the literature. They should look for gaps in knowledge and identify what questions need to be answered or what problems need to be given solutions. Thus, they will be able to formulate their own conceptual framework to serve as guide in their research venture.

For more information on examples of variables, visit simplyeducate.me, a free website devoted to research and statistics topics.
©Patrick Regoniel 29 August 2010 The Theoretical Framework | The Conceptual Framework

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What is the Difference Between the Theoretical and the Conceptual Framework?

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Many students, both in the undergraduate and graduate levels, have difficulty discriminating the theoretical from the conceptual framework. This requires a good understanding of both frameworks in order to conduct a good investigation. This article explains the two concepts in easily understandable language. Read on to find out.

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2-way ANOVA

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I perform and interpret a two way ANOVA in SPSS. The example consists of 2 between subjects factors: one with 2 levels and one with 3 levels. So, it's a 2 * 3 between subjects design. I test both main effects and the interaction effect. I then follow-up with some basic post-hoc tests.

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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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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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Reliability test Part 2

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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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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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Paired sample T-test

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

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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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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