ANCOVA Analysis for Videogame Training and Divided Attention

This document discusses the ANCOVA analysis conducted to investigate the effect of videogame training on people's ability to divide attention across multiple sources of information. The study includes three variables: videogame group, divided attention score, and age. The results of the analysis are presented along with descriptive statistics and Levene's test of equality of error variances.

PORTFOLIO

Table of Contents
EXERCISE 1...................................................................................................................................2
Reflection.........................................................................................................................................2
EXERCISE 2...................................................................................................................................4
Descriptive statistics........................................................................................................................4
Factorial ANOVA............................................................................................................................5
Recommendation.............................................................................................................................7
EXERCISE 3...................................................................................................................................9
A. ANCOVA Analysis................................................................................................................9
B. One-way ANOVA Analysis..................................................................................................12
MANOVA exercise.......................................................................................................................13
C. Descriptive statistics.............................................................................................................13
D. MANOVA Analysis.............................................................................................................14
E. Explanation............................................................................................................................15
EXERCISE-4.................................................................................................................................16
Introduction....................................................................................................................................16
METHOD......................................................................................................................................17
RESULTS......................................................................................................................................17
DISCUSSION................................................................................................................................19
REFERENCES..............................................................................................................................20
APPENDIX....................................................................................................................................21
APPENDIX-1............................................................................................................................21
APPENDIX-2............................................................................................................................24
APPENDIX-3............................................................................................................................27
1

EXERCISE 1
Reflection
BPS code of ethics is a full fledged designed code of ethics which guide all the members of
British Psychological Society to carry out their professional conduct. These codes help
psychologists to ensure the ethical treatment of participants. In this reflection multiple studies
which are previously published will be used to show that how data from participants is gathered
without crossing boundaries of ethics.
There are major principles which help in best practice in ethics review; these four principles
include independence, competence, facilitation and transparency & accountability. The first
principle of independence states that the virtue of review of ethics must be independent from the
research itself so that any conflict of interest between researchers and the auditor who is
reviewing the ethics protocol must work between governed structures. This principle further adds
that the person who is investigating the ethics review protocol must be different and independent
from the investigator as by this way ethical conduct of a study can be ensured.
Another principle of BPS code of ethics is competence which states the ethics review
protocol of a research must be investigated by a competent body. The investigators who can form
this body of evaluating ethics review protocol must have proper expertise and must have a proper
training in this process. This principle has the agenda that every research must be checked by
competent reviewers so that ethical treatment of participants can be ensured.
Third principle of BPS codes of ethics is Facilitation; under this principle the review body of
ethics must facilitate the researcher to educate them about the ethical implications which their
research requires it imply. The vision of this principle is to invoke the responsibility of ethics
review body to educate and support the researchers.
The fourth and last principle of BPS code of ethics is transparency and accountability. This
principle states that the process of reviewing the ethical protocol of a study must be accountable
and open for scrutiny so that whenever any misleading ethics review can be undertaken it can be
appropriately located by the scrutiny process. This principle ensures transparent ethics review.
All the above principles of BPS code ensure informed consent, confidentiality, deception,
debriefing and right to withdraw elements in a research.
2

It is important for investigators to follow all the BPS codes and principles to ethical conduct
their study. The study conducted by (Best, 2010) and (Repovš and Baddeley, 2006) are good
examples of studies which are undertaken by ensuring every principle of BPS codes. These
studies have asked for a consent from their participants and even have maintained anonymity
while presenting their results of the study. Few more examples of ethical investigations are
(Hafer and Begue, 2005) and (Koole, Greenberg and Pyszczynski, 2006); these studies have also
followed all ethical guidelines.
Out of the published studies stated above, there are few which also has some ethical
pitfalls. The study of (Hafer and Begue, 2005) has avoided the ethical pitfall of reward system
due to which the responses of participants could have become biased. These researchers should
have awareness of complexity of ethics which could have helped them in making better
judgements.
3

