Psychology Assignment 1 2010_ Anne Percy
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Transcript of Psychology Assignment 1 2010_ Anne Percy
PSYCHOLOGY ASSIGNMENT COVER SHEET
Name: ____Anne Percy
_________________________________________
Address: __42 Cathcart street Lismore NSW 2480___________________________________________
_____________________________________________
_____________________________________________
Unit Code: BHS30001
Unit Name: Research Methods in Psychology
Student No: 83036730
Assignment No: One (1)
I have read and understand the rules relating to Awards (Rule 3.17) as contained in the University Handbook. I understand the penalties which apply for plagiarism and agree to be bound by these rules. The work I am submitting attached to this declaration is entirely my own work.
Signature:_________________________________ Date:_17/8/2010_________________
MARKERS COMMENTS:
Anne, Overall your assignment was well done, but you need to work on the correct formatting of your inferential statistical tests, and also make sure you know how to get SPSS to produce graphs with error bars. You also lost marks in question 3, because you did not answer all the question.
34/50
RETURN ASSIGNMENT TO:
Psychology AssignmentsBlock M – 1s t FloorSouthern Cross UniversityHogbin DriveCOFFS HARBOUR NSW 2450__________________________Receipt/dispatch enquiryTel: 02 6659 3301
School of Health & Human Sciences
Anne Percy
Date of Receipt: __________________________
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RESEARCH METHODS IN PSYCHOLOGY
Anne Percy
Southern Cross University
Subject: Research Methods in Psychology
Assignment: One
Student No: 83036730
Tutor: Ms Belinda Smith
Submitted: 17th August 2010
Course: Graduate Diploma of Psychology
EXTENSIONS PERMITTED –please refer to Disability Dispensation
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Question 1
SPSS data outputBetween-Subjects Factors
Value Label N
Group 1 Coffs Tute 1 34
2 Coffs Tute 2 21
3 Lismore 24
4 Online 22
Levene's Test of Equality of Error Variancesa
Dependent Variable:Age
F df1 df2 Sig.
3.939 3 97 .011
Tests the null hypothesis that the error variance of
the dependent variable is equal across groups.
a. Design: Intercept + Group
Tests of Between-Subjects Effects
Dependent Variable:Age
Source
Type III Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 3850.157a 3 1283.386 11.527 .000 .263
Intercept 86785.164 1 86785.164 779.459 .000 .889
Group 3850.157 3 1283.386 11.527 .000 .263
Error 10800.001 97 111.340
Total 101927.000 101
Corrected Total 14650.158 100
a. R Squared = .263 (Adjusted R Squared = .240)
Case Processing Summary
Group
Cases
Valid Missing Total
N Percent N Percent N Percent
Age Coffs Tute 1 34 100.0% 0 .0% 34 100.0%
Coffs Tute 2 21 100.0% 0 .0% 21 100.0%
Lismore 24 100.0% 0 .0% 24 100.0%
Online 22 100.0% 0 .0% 22 100.0%
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Descriptives
Group Statistic Std. Error
Age Coffs Tute 1 Mean 25.06 1.721
95% Confidence Interval for
Mean
Lower Bound 21.56
Upper Bound 28.56
5% Trimmed Mean 24.12
Median 20.00
Variance 100.663
Std. Deviation 10.033
Minimum 18
Maximum 49
Range 31
Interquartile Range 8
Skewness 1.590 .403
Kurtosis 1.072 .788
Coffs Tute 2 Mean 22.71 1.718
95% Confidence Interval for
Mean
Lower Bound 19.13
Upper Bound 26.30
5% Trimmed Mean 21.53
Median 20.00
Variance 62.014
Std. Deviation 7.875
Minimum 18
Maximum 49
Range 31
Interquartile Range 3
Skewness 2.803 .501
Kurtosis 7.344 .972
Lismore Mean 32.58 2.382
95% Confidence Interval for
Mean
Lower Bound 27.66
Upper Bound 37.51
5% Trimmed Mean 32.04
Median 29.50
Variance 136.167
Std. Deviation 11.669
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Minimum 19
Maximum 57
Range 38
Interquartile Range 22
Skewness .449 .472
Kurtosis -1.028 .918
Online Mean 39.00 2.593
95% Confidence Interval for
Mean
Lower Bound 33.61
Upper Bound 44.39
5% Trimmed Mean 38.90
Median 38.50
Variance 147.905
Std. Deviation 12.162
Minimum 20
Maximum 60
Range 40
Interquartile Range 21
Skewness .193 .491
Kurtosis -1.040 .953
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ANOVA
Age
Sum of Squares df Mean Square F Sig.
