Statistics & Environment Solutions Consultants

door # 9 & 10, 6th Floor, Khaitan Mall, Block 6, Khaitan

P. O. Box 19076

Khaitan, Kuwait 83001

Kuwait

ph: +965-2473-5590

fax: +965-2473-5509

alt: +965-66119158

sscsecco

**Graph & Charts**

**1. Bar Charts**

A bar chart is helpful in graphically describing (visualizing) your data; it will often be used in addition to inferential statistics (see our Descriptive and Inferential Statistics guide). A bar chart can be appropriate if you are running an Independent T-Test or Dependent T-Test. The example we will use is based on the data from our Independent T-Test guide.

**2.Clusterd Bar Chart**

A clustered bar chart is helpful in graphically describing (visualizing) your data; it will often be used in addition to inferential statistics (see our guide on Descriptive and Inferential Statistics). A clustered bar chart can be appropriate if you are running a two-way ANOVA.

**3.Scatter Plot**

A scatterplot can be used to detemine whether a relationship is linear, detect outliers and graphically present a relationship. Determining whether a relationship is linear is an important assumption of correlation and simple regression. The example presented here is the same as in our guide on simple regression.

**Checking Assumptions**

**1. Testing for normality**

An assessment of the normality of data is a prerequisite for many statistical tests as normal data is an underlying assumption in parametric testing. There are two main methods of assessing normality - graphically and numerically.

**Pridicting Scores**

**1.Linear Regression**

Regression analysis is the next step up after correlation; it is used when we want to predict the value of a variable based on the value of another variable. In this case, the variable we are using to predict the other variable's value is called the independent variable or sometimes the predictor variable. The variable we are wishing to predict is called the dependent variable or sometimes the outcome variable.

**Association**

**1.Pearson 's corrolation**

The Pearson product-moment correlation coefficient is a measure of the strength and direction of association that exists between two variables measured on at least an interval scale. It is denoted by the symbol *r*. An introductory guide to this test is provided in our Statistical Guides section here and we recommend you read it if you are not familiar with this test.

**2. Spearsman 's corrolation**

The Spearman Rank Order Correlation coefficient, *r _{s}*, is a non-parametric measure of the strength and direction of association that exists between two variables measured on at least an ordinal scale. It is denoted by the symbol

**3.Chi Square For associations**

The Chi-Square test for independence, also called Pearson's Chi-square test or the Chi-square test of association is used to discover if there is a relationship between two categorical variables.

**Reliabilty**

**1.Cronbach Alpha**

Cronbach's alpha is the most common measure of internal consistency ("reliability"). It is most commonly used when you have multiple Likert questions in a survey/questionnaire that form a scale and you wish to determine if the scale is reliable.

**Differnce Between Groups**

**1,Independent T-test**

The independent t-test compares the means between two unrelated groups on the same continuous, dependent variable. The SPSS t-test procedure allows the testing of equality of variances (Levene's test) and the *t*-value for both equal- and unequal-variance. It also provides the relevant descriptive statistics. A statistical guide on the independent t-test is provided

**2. Dependent T-Test**

The dependent t-test (called the Paired-Samples T Test in SPSS) compares the means between two related groups on the same continuous variable. The SPSS t-test procedure also provides relevant descriptive statistics. For an easy-to-follow guide on the dependent t-test please see our statistical guide.

**3.One Way Anova**

The one-way analysis of variance (ANOVA) is used to determine whether there are any significant differences between the means of three or more independent (unrelated) groups. This guide will provide a brief introduction to the one-way ANOVA including the assumptions of the test and when you should use interpret the output. This guide will then go through the procedure for running this test in SPSS using an appropriate example, which options to choose and how to interpret the output. Should you wish to learn more about this test before doing the procedure in SPSS, please

**4. Anova With repeated Measures**

.An ANOVA with repeated measures is for comparing three or more group means where the participants are the same in each group. This usually occurs in two situations - when participants are measured multiple times to see changes to an intervention or when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared. For a complete guide on ANOVA with Repeated Measures, please go to our guide

**5. Two way Anova**

.The two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors). You need two independent, categorical variables and one continuous, dependent variable (see our guide on Types of Variable).

**6.One wau Manuva**

The one-way MANOVA is used to determine whether there are any differences between independent groups on more than one continuous dependent variable. In this regard, it differs from a one-way ANOVA only in measuring more than one dependent variable at the same time, unlike the one-way ANOVA that only measures one dependent variable.

**7.Mann-Whitney U Test**

The Mann-Whitney U Test is used to compare differences between two independent groups when the dependent variable is either (a) ordinal or (b) interval but not normally distributed. It is the nonparametric alternative to the independent t-test.

**8.Wilcoxon Singed Rank Test**

The Wilcoxon Signed-Rank Test is the nonparametric test equivalent to the dependent t-test. It is used when we wish to compare two sets of scores that come from the same participants. This can occur when we wish to investigate any change in scores from one time point to another or individuals are subjected to more than one condition. As the Wilcoxon Signed-Ranks Test does not assume normality in the data it can be used when this assumption has been violated and the use of the dependent t-test is inappropriate.

**9.Kruskal Wallis H Test**

The Kruskal-Wallis Test is the nonparametric test equivalent to the one-way ANOVA and an extension of the Mann-Whitney Test to allow the comparison of more than two independent groups. It is used when we wish to compare three or more sets of scores that come from different groups.

**10.Friedman Test**

The Friedman Test is the non-parametric alternative to the one-way ANOVA with repeated measures. It is used to test for differences between groups when the dependent variable being measured is ordinal. It can also be used for continuous data that has violated the assumptions necessary to run the one-way ANOVA with repeated measures; for example, marked deviations from normality.

Copyright 2011 Statistics & Environment Solutions Consultants. All rights reserved.

Statistics & Environment Solutions Consultants

door # 9 & 10, 6th Floor, Khaitan Mall, Block 6, Khaitan

P. O. Box 19076

Khaitan, Kuwait 83001

Kuwait

ph: +965-2473-5590

fax: +965-2473-5509

alt: +965-66119158

sscsecco