EXERCISE 2
A study has been conducted to investigate the effect of reward on procrastination. The
participants of this study will be 60 undergraduate psychology students. These 60 students are
divided into two criterions or factors. The first criteria or variable is the level of reward; there are
three levels or values of this variable which are “no reward”, “small reward” and “large reward”.
The second factor or variable is the task emotion which has two level and those are “pleasant”
and “unpleasant”. All these variables are recorded under a SPSS datasheet which is intended to
be used to conduct Factor ANOVA. This technique of data analysis will help in determining the
impact of reward and emotional manipulation on the procrastination.
The 60 participants are provided with numerical math quizzes and then the data is recorded
based on the time in which each participant has completed their quiz. All the data will be used to
conduct factor ANOVA and to conclude the results.
Descriptive statistics
Descriptive statistics includes measures of central tendency and variance that helps in developing
the familiarity with a variable. In present investigation of procrastination, descriptive statistics of
three variables are summarized and the table of descriptive statistics is attached below including
the summary statistics of task emotion, reward and time.
Statistics
taskemotion Reward Time
N Valid 60 60 60
Missing 0 0 0
Mean 1.50 2.00 13.90
Median 1.50 2.00 14.00
Mode 1a 1a 14
Std. Deviation .504 .823 2.729
Variance .254 .678 7.447
Range 1 2 11
Minimum 1 1 9
Maximum 2 3 20
a. Multiple modes exist. The smallest value is shown
4

Factorial ANOVA
Factor ANOVA is a two way ANOVA that allows to analyze the impact of two independent
variables on one dependent variable. In this case one dependent variable is “time” or what we
can call it procrastination. Besides this, the two independent variables are reward having three
values and emotion having two values. The entire Factorial ANOVA results gained from SPSS
output file are attached in APPENDIX-1 and few essential tables from the results are attached
below along with analysis.
Descriptive Statistics
Dependent Variable: time
taskemotion reward Mean Std. Deviation N
pleasant none 13.00 2.708 10
sweets 12.40 2.221 10
money 12.40 1.897 10
Total 12.60 2.238 30
unpleasant none 16.90 2.514 10
sweets 15.80 1.549 10
money 12.90 1.792 10
Total 15.20 2.578 30
Total none 14.95 3.236 20
sweets 14.10 2.553 20
money 12.65 1.814 20
Total 13.90 2.729 60
5

Above descriptive statistics table and plot graph are observed and helped in summarizing
that mean time taken by pleasant emotion participants is lower from the mean time taken by
unpleasant emotion participants emphasizing that when participants were told that they are under
observation to complete a math test and not a fun exercise, they took higher time. Also, the
overall time taken by participants participated for reward was much higher than the participants
participated for no or small reward.
Tests of Between-Subjects Effects
Dependent Variable: time
Source
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 189.200a 5 37.840 8.167 .000
Intercept 11592.600 1 11592.600 2502.000 .000
taskemotion 101.400 1 101.400 21.885 .000
reward 54.100 2 27.050 5.838 .005
taskemotion * reward 33.700 2 16.850 3.637 .033
Error 250.200 54 4.633
Total 12032.000 60
Corrected Total 439.400 59
a. R Squared = .431 (Adjusted R Squared = .378)
6

The above table is used to determine that whether there is an interaction between two
independent variables of the study or not. The row of task emotion * reward will analyze
whether interaction between these two variables is statistically significant or not. As the
significance or p value of this interaction is .033 which is lower than .05, the interaction between
these two variables is significant.
Multiple Comparisons
Dependent Variable: time
Tukey HSD
(I) reward (J) reward
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
None sweets .85 .681 .430 -.79 2.49
money 2.30* .681 .004 .66 3.94
sweets none -.85 .681 .430 -2.49 .79
money 1.45 .681 .093 -.19 3.09
money none -2.30* .681 .004 -3.94 -.66
sweets -1.45 .681 .093 -3.09 .19
Based on observed means.
The error term is Mean Square(Error) = 4.633.
*. The mean difference is significant at the .05 level.
The above table is the multiple comparison table based on the post hoc test of Tukey. The above
table shows the interactions between all the variables and their values. Since, the significance
level values are less than 0.5, it can be said that the time taken for completion of the task is
different for each category of reward; concluding reward has a significant impact on
procrastination.
Recommendation
In a scenario, where it has been ascertained that the school children who do a lots of number
puzzles are more inclined to secure higher scores in GCSE math examinations and a school math
teacher wants to incorporate these puzzles into her lessons, there are few recommendations for
the math school teacher which should be considered by her to ensure effective results of the
students:
7

The math teacher should make sure that her students take this math numeric puzzles as a fun
exercise and not an exercise of testing their math ability. This recommendation is provided after
analyzing the findings which state that students who take their numeric puzzle as a fun exercise
(pleasant) takes lesser mean time to complete the puzzles than the students who consider these
numeric puzzles as a test of their math ability (unpleasant).
Another recommendation for the math teacher is to include some sort of reward against the
completion of math puzzle faster as it will induce the motivation of students and they will
complete the math puzzle in less time. This recommendation is based on the experiment
conducted in the study. The findings of this experiment study stated that the students who were
promised to gain a reward observed to complete the math puzzle a lot faster than the students
who were not participating for any reward.
8