Between Groups 3850.157 3 1283.386 11.527 .000
Within Groups 10800.001 97 111.340
Total 14650.158 100
Contrast Coefficients
Contras
t
Group
Coffs Tute 1 Coffs Tute 2 Lismore Online
1 1 1 -2 0
2 1 1 1 -3
Contrast Tests
Contrast Value of Contrast Std. Error t df Sig. (2-tailed)
Age Assume equal variances 1 -17.39 5.209 -3.339 97 .001
2 -36.64 7.666 -4.780 97 .000
Does not assume equal
variances
1 -17.39 5.349 -3.252 35.438 .003
2 -36.64 8.491 -4.316 29.458 .000
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Multiple Comparisons
Age
Games-Howell
(I) Group (J) Group
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Coffs Tute 1 Coffs Tute 2 2.345 2.432 .770 -4.12 8.81
Lismore -7.525 2.938 .064 -15.36 .32
Online -13.941* 3.112 .000 -22.29 -5.59
Coffs Tute 2 Coffs Tute 1 -2.345 2.432 .770 -8.81 4.12
Lismore -9.869* 2.937 .009 -17.74 -2.00
Online -16.286* 3.111 .000 -24.66 -7.91
Lismore Coffs Tute 1 7.525 2.938 .064 -.32 15.36
Coffs Tute 2 9.869* 2.937 .009 2.00 17.74
Online -6.417 3.521 .277 -15.82 2.99
Online Coffs Tute 1 13.941* 3.112 .000 5.59 22.29
Coffs Tute 2 16.286* 3.111 .000 7.91 24.66
Lismore 6.417 3.521 .277 -2.99 15.82
*. The mean difference is significant at the 0.05 level.
ANOVA
Age
Sum of Squares df Mean Square F Sig.
Between Groups 3850.157 3 1283.386 11.527 .000
Within Groups 10800.001 97 111.340
Total 14650.158 100
Contrast Coefficients
Contras
t
Group
Coffs Tute 1 Coffs Tute 2 Lismore Online
1 1 1 -2 0
2 1 1 1 -3
Contrast Tests
Contrast Value of Contrast Std. Error t df Sig. (2-tailed)
Age Assume equal variances 1 -17.39 5.209 -3.339 97 .001
2 -36.64 7.666 -4.780 97 .000
Does not assume equal
variances
1 -17.39 5.349 -3.252 35.438 .003
2 -36.64 8.491 -4.316 29.458 .000
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A one-way between groups ANOVA was conducted to compare the ages of students enrolled in different
tutorial groups for Quantitative Methods at Southern Cross University in either 2008 or 2009. Table 1
shows the descriptive statistics for four tutorial groups: Coffs Harbour 1, Coffs Harbour 2, Lismore and
online group.
Table 1. Descriptive Statistics for the ages ofMean age of the 4 tutorial groups.___________________________________________________________________________ Tutorial Group Coffs H. 1 Coffs H. 2 Lismore Online___________________________________________________________________________M 25.06 22.71 32.58 39.00SD 10.03 7.88 11.70 12.16______________________________________________________________________
The histograms to check for normality show that Coffs Harbour tutorial 1, Coffs Harbour tutorial 2 and
Lismore groups are not normally distributed, but are positively skewed, while the histogram for the online
group is negatively skewed. The values for skewness were Coffs Harbour tutorial 1(z = 3.90), Coffs
Harbour tutorial 2(z = 5.60), Lismore (z = 1.00) and online (z = .40). The z scores for Coffs Harbour
tutorials 1 and 2 show a deviance from normality. The values for kurtosis were Coffs Harbour tutorial 1(z
= 1.40), Coffs Harbour 2 (z = 7.60), Lismore (z = -1.10) and online (z = -1.10). The scores for Coffs
Harbour tutorial 1 and Coffs Harbour tutorial 2 show a problem with normality. The Kolmogorov-
Smirnov Statistic for both Coffs Harbour tutorial groups and the Lismore group are significant, p = .05,
while the online group does not have a significant result. The Shapiro-Wilk Statistic for both Coffs
Harbour tutorial groups and the Lismore group are significant, p = .05, while the online group does not
have a significant result.
