introduction to spss RESEARCH METHODS & STATISTICS HANDBOOK PHẦN 4 pdf

One-way ANOVA for Independent Samples In this case, we want to determine if there is a significant difference in the height to weight ratio between the three age groups in the sample in

the ranks scores for the other condition The mean ranks for each of these three levels are given, as well as the sums of the ranks for each and the number of cases that fall under each level

The main results are underneath this table, where the Z value and the p value are given The usual standard for levels of significance is used (if p is less than 0.05) How many cases are there where HWRATIO is greater than HWRATIO2?

Is there a significant difference between ranked height/weight ratios before and after the exercise/diet program?

WEEK 4: October 24th

ANOVAS

This practical will involve familiarising students with the analysis of variance (ANOVA) The ANOVAs used in this practical are when you may want to determine

if there is a significant difference between three or more groups when you have only a

single variable

One-way ANOVA for Independent Samples

In this case, we want to determine if there is a significant difference in the height to weight ratio between the three age groups in the sample in family.sav - children, adults and elderly We also want to carry out a Tukey‟s post-hoc test to identify where those difference lie, if any The procedure is remarkably similar to carrying out an

unrelated samples t-test Go: ANALYZE, COMPARE MEANS, ONE-WAY

ANOVA

As you can see, the layout of the dialogue box is basically the same as the one for unrelated t-tests from last week First select your Dependent variable(s) - in this case move the variable HWRATIO into the dependent list section Your factor (independent variable) is the variable AGEGRP Press the Continue button

Before running the analysis, press the Post-hoc button and turn on the Tukey‟s test Now press the Continue and Ok buttons and the analysis will be carried out

OUTPUT

There are two sections to the results for the one-way ANOVA

1 The first section indicates whether any significant differences exist between the different levels of the independent variable The between groups, within groups, sums of squares are listed, degrees of freedom, the F-ratio and the F-probability score (significance level) It is this last part that indicates significance If the F-prob is less than 0.05 than a significant difference exist In this case, the F-F-prob

is 0.000, so we can say that there is a statistically significant difference in height

to weight ratios between the three age groups

2 The post-hoc test identifies where exactly those difference lie The final part of the second section is a small table with the levels of the independent variable listed down the side Looking at the comparisons between these levels we see that children have a significantly higher mean height to weight ratio than adults and the elderly (this is also indicated by the asterixes)

For the meantime, ignore the third table of the output

One-way ANOVA for Related Samples

The procedure for running this is very different from anything you‟ve done before The first step is easy enough - you need to add a third height to weight ratio variable, representing the ratios for the subjects some time after they stopped doing the exercise/diet plan The data is below:

Variable Name: HWRATIO3

Variable Label: Height/Weight Ratio post-plan

Data: see table below

Subject Number HWRATIO3 score

The first step is to run a single factor ANOVA by going: ANALYZE, GENERAL

LINEAR MODEL, REPEATED MEASURES

The dialogue box is different from the usual format The first step is to give a name to the factor being analysed, basically the thing the three variables have in common All three variables cover height to weight ratios, so

 in the “With-in Subject Factor Name:” box type RATIO

 in the Number of Levels box, type 3 (representing the three variables)

 press the Add button, then the Define button

The next dialogue box is a bit more familiar In the right-hand column, there are three question marks with a number beside each Select each of the three variables to be included in the analysis, and move them across with the arrow button Notice how each of the variables replaces one of the question marks, indicating to SPSS which

three variables represent the three levels of the factor RATIO Then proceed by clicking on OK

OUTPUT

Firstly, you can ignore the sections of the output titled “Multivariate Tests” and

“Mauchly‟s Test of Sphericity”

You need to examine the section titled “Tests of Within-Subjects Effects” This section indicates whether any significant differences exist between the different levels

of the within subjects variable The degrees of freedom and sums of squares are listed,

as well as the F-score and its significance level If the significance level is less than 0.05 than a significant difference exist In this case, it is 0.001 (look at the measure for sphericity assumed), so we can say that there is a statistically significant difference in height to weight ratios between the three times when measurement were taken

You can ignore the section titled “Tests of Between-Subjects Effects” It is irrelevant here

To do a post-hoc test to identify where the differences lie, the SPSS for Windows made easy manual recommends doing Paired-Sample T-tests In this case

HWRATIO & HWRATIO2

HWRATIO & HWRATIO3

HWRATIO2 & HWRATIO3

From these three T-tests, you can determine which of the height to weight ratios are significantly different from each other

Kruskall-Wallis ANOVA (KWANOVA – Unrelated)

This is similar to the non parametric independent ANOVA, where ranks are used instead of the actual scores We will run the analysis on the same variables, so go

ANALYZE, NONPARAMETRIC TESTS, and K INDEPENDENT SAMPLES

As with the parametric test, move HWRATIO over to the test (dependent variable list and AGEGRP over to the Grouping (independent) variable list, and define the group with a minimum of 1 and a maximum of 3 Click the Ok button Notice that the non

parametric ANOVA doesn‟t have a post-hoc test If you run this ANOVA, you‟ll

have to consult a statistics book as to how to do a post hoc on the results One way would be to run a series of t-tests on all the combinations of the conditions

OUTPUT

The first section gives you the mean ranks and the number of cases for each level of the independent variable The second section lists the Chi-Square value, degrees of freedom and significance of the test

Is there a significant difference between the three groups (remember you can‟t say exactly what that difference is without a post hoc test)?