EXERCISE 3
A. ANCOVA Analysis
ANCOVA analysis is the one-way ANCOVA analysis in which there are two or more
independent variables with one dependent variable and a covariate. In the present case, the aim
of undertaking an ANCOVA exercise is to investigate whether training in videogames can
improve the people’s ability to divide attention across multiple sources of information. For this,
there are three variables in this study. The first variable is the videogame group, these variable
has three values, first group is the control group who was not given video game training at all,
the second group was the group of people who was given video game training of 10 hours per
week for 3 weeks and the last group is of people who were given training of 20 hours per week
for 3 weeks. Second variable in this study is the divided attention score which is a scale variable
and lastly third variable is age of each participant which is also a scale variable.
For conducting ANCOVA, videogame group is an independent or fixed variable, divided
attention is a dependent variable and age is a covariate. It is assumed for this analysis that the
data set is normal and no normality test is required to be conducted. The results of the analysis
are attached below with analysis.
9

Between-Subjects Factors
Value Label N
Videogame group 1 Control group 10
2 10 hrs/week 10
3 20 hrs/week 10
Descriptive Statistics
Dependent Variable: Divided attention (DV)
Videogame group Mean Std. Deviation N
Control group 21.70 8.642 10
10 hrs/week 21.70 3.199 10
20 hrs/week 25.50 3.979 10
Total 22.97 5.881 30
Levene's Test of Equality of Error Variancesa
Dependent Variable: Divided attention (DV)
F df1 df2 Sig.
4.261 2 27 .025
Tests the null hypothesis that the error variance of the
dependent variable is equal across groups.
a. Design: Intercept + AgeCOV + Videogamegroup
The above descriptive statistics table shows the mean divided attention for each of the
three groups. This table helps in analyzing that the group which was trained on video games for
20 hours per week for 3 weeks has the highest mean score of divided attention. Also, the mean
score of control group and the group which has 10 hours of training are same which implies that
the training on video games is only viable for divided attention when the training is for at least
20 hours per week for 3 weeks.
10

Tests of Between-Subjects Effects
Dependent Variable: Divided attention (DV)
Source
Type III Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 398.313a 3 132.771 5.709 .004 .397
Intercept 3022.675 1 3022.675 129.974 .000 .833
AgeCOV 302.046 1 302.046 12.988 .001 .333
Videogamegroup 192.592 2 96.296 4.141 .027 .242
Error 604.654 26 23.256
Total 16827.000 30
Corrected Total 1002.967 29
a. R Squared = .397 (Adjusted R Squared = .328)
The above descriptive statistics table has already helped in concluding that the three
groups has few differences in divided attention score, but the above table of “Tests of Between
Subject Effects” will help in finding that whether these differences are significant or not. The
row of Videogamegroup in above table has a significance or p value of .027. As this p value is
less than 0.05, it can be implied that the divided attention mean is statistically different in three
groups of Video games.
Estimates
Dependent Variable: Divided attention (DV)
Videogame group Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
Control group 20.618a 1.554 17.423 23.813
10 hrs/week 21.654a 1.525 18.519 24.789
20 hrs/week 26.628a 1.557 23.428 29.828
a. Covariates appearing in the model are evaluated at the following values: Age (COV) =
28.63.
The above estimates table is representing the adjusted mean difference which has been
occurred in three groups due to the age variable which is the covariate.
11

Pairwise Comparisons
Dependent Variable: Divided attention (DV)
(I) Videogame group (J) Videogame group
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
Control group 10 hrs/week -1.036 2.176 1.000 -6.604 4.531
20 hrs/week -6.011* 2.242 .038 -11.748 -.273
10 hrs/week Control group 1.036 2.176 1.000 -4.531 6.604
20 hrs/week -4.974 2.181 .093 -10.556 .607
20 hrs/week Control group 6.011* 2.242 .038 .273 11.748
10 hrs/week 4.974 2.181 .093 -.607 10.556
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.
Univariate Tests
Dependent Variable: Divided attention (DV)
Sum of Squares df Mean Square F Sig.
Partial Eta
Squared
Contrast 192.592 2 96.296 4.141 .027 .242
Error 604.654 26 23.256
The F tests the effect of Videogame group. This test is based on the linearly independent pairwise comparisons
among the estimated marginal means.
The analysis has already concluded that there are significant differences in the three
groups but the post hoc tests or the pairwise comparison table above will help in analyzing which
specific groups has significant differences. From the above pairwise comparison table, it is clear
that only control group and 20-hour group has significance value lower than 0.05 implying
control group and 20-hour group has significant differences in mean divided attention.
12