The analysis ANOVA shows there was a statistically significant difference between the tutorial groups’
ages, F(3,97), p = 11.53, two-tailed, Partial ή2 = 26%, two-tailed. Levene’s test showed the assumption
of homogeneity of variance was violated, F( 3,97) = 3.94 , p = .011. Since there were unequal sample
sizes, as well as heterogeneity of variance, the Games-Howell procedure was used to conduct post hoc
analyses using an alpha of .05. The analysis revealed that the mean ages of online students was were
significantly different from that of Coffs Harbour tutorial 1 and 2 students, and the mean age of the Coffs
Harbour tutorial 2 was significantly different to Lismore students. However there was no significant
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difference between the mean ages of Lismore and online students, or between Coffs Harbour tutorial 1
and Lismore students. The difference between Coffs Harbour tutorial 1 and tutorial 2 was not significant.
Two a priori linear contrasts using a Bonferroni adjusted alpha of .025 showed that the combined ages of
the Coffs Harbour tutorial groups differed significantly from the Lismore group (t(df) = -3.25, p = .003)
and the ages of the three internal groups (Coffs Harbour tutorial 1, tutorial 2 and Lismore) differed
significantly from the online group (t(df) = -4.32, p = .001).
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Question 2.
SPSS data outputBetween-Subjects Factors
Value Label N
gender 1 female 26
2 male 28
age 1 6-7 18
2 10-11 18
3 uni 18
Descriptive Statistics
Dependent Variable:colour squares recalled
gender age Mean Std. Deviation N
female 6-7 7.00 3.841 9
10-11 18.25 7.942 8
uni 18.22 7.902 9
Total 14.35 8.490 26
male 6-7 7.33 4.213 9
10-11 8.90 6.136 10
uni 18.78 5.652 9
Total 11.57 7.295 28
Total 6-7 7.17 3.915 18
10-11 13.06 8.292 18
uni 18.50 6.671 18
Total 12.91 7.941 54
Levene's Test of Equality of Error Variancesa
Dependent Variable:colour squares recalled
F df1 df2 Sig.
1.769 5 48 .137
Tests the null hypothesis that the error variance of
the dependent variable is equal across groups.
a. Design: Intercept + gender + age + gender *
age
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Tests of Between-Subjects Effects
Dependent Variable:colour squares recalled
Source
Type III Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 1547.026a 5 309.405 8.271 .000 .463
Intercept 9201.112 1 9201.112 245.976 .000 .837
gender 106.940 1 106.940 2.859 .097 .056
age 1162.546 2 581.273 15.539 .000 .393
gender * age 285.526 2 142.763 3.817 .029 .137
Error 1795.511 48 37.406
Total 12339.000 54
Corrected Total 3342.537 53
a. R Squared = .463 (Adjusted R Squared = .407)
1. gender
Dependent Variable:colour squares recalled
gender Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
female 14.491 1.201 12.075 16.906
male 11.670 1.157 9.344 13.997
2. age
Dependent Variable:colour squares recalled
age Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
6-7 7.167 1.442 4.268 10.065
10-11 13.575 1.451 10.658 16.492
uni 18.500 1.442 15.602 21.398
Estimates
Dependent Variable:colour squares recalled
gender age Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
female 6-7 7.000 2.039 2.901 11.099
10-11 18.250 2.162 13.902 22.598
uni 18.222 2.039 14.123 22.321
male 6-7 7.333 2.039 3.234 11.432
10-11 8.900 1.934 5.011 12.789
uni 18.778 2.039 14.679 22.877
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Pairwise Comparisons
Dependent Variable: colour squares recalled
age (I) gender
(J)
gender
Mean Difference
(I-J) Std. Error Sig.a
95% Confidence Interval for
Differencea
Lower Bound Upper Bound
6-7 female male -.333 2.883 .908 -6.130 5.464
male female .333 2.883 .908 -5.464 6.130
10-11 female male 9.350* 2.901 .002 3.517 15.183
male female -9.350* 2.901 .002 -15.183 -3.517
uni female male -.556 2.883 .848 -6.353 5.241
male female .556 2.883 .848 -5.241 6.353
Based on estimated marginal means
a. Adjustment for multiple comparisons: Bonferroni.
*. The mean difference is significant at the .05 level.
