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:

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Anne Percy

Date of Receipt: __________________________

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Anne Percy

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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Anne Percy

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


Mean

Lower Bound 19.13

Upper Bound 26.30


Median 20.00

Variance 62.014


Minimum 18

Maximum 49

Range 31


Skewness 2.803 .501

Kurtosis 7.344 .972

Lismore Mean 32.58 2.382


Mean

Lower Bound 27.66

Upper Bound 37.51


Median 29.50

Variance 136.167


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Minimum 19

Maximum 57

Range 38


Skewness .449 .472

Kurtosis -1.028 .918

Online Mean 39.00 2.593


Mean

Lower Bound 33.61

Upper Bound 44.39


Median 38.50

Variance 147.905


Minimum 20

Maximum 60

Range 40


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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Anne Percy

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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Southern Cross University, 26/08/10,

This table is nicely presented, but a graph with error bars was requested for this question, and would have been preferable to the table.


Good!


This normally goes before the ANOVA.


I agree that the data deviated from normality, but this analysis was not required for this question.

Anne Percy

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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Indicate whether the Coffs students were older or younger than the Lismore students. 16.5 / 20

Anne Percy

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


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


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


gender Mean Std. Error



female 14.491 1.201 12.075 16.906

male 11.670 1.157 9.344 13.997

2. age


age Mean Std. Error



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


gender age Mean Std. Error



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


Differencea


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


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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Good interpretation of the results. 6/10


For your assignments, it is preferable to report the full outcome of the t-test.


Your F tests were nicely formatted for question 1 – stick to the same format for this question.


Means and SDs are not needed here if they are shown in a graph.


Give your figure a caption.


Create a graph with error bars using the Interactive graph menu in SPSS.

Anne Percy

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


Mean

Lower Bound 162.374581

Upper Bound 183.790577


Median 163.939338

Variance 1539.070


Minimum 103.4310

Maximum 263.7308

Range 160.2997

Interquartile Range 55.6382

Skewness .577 .325

Kurtosis -.368 .639

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Anne Percy

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


Antisaccade Reaction time Mean 283.576102 11.2972464


Mean




Median 265.657143

Variance 6891.900


Minimum 177.2128

Maximum 587.4000

Range 410.1872


Skewness 1.507 .325

Kurtosis 2.713 .639

Tests of Normality



Antisaccade Reaction time .196 54 .000 .874 54 .000


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Descriptives



trials

Mean 11.117552 1.9717417


Mean




Median 6.957048

Variance 209.939


Minimum .0000

Maximum 78.7879

Range 78.7879


Skewness 2.780 .325

Kurtosis 9.343 .639

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Tests of Normality




trials

.221 54 .000 .689 54 .000


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Descriptives



trials

Mean 12.331355 2.1473244


Mean




Median 6.781609

Variance 248.994


Minimum .0000

Maximum 86.6667

Range 86.6667


Skewness 2.648 .325

Kurtosis 9.131 .639

Tests of Normality




trials

.241 54 .000 .716 54 .000


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repeated trials

Mean 16.702480 2.2910898


Mean




Median 14.550265

Variance 283.451


Minimum .0000

Maximum 90.0000

Range 90.0000


Skewness 2.045 .325

Kurtosis 6.036 .639

Tests of Normality




repeated trials

.161 54 .001 .816 54 .000


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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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Good.


Did you delete any outliers? The outcome of these tests depends on whether outliers were deleted. Also, you haven’t looked at the skewness and kurtosis statistics for each of the variables. 4/10

Anne Percy

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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Overall, a comprehensive coverage of effect size and confidence intervals. 7.5/10

Psychology Assignment 1 2010_ Anne Percy

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