{"id":114,"date":"2024-08-21T12:04:57","date_gmt":"2024-08-21T12:04:57","guid":{"rendered":"https:\/\/routledgelearning.com\/researchmethods\/?post_type=content&p=114"},"modified":"2024-09-13T10:25:37","modified_gmt":"2024-09-13T10:25:37","slug":"chapter-22-multi-level-analysis-differences-between-more-than-two-conditions-anova","status":"publish","type":"content","link":"https:\/\/routledgelearning.com\/researchmethods\/student-resources\/chapter-22-multi-level-analysis-differences-between-more-than-two-conditions-anova\/","title":{"rendered":"Chapter 22 – Multi-level analysis \u2013 differences between more than two conditions (ANOVA)"},"content":{"rendered":"\n
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\n\tHome<\/a>\n<\/span>\u203a<\/span>\n\tStudent Resources<\/a>\n<\/span>\u203a<\/span>\n\tChapter 22 \u2013 Multi-level analysis \u2013 differences between more than two conditions (ANOVA)\n<\/span><\/div>\n\n

Chapter 22 – Multi-level analysis \u2013 differences between more than two conditions (ANOVA)<\/h1>\n\n\n

In this chapter we look at designs where there is one independent variable but with more than two levels.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Exercises<\/h2>\n\n\n\n

Here is the data file oneway ANOVA caffeine.sav used in the main one-way ANOVA calculations in the chapter.<\/p>\n\n\n\n

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Download – oneway ANOVA caffeine<\/a><\/div>\n<\/div>\n\n\n\n

Exercise 22.1<\/h3>\n\n\n\n

Calculating one-way unrelated ANOVA<\/strong><\/p>\n\n\n\n

You will need one of these data sets for this exercise<\/p>\n\n\n\n

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Download – 1-way unrelated ANOVA<\/a><\/div>\n<\/div>\n\n\n\n

These are fictitious data supposedly collected from an experiment in which participants are given (with their permission) either Red Bull (a high caffeine drink), Diet Coke (moderate caffeine) or decaffeinated Coke (no caffeine, i.e., control group). They are then asked to complete a maze task where they have to trace round a maze to find the exit as quickly as possible.<\/p>\n\n\n\n

Carry out a one-way ANOVA analysis on the data either in SPSS, by using a spreadsheet programme or even by hand and make a full report of results. Include the use of a Tukeyb<\/sup> post-hoc test if possible. You should also report effect sizes if you can. If you are calculating by hand you could conduct simple effect t<\/em> tests between two samples at a time and adjust alpha accordingly.<\/p>\n\n\n\n

Show answer<\/summary>\n

F<\/em> (2, 34) = 6.661, p<\/em> = .004 (or < .01), partial eta2<\/sup> = .282, a very large effect size.<\/p>\n\n\n\n

Scores in the Red Bull group are significantly higher than scores in the caffeine-free group. This is shown by the Tukeyb<\/sup> test, which says that Red Bull and caffeine-free samples are in different subsets (non-homogenous) or by t<\/em> tests. The simple effect test between Red Bull and caffeine-free gives t <\/em>(22) = 3.76 or calculated by the Bonferroni method t<\/em> (22) = 3.65. Either way this is highly significant (p<\/em> < .01).<\/p>\n<\/details>\n\n\n\n

Exercise 22.2<\/h3>\n\n\n\n

Interpreting SPSS results for a one-way ANOVA<\/strong><\/p>\n\n\n\n

Shown below is the SPSS output after a one-way ANOVA has been performed on data where patients leaving hospital have been treated in three different ways, 1. traditionally (the control group), 2. with extra information (leaflet and video) given as they leave hospital and 3. with this information and a home visit from a health professional. The scores represent an assessment of their quality of recovery after three months. Have a go at answering the multiple-choice questions that appear below.<\/p>\n\n\n\n

Test of homogeneity of variances<\/td><\/tr>
Score<\/td> <\/td> <\/td> <\/td><\/tr>
Levene Statistic<\/td>df1<\/td>df2<\/td>Sig.<\/td><\/tr>
5.191<\/td>2<\/td>36<\/td>.010<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
ANOVA<\/td><\/tr>
Score<\/td> <\/td> <\/td> <\/td> <\/td> <\/td><\/tr>
 <\/td>Sum of Squares<\/td>df<\/td>Mean Square<\/td>F<\/td>Sig.<\/td><\/tr>
Between Groups<\/td>30.974<\/td>2<\/td>15.487<\/td>5.771<\/td>.007<\/td><\/tr>
Within Groups<\/td>96.615<\/td>36<\/td>2.684<\/td> <\/td> <\/td><\/tr>
Total<\/td>127.590<\/td>38<\/td> <\/td> <\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Score<\/td><\/tr>
Tukey B<\/td> <\/td> <\/td> <\/td><\/tr>
Type of post-op care<\/td>N<\/td>Subset for alpha = 0.05<\/td><\/tr>
1<\/td>2<\/td><\/tr>
Trad. care<\/td>13<\/td>5.3846<\/td> <\/td><\/tr>
Trad. care + inform.<\/td>13<\/td>6.7692<\/td>6.7692<\/td><\/tr>
Trad. care + inform. + visit<\/td>13<\/td> <\/td>7.5385<\/td><\/tr>
Means for groups in homogeneous subsets are displayed.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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