Univariate Tests
Dependent Variable:colour squares recalled
age Sum of Squares df Mean Square F Sig.
Partial Eta
Squared
6-7 Contrast .500 1 .500 .013 .908 .000
Error 1795.511 48 37.406
10-11 Contrast 388.544 1 388.544 10.387 .002 .178
Error 1795.511 48 37.406
uni Contrast 1.389 1 1.389 .037 .848 .001
Error 1795.511 48 37.406
Each F tests the simple effects of gender within each level combination of the other effects shown. These tests are
based on the linearly independent pairwise comparisons among the estimated marginal means.
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There was no significant main effect on colour recall for females (M=14.35,SD=8.49) being not so different for males (M=11.57,SD=7.295),F(1,5348)=2.895.Pp>0.05 ή2= 0.056However there was a highly significant difference between means of the three age groups fF(2,53548)=15.539, p<0.001 ή2=.393.
Gender by age interaction was also highly significant, fF(2,5348)=3.817.p<0.05,ή2=0.139. Closer examination of interaction effects using the Bonferroni adjustment analysis of simple effects found no significant difference between males and females in the 6-7 years age group p>0.05, or in the university age group with a p>0.05. However there was a significant difference between females and males in 10-11 age group p <0.05. This suggests that while there is no overall difference between females and males, females acquire memory recall for colours earlier than males, at age 10-11, and this could be further investigated.
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Question 3SPSS data output
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Saccade Reaction time 56 103.4310 494.6667 178.680501 57.7294329
Antisaccade Reaction time 55 177.2128 669.0000 290.583809 97.2893108
Percentage errors in blocked
trials
56 .0000 100.0000 14.231392 21.6555204
Percentage errors in switch
trials
56 .0000 96.9697 15.309059 21.9943627
Percentage errors in
repeated trials
56 .0000 100.0000 19.352716 21.6619078
Valid N (listwise) 55
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Saccade Reaction time 54 100.0% 0 .0% 54 100.0%
Descriptives
Statistic Std. Error
Saccade Reaction time Mean 173.082579 5.3386610
95% Confidence Interval for
Mean
Lower Bound 162.374581
Upper Bound 183.790577
5% Trimmed Mean 171.761476
Median 163.939338
Variance 1539.070
Std. Deviation 39.2309857
Minimum 103.4310
Maximum 263.7308
Range 160.2997
Interquartile Range 55.6382
Skewness .577 .325
Kurtosis -.368 .639
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Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Saccade Reaction time .154 54 .003 .955 54 .042
a. Lilliefors Significance Correction
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Descriptives
Statistic Std. Error
Antisaccade Reaction time Mean 283.576102 11.2972464
95% Confidence Interval for
Mean
Lower Bound 260.916695
Upper Bound 306.235509
5% Trimmed Mean 276.376946
Median 265.657143
Variance 6891.900
Std. Deviation 83.0174677
Minimum 177.2128
Maximum 587.4000
Range 410.1872
Interquartile Range 106.2804
Skewness 1.507 .325
Kurtosis 2.713 .639
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Antisaccade Reaction time .196 54 .000 .874 54 .000
a. Lilliefors Significance Correction
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Descriptives
Statistic Std. Error
Percentage errors in blocked
trials
Mean 11.117552 1.9717417
95% Confidence Interval for
Mean
Lower Bound 7.162739
Upper Bound 15.072365
5% Trimmed Mean 9.006633
Median 6.957048
Variance 209.939
Std. Deviation 14.4892834
Minimum .0000
Maximum 78.7879
Range 78.7879
Interquartile Range 11.8348
Skewness 2.780 .325
Kurtosis 9.343 .639
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Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Percentage errors in blocked
trials
.221 54 .000 .689 54 .000
a. Lilliefors Significance Correction
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Descriptives
Statistic Std. Error
Percentage errors in switch
trials
Mean 12.331355 2.1473244
95% Confidence Interval for
Mean
Lower Bound 8.024368
Upper Bound 16.638343
5% Trimmed Mean 10.176129
Median 6.781609
Variance 248.994
Std. Deviation 15.7795470
Minimum .0000
Maximum 86.6667
Range 86.6667
Interquartile Range 10.1149
Skewness 2.648 .325
Kurtosis 9.131 .639
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Percentage errors in switch
trials
.241 54 .000 .716 54 .000
a. Lilliefors Significance Correction
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Statistic Std. Error
Percentage errors in
repeated trials
Mean 16.702480 2.2910898
95% Confidence Interval for
Mean
Lower Bound 12.107136
Upper Bound 21.297824
5% Trimmed Mean 14.777447
Median 14.550265
Variance 283.451
Std. Deviation 16.8360027
Minimum .0000
Maximum 90.0000
Range 90.0000
Interquartile Range 17.9233
Skewness 2.045 .325
Kurtosis 6.036 .639
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Percentage errors in
repeated trials
.161 54 .001 .816 54 .000
a. Lilliefors Significance Correction
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SACCADEHistograms for saccade variable shows a “lumpy” shape ,Q-Q plot shows an almost near normal fit, however the Shapiro –Wilk test for normality demonstrates this group of scores is not normally distributed.
