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SPSS (the Statistical Package for the Social Sciences)

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## SPSS (the Statistical Package for the Social Sciences)

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**SPSS (the Statistical Package for the Social Sciences)**PART 2**Lesson objectives**• Recap SPSS • Data entry • Data view • Variable view • Descriptive analysis • Determining reliability • Inferential Statistics with SPSS**Inferential Statistics**• Based on the assumption that the sample is random • Types of tests • Chi Squared • Correlation • T test**Example research**Purpose : To determine if a certain method of teaching will lead to higher achievement among visual learners**Design**Independent Variable INTERVENTION Teaching method Dependent Variable Learning styles Achievement Satisfaction • Population: EDU 540 students (154) • Sample ( chosen at random, 3 lessons taught by the same person using the ‘method’) • Class 1 (30) • Class 2 (45) • Class 3 (38)**Instruments**• Learning style inventory • Scores will determine learning styles • Can categorize as visual, tactile or auditory • Questionnaire • Satisfaction regarding the teaching method • higher score – higher lesson satisfaction • Test • Scores will determine achievement**What to describe?**• Descriptive stats • Age • Gender • Program • Learning styles • Cross tabulate? • Gender and learning styles**Significance**• If significant, unlikely to have occurred by chance • there is statistical evidence that there is a difference, a correlation, an association between etc….**Significance level**• Significance levels show you how likely a result is due to chance. • The most common level, used to mean something is good enough to be believed, is .95. • The finding has a 95% chance of being true. • No statistical package will show you "95%" or ".95" to indicate this level. Instead it will show you ".05," meaning that the finding has a five percent (.05) chance of not being true, which is the converse of a 95% chance of being true. • To find the significance level, subtract the number shown from one. For example, a value of ".01" means that there is a 99% (1-.01=.99) chance of it being true**Hypothesis testing**• The Null hypothesis states there is no true difference/no relationship between parameters in the population • We reject or accept the null hypothesis • It is rejected only when it becomes evidently false, that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis • Example • There is no significant difference between the mean test scores of visual and tactile learners**Hypothesis testing**YOU ALWAYS TEST THE NULL HYPOTHESIS!**Significance**• Test of significance • To decide whether to accept or reject the null hypothesis • Select probability • 5 out of 100 times the difference did not occur by chance ( Significance level: 0.05) • 1 out of 100 times the difference did not occur by chance ( Significance level: 0.01) • Confidence level? • 95% or 99%**Example**• Null hypothesis • There is no relationship between variables.. • Significance level : 0.05 • Test statistic • Probability value 0.009 or Sig. 0.009 (smaller than 0.05) • What does that mean? • very unlikely that there’s no relationship between the variables • Variables not independent of each other • REJECT Null hypothesis**Example**• Null hypothesis • There is no relationship between variables.. • Significance level : 0.01 • Test statistic • Probability value 0.12 or Sig. 0.12(greater than 0.01) • What does this mean? • Higher likelihood that there’s no relationship between the variables • Variables are independent of each other • ACCEPT Null hypothesis**Now.. What to infer?**• Independence/ Association • Correlation • Differences**Independence test –Chi squared**• Chi squared test is used in situations where you have two categorical variables • Gender and employment sector • Gender and learning styles • Chi-square test of independence tests the null hypothesis that there is no association between the two variables**Example: Test for independence**• Gender • Female • Male • Learning styles • Visual • Tactile • Auditory • Null Hypothesis: No association between gender and learning styles**Using SPSS for chi squared**• Click • Analyze • Descriptive • Crosstabs • Statistics**Using SPSS for chi squared**• Low chi squared statistic • Sig.961 • Accept null hypothesis • There is no association… • Variables independent of each other**Correlation**• Measure of the linear relationship between two variables. • A correlation coefficient has a value ranging from -1 to 1. • Values that are closer to the absolute value of 1 indicate that there is a strong relationship between the variables being correlated whereas values closer to 0 indicate that there is little or no linear relationship. • The sign of a correlation coefficient describes the type of relationship between the variables being correlated. • A positive correlation coefficient indicates that there is a positive linear relationship between the variables: as one variable increases in value, so does the other. • A negative value indicates a negative linear relationship between variables: as one variable increases in value, the other variable decreases in value.**Example: Correlation**• Correlation between learning styles and test scores • Correlation between learning styles and satisfaction**Correlation in SPSS**• Start at the Analyze menu. • Select the Correlate option from this menu. You will see three options for correlating variables: • Bivariate • Partial • Distances. • The bivariate correlation is for situations where you are interested only in the relationship between two variables**Correlation in SPSS**• Then, consider is the type of correlation coefficient. • Pearson's is appropriate for continuous data • Kendall's tau-b and Spearman's, are designed for ranked data. • The choice between a one and two-tailed significance test in the Test of Significance box should be determined by the hypothesis you are testing • if you are making a prediction that there is a negative or positive relationship between the variables, then the one-tailed test is appropriate • if you are not making a directional prediction, you should use the two-tailed test (there is not a specific prediction about the direction of the relationship between the variables)**Output**• Correlation is not statistically significant**Let’s check for significant difference**Differences between test scores of the groups of learners**Differences: Using t test**• The t test is a useful technique for comparing mean values of two sets of numbers. • Statistic for evaluating whether the difference between two means is statistically significant. • t tests can be used either • to compare two independent groups (independent-samples t test) • to compare observations from two measurement occasions for the same group (paired-samples t test).**Remember**t test - tests the null hypothesis / that there is no difference …**t test**• If you are using the t test to compare two groups, the groups should be randomly drawn from normally distributed and independent populations. • Using SPSS • Analyze • Compare Means One-Sample T test... Independent-Samples T test... Paired-Samples T test...**Types of t-test**• The one-sample t test is used compare a single sample with a population value. • Example, a test could be conducted to compare the average test scores of U5C with a value that was known to represent the whole EDU 540 population. • The independent-sample t test is used to compare two groups' scores on the same variable. • Example : Compare the test scores of U5C and PKPG to evaluate whether there is a difference in their scores. • The paired-sample t test is used to compare the means of two variables within a single group. • Example, it could be used to see if there is a statistically significant difference between test 1 and test 2 among the members of U5C**Output**• Notice the two parts of the output • Equal variances assumed • Equal variance not assumed • Which to use? • Look at Levene’s test for equality of variance • If small Sig. - groups have unequal variances**Output**• t-statistics is -6.024 • Sig. level : .000 • The significance level tells us that the probability that (there is no difference between visual and tactile learners) – the “NULL” is very small • Hence, there is a significant difference in the test scores between visual and tactile learners**Have fun with SPSS!**Proceed to Qualitative Analysis and Ethics in Research