B. One-way ANOVA Analysis
One-way ANOVA analysis is conducted to eliminate the covariate of age and then
analyze the results to interpret that whether the results show any differences or not.
Descriptives
Divided attention (DV)
N Mean
Std.
Deviation Std. Error
95% Confidence Interval for
Mean
Minimum MaximumLower Bound Upper Bound
Control group 10 21.70 8.642 2.733 15.52 27.88 10 37
10 hrs/week 10 21.70 3.199 1.012 19.41 23.99 15 25
20 hrs/week 10 25.50 3.979 1.258 22.65 28.35 19 33
Total 30 22.97 5.881 1.074 20.77 25.16 10 37
ANOVA
Divided attention (DV)
Sum of Squares df Mean Square F Sig.
Between Groups 96.267 2 48.133 1.433 .256
Within Groups 906.700 27 33.581
Total 1002.967 29
In the above descriptive statistics table, it can be clearly seen that the control group mean is
different than the mean of 20 hour group but the ANOVA table is reflecting significance value of
.256 implying that the difference between these groups are not significant. The results of one
way ANOVA and one way ANCOVA are quite opposite as one way ANCOVA implies the
differences between the groups are significant but one way ANOVA suggests that these
differences are not significant. So, it can be said that the covariate of age holds major importance
in this scenario. So, it can be said that the variable of age (covariate) does makes a difference in
the results and the difference is caused due to the variance in mean and adjusted mean values.
13

MANOVA exercise
C. Descriptive statistics
Statistics
T1
Body
Weight
Score
T1
Preoccupation
Score
T2
Body
Weight
Score
T2
Preoccupation
Score
T3
Body
Weight
Score
T3
Preoccupation
Score
T4
Body
Weight
Score
T4
Preoccupation
Score
N Valid 21 21 21 21 21 21 21 21
Missing 73 73 73 73 73 73 73 73
Mean 1.05 1.29 2.38 2.19 2.33 2.29 2.86 2.38
Median 1.00 1.00 2.00 2.00 2.00 2.00 3.00 2.00
Mode 1 1 1 2 1 2 4 3
Std.
Deviation .218 .463 1.359 .602 1.354 .644 1.315 .669
Variance .048 .214 1.848 .362 1.833 .414 1.729 .448
Range 1 1 3 2 3 2 3 2
Minimum 1 1 1 1 1 1 1 1
Maximum 2 2 4 3 4 3 4 3
Sum 22 27 50 46 49 48 60 50
D. MANOVA Analysis
The MANOVA analysis undertaken in present case is one way MANOVA where one
dependent variable which is time is considered along with two independent variables of body
weight and preoccupation. The time variable is a group variable having 4 groups, each reflecting
a year. The aim behind conducting this analysis is to determine the impact of time on
preoccupation and body weight. The two dependent variables are the symptoms of people which
represent Body weight (where 1 = pathologically low, 4 = normal and stable) and Preoccupation
with food/losing weight (where 1 = cannot concentrate on anything else, 4 = no preoccupation).
The complete results of the MANOVA analysis are attached in Appendix and few
important tables are attached below along with analysis.
14

Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model Weight 37.845a 3 12.615 9.247 .000
Preocc 16.131b 3 5.377 14.956 .000
Intercept Weight 390.012 1 390.012 285.873 .000
Preocc 348.107 1 348.107 968.245 .000
Time Weight 37.845 3 12.615 9.247 .000
Preocc 16.131 3 5.377 14.956 .000
Error Weight 109.143 80 1.364
Preocc 28.762 80 .360
Total Weight 537.000 84
Preocc 393.000 84
Corrected Total Weight 146.988 83
Preocc 44.893 83
a. R Squared = .257 (Adjusted R Squared = .230)
b. R Squared = .359 (Adjusted R Squared = .335)
The descriptive statistics table has already clearly shown that the mean score of weight
and preoccupation is different in all years but whether this difference is significant or not will
analyzed using above table of “Tests of Between-Subjects Effects”. The row of time is required
to be considered which has significance values. The p or significance values of both the variables
which is wight and preoccupation is 0.000 that is less than .05 implying that the difference in
weight and preoccupation is different in each year or period. In additional the multiple
comparisons table can also reflect that which combination or years has significant difference or
not. Although, it is already proven that the body weight and preoccupation with food differs in
each year.
15