ANTISACCADEHistograms shows a positively skewed shape, Shapiro –Wilk is significant and therefore the sample is not normally distributed .Q-Q plot demonstrates a not normal fit.
PERCENTAGE ERRORS IN BLOCKED TRIALSHistograms demonstrate a strong positive skew indicating the distribution is not normally distributed. Shapiro –Wilk is significant and therefore the group is not normally distributed. Q-Q plot illustrates no goodness of fit, for normality.
PERCENTAGE ERRORS IN SWITCHED TRIALSHistograms is highly positively skewed the shape of then histogram is not normally distributed, Shapiro –Wilk statistic is significant therefore the group is not normally distributed. Q-Q plot demonstrates nil goodness of fit for normality.
PERCENTAGE ERRORS IN REJECTED TRIALS.Histogram demonstrates a positive skewed shape that is not normally distributed. Shapiro-Wilk is significant therefore this group of scores are not normally distributed, Q-Q plot shows scores are not normally distributed
Given that the outcomes for tests of normality have been violated, as well homogeneity of variance shows a significant result, and we have significant results for skewness and kurtosis .Shapiro-Wilk on all 5 variables tests for normality are significant using alpha =0.05 highlights we have a highly significant result. While ANOVA is robust to violation of assumptions of normality, it might be appropriate to transform raw scores, for example using square roots or logarithmic analysis. Otherwise it may be appropriate to use another form of analysis other than an ANOVA.
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Question 4
Effect size refers to the magnitude of findings, for example the correlation between two variables, or a
difference between two means. Effect size implies an interest in strength, importance and meaningfulness
of findings. Effect sizes can be used to evaluate how the effect compares with the effects of other
interventions.
There are several types of effect size based on either difference scores or correlations.
Standardised mean effect size such as (Cohen’s d and Hedges ‘ g) are essentially z scores .These effect
sizes indicate the mean difference between two variables expressed in standard deviation units. A score of
0 represents no change and effect size can be negative or positive.
Cohen’s d is appropriate only to a comparison of two treatment means; however for analysis of variance a
statistic called Cohen’s f is the appropriate measure of effect size. For f test across all factors the d-family
of measures are not generally appropriate (Howell.p.325). An alternative set of tests based on correlations
(r) between dependant variables and independent variables are more appropriate.
This set of measures tells us how much of the variability of the dependant variable can be assigned to the
treatment effect.]
The value r2 is called the coefficient of determination as it measures the proportion of variability in one
variable that can be determined for the relationship with the other variable (Gravetter and Wallnau,p.534).
Effect size for ANOVA, is r2 and is usually denoted as ή2.This is obtained from the ratio of the treatment
variability to the total variability, however this estimate can be biased because we are dealing with sample
means rather than population means.
Percentile scores based on the properties of the normal distribution can be used, as well, to aid in the
interpretation of standardised mean effects. Percentiles can be used to indicate, for example, where
someone who started on the 50th percentile could on average expect to end up following a “treatment”
(compared to people who didn’t who didn’t experience a “treatment”).
In some sense confidence intervals serve the function as classical inferential statistical tests, such as t-
tests, but are more informative because they indicate the spread in data around the mean effect size.
A confidence interval represents the likely range of the true population mean effect size based on the data
obtained from the sample. A 95% confidence interval is commonly chosen, however this could lead to
type 2 errors when conducted with small sample size.
The Publication Manual of the APA (APA, 2001, p. 22) strongly recommend the use of confidence
intervals when reporting results because confidence intervals combine information on location and
precision and can often be directly used to infer significance levels.
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