Introduction to Data Analysis Using SPSS

8/3/2019 Introduction to Data Analysis Using SPSS

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INTRODUCTION TO

DATA ANALYSIS USINGSPSSTypes of variables

Mean, median and mode


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Learning sequence

Univariate Bivariate Multivariate

Describing Describing Describing

complex

Distribution relationships relationship

Measures of Sampling distribution regressionanalysis

central tendency Hypothesis testing

Measure of spread e.g. Chi square


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Types of variables

Dependent Variables and IndependentVariables

An independent ( experimental or predictor) variable

is a variable that is being manipulated in anexperiment in order to observe the effect on adependent (outcome) variable.


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Scenario

Situation: A tutor asks 100 students to complete a maths test.

Research question:

The tutor wants to know why some students perform better than others.

Hypothesis:

The tutor thinks that it might be because of two reasons: some students spend more time revising for their test; and

some students are naturally more intelligent than others.

Procedure: The tutor decides to investigate the effect of revision time and intelligence on the test

performance of the 100 students.

The dependent and independent variables for the study are:

Dependent Variable: Test Mark (measured from 0 to 100)

Independent Variables: Revision time (measured in hours) Intelligence (measuredusing IQ score)


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Types of variables

Nominal allow for only qualitative classification/categorical.

measured only in terms of whether the individual itemsbelong to certain distinct categories, but we cannot quantifyor even rank order the categories

Ordinal is a nominal variable, but its different states are ordered in

a meaningful sequence.

Ordinal data has order, but the intervals between scale

points may be uneven.

Scale/ratio/interval can be measured along a continuum and they have a

numerical value (for example, temperature measured in

degrees Celsius or Fahrenheit).


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Summary

Nominal Ordinal Interval

Categorical? YES YES YES

Can rank? NO YES YES

Can measure theactual distancebetween data

points?

NO NO YES


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Descriptive Statistics

The analysis of data that helps describe, showor summarize data in a meaningful way suchthat, for example, patterns might emerge from

the data. DO NOT allow us to make conclusions beyond

the data we have analysed or reach

conclusions regarding any hypotheses wemight have made.

They are simply a way to describe our data.


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Distributions

Bars charts

Histogram

Normal distribution

Positive skew

Negative skew


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Measure of Central tendency

Ways of describing the central position of afrequency distribution for a group of data.

The mean, median and mode are all validmeasures of central tendency but, underdifferent conditions, some measures of central

tendency become more appropriate to usethan others.


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Summary

Type of VariableBest measure of central

tendency

Nominal Mode

Ordinal Median

Interval/Ratio (not skewed) Mean

Interval/Ratio (skewed) Median


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Significant?

Imagine this situation:

You are in a class with just four other students, andthe five

of you took a 5-point pop quiz.Today your instructor is walking around the room,

handing

back the quizzes.

She stops at your desk and hands you your paper.Written

in bold black ink on the front is 3/5

How do you react? Are you happy with your score


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Measuring the spread of data

The most common measure of variation, or spread, is the standarddeviation.

The standard deviation is a number that measures how far datavalues are from their mean.

Suppose that we are studying waiting times at the checkout line forcustomers at supermarket A and supermarket B;

the average wait time at both markets is 5 minutes.

At market A, the standard deviation for the waiting time is 2minutes;

At market B the standard deviation for the waiting time is 4minutes.

Market B has a higher standard deviation, we know that there ismore variation in the waiting times at market B.

Overall, wait times at market B are more spread out from the


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Standard deviation

For data having a distribution that is MOUND-SHAPED and SYMMETRIC:

Approximately 68% of the data is within 1 standarddeviation of the mean.

Approximately 95% of the data is within 2 standarddeviations of the mean.

More than 99% of the data is within 3 standarddeviations of the mean.


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Activity 1

Data Entering

Recoding variables


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Hypothesis testing

Define the research hypothesis and set the parameters forthe study.

Set out the null and alternative hypothesis (or more thanone hypothesis; in other words, a number of hypotheses).

Explain how you are going to operationalise (that is,

measure or operationally define) what you are studying andset out the variables to be studied.

Determine whether the distribution that you are studying isnormal (this has implications for the types of statistical teststhat you can run on your data).

Select an appropriate statistical test based on the variablesyou have defined and whether the distribution is normal ornot.

Run the statistical tests on your data and interpret theoutput.

Accept or reject the null hypothesis.


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Parametric and non parametrictest


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Chi Square Test for Association

to discover if there is a relationship betweentwo categorical variables.

Null hypothesis : No relation between X and Ywhen

H0: X02 = 0

There is a relationship between X and Y when

H1: X12 > 0


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P values

Indicate the extend to which the sample resultscan be generalized to the population

When p


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Activity

Introduction to Data Analysis Using SPSS

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