Friedman‟s - Related ANOVAs

This is similar to the nonparametric related samples ANOVA, where ranks are used instead of the actual scores We will run the analysis on the same variables, so go

ANALYZE, NONPARAMETRIC TESTS, and K RELATED SAMPLES

This is much easier to run - just move the three variables (HWRATIO, HWRATIO2

and HWRATIO3) over to the right column and click OK

OUTPUT

There is the Chi-square score, the d.f and whether it‟s significant (as usual, has to be less than 0.05) Again, for post-hoc tests, you‟ll probably have to consult a statistics book or possibly run three non-parametric related samples T-tests

WEEK 5: 30th October

Study Week

No Practical

WEEK 7: November 14th QUALITATIVE RESEARCH: STUDENT SEMINAR

PRESENTATION PREPARATION

Students should use this time to prepare work for their presentations Dr Alison will be available in his office for guidance if necessary

WEEK 8: November 21st QUALITATIVE RESEARCH: STUDENT SEMINAR

INTERVIEWING AND DISCOURSE ANALYSIS

conductig interviews etc

This period should be used to conduct interviews in preparation for the session on content analysis Students are expected to conduct interviews or sessions that result in naturally occurring language It is important that this material is transcribed in

preparation for week 11‟s session Dr Alison will be available for consultation

WORKING WITH NATURALLY OCCURING

LANGUAGE

PREPARATION

Students will use this period to work with their material gathered in the previous sessions They should use this time to prepare for presentations in the final practical session (12th December)

WORKING WITH NATURALLY OCCURING

LANGUAGE: STUDENT SEMINAR

Students are expected to organise their own seminar presentations in this session on the results and methods employed regarding the content analysis of their material

SECTION III EXTRA MATERIAL

For the benefit of students who wish to follow up other procedures in their own time,

we have included the following section which gives you some opportunity to play with graphics packages and explore some issues associated with regression in preparation for next term Try not to worry if this all sounds unfamiliar at first This section is simply to give you a running start when it comes to your work after Christmas

REGRESSION

Simple Regression

In simple regression, the values of one variable (the dependent variable (y in this case)) are estimated from those of another (the independent variable (x in this case))

by a linear (straight line) equation of the general form:

y‟=bo+b1(x)

where y‟ is the estimated value of y, b1 is the slope (known as the regression coefficient)

and bo is the intercept (known as the regression constant)

Multiple Regression

In multiple regression the values of one variable (the dependent variable (y)) are estimated form those of two or more variables (the independent variables (x1,

x2,…,xn)) This is achieved by the construction of a linear equation of the general form:

y‟=bo+b1(x1)+b2(x2)+…+bn(xn)

where the parameters b1,b2,…,bn are the partial regression coefficients and the intercept bo is the regression constant

Residuals

When a regression equation is used to estimate the values of a variable (y) from those

of one or more independent variables (x), the estimates (y‟) will not be totally accurate (i.e., the data points will not fall precisely on the straight line) The discrepancies between y (the actual values) and y‟ (the estimated values) are known

as residuals and are used as a measure of accuracy of the estimates and of the extent

to which the regression model gives a good account of the data in question

The multiple correlation coefficient

One measure of the efficacy of regression for the prediction of y is the Pearson correlation between the true values of the target variable y and the estimates y‟ obtained by substituting the corresponding values of x into the regression equation The correlation between y and y‟ is known as the multiple correlation coefficient (R (versus r which is Pearson‟s (the correlation between the target variable and any one independent variable)) In simple regression R takes the absolute value of r between the target variable and the independent variable (so if r=-0.87 than R=0.87)

Running Simple Regression

Using the family.sav file we want to look at how accurately we can estimate height to weight ratios (HWRATIO) using the subject‟s age (AGE) To run a simple

regression, choose ANALYSE, REGRESSION and LINEAR

As usual, the left column lists all the variables in your data file There are two sections for variables on the right The “Dependent” box is where you move the dependent variable Move HWRATIO there The “Independent(s)” box is where you move AGE

 Next click the STATISTICS button, and turn on the “Descriptive” option

 As already states, a residual is the difference between the actual value of the dependent variable and its predicted value using the regression equation Analysis

of the residuals gives a measure of how good the prediction is and whether there are any cases that should be considered outliers and therefore dropped from the analysis Click on “Case-wise diagnostics” to obtain a listing of any exceptionally large residuals

 Now click on CONTINUE

 Now click on the PLOTS button Since systematic patterns between the predicted

values and the residuals can indicate possible violations of the assumption of linearity you should plot the standardised residuals against the standardised predicted values To do this transfer *ZRESID into the Y: box and *ZPRED into

the X: box and then Click CONTINUE

 Now click Ok

Output

The first thing to consider is whether your data contains any outliers There are no outliers in this data If there were this would be indicated in a table labelled

“Casewise Diagnostics” and the cases that corresponded to these outliers would have

to be removed from your data file using the filter option you learned previously

With that out of the way, the first table (Descriptive Statistics) to look at is right at the top The first part gives the means and standard deviations for the two variables (e.g