E. Explanation
By including a covariate variable such as height and build would have helped in gaining even
more effective results as co variate also provides a table of estimates which helps in observing
the adjusted means that provides accurate results of difference between years.
16

EXERCISE-4
A study to test visuo-spatial working memory of children and adults
Introduction
A study has been conducted to analyse the differences in the mean scores in working memory
model and visuo-spatial sketchpad of children and adults. This study is designed to investigate
impact of the age on the scores of working memory model and visuo-spatial sketchpad.
The rationale of this study is to conduct a developmental research that can analyse the
differences in the scores. The research of (Logie and Pearson, 1997) includes the experiment
which explored the dissociation between visual and spatial memory of children. Considering this,
the present study is justified to carry out as it is aimed to analyse whether ability of children’s
spatial and visual memory is different from adults or not.
This study is based two hypothesis which are:
 The mean scores of children’s corsi span (visual) are different than adults.
 The mean scores of children’s recall of design (spatial) are different than adults.
The first model which is used in this study is working memory model. This model is used to test
the short-term memory of a human and is based on the concept of temporary storage capacity
and the process for manipulating the stored information. In this model, the participants are given
nine wooden blocks each having a design sequence. These boxes have designs and numbers, the
numbers can be seen by the investigator and the designs can be seen by the participants. The test
is to identify and replicate the exact design which investigator has shown the participant. The test
scores of this working memory model are recorded by level of appropriateness that participants
have shown while replicating the design shown on the 9 wooden blocks. The test results of such
test are shown “Corsi span”.
The second model that has been used in this study is visuo-spatial memory. This test is
conducted to test the visual and spatial memory of the participants of this study. This model
includes eight designs which participants has to recall. All these eight items are scored for
accuracy using a modified version of the British Ability Scales scoring procedure. Higher the test
scores in this test model are, the higher the ability, participant has to recall the designs. The test
results of such test are included as “recall of designs”. In this test, the investigator shows a
design to the participant for just 5 seconds and the participant has to replicate that design on a
17

piece of paper by using the temporary memory. This procedure is repeated for 8 times for each
item.
METHOD
Participants: For this study, a total of 136 participants were recruited from the University. Out
of these 136 participants, 60 were children and 76 were adults. All these respondents were
recruited by using opportunity sample that has helped to conducted an un biased research.
Materials: For this study, two models were used. The first model is for working memory, in
which 9 wooden blocks are used as materials. The second model is the design recall model for
which 8 different design items are used. Along with these material, miscellaneous items such
paper, pen and other items are used as well.
Design: This study has two independent variables which are “group” and “test”. The variable
group has two values which are children and adults; second variable of test also has two values
which are visual and spatial. The dependent variables are the test secured in two tests.
Procedure: All 137 participants were tested one by one for each of the test. In the first test of
corsi block task (visual test), each participant was shown nine wooden blocks each has a partial
design. All these 9 blocks together make a sequence, for every correct block placed, participant
can have 3 marks and by this there are maximum of 27 (9 * 3) marks in this test.
The second test is the design recall test (spatial) which is used to test the spatial memory of the
participants. In this test, each of the participant will be provided with 8 items one by one having
a distinctive design. Each design item will be shown for 5 seconds and then participants have to
replicate that design using the spatial for dimensional memory. Each item will have 3 marks and
by this there will be maximum 24 marks for this test.
Both the test scores will be dependent variables whereas, group of children and adults will be
independent. To test the impact of being an adult and child on test scores will be tested using
Factorial ANOVA. The software application of SPSS will be used in this process.
RESULTS
A Factorial ANOVA is conducted for the data set and the results are attached below; there were
various supporting tables of the ANOVA results but out of them only descriptive statistics and
Tests of Between-Subjects Effects table is used.
18

 The table of descriptive statistics shows that the mean scores secured by children in corsi
span test are 11.01 which are lower than the scores secured by adults which are 18.59.
 One the other hand, the marks secured by children in recall of designs test were 14.16
which were again lower than the scores of adults which were 14.39.
 By observing the mean scores in descriptive statistics, it is clear that there are differences
between the mean scores of children and adults but whether these differences are
statistically significant or not, it will be tested by table of “Tests of Between-Subjects
Effects”.
 The table of test effect shows the significance values in “Group” row. By this, it can be
seen that the p value of corsi span is .000 which is lower than .05 implying the
differences between the mean scores of corsi span of children and adults are statically
different.
 The second p value is for recall of designs and this p value is .520. As the p value > .05, it
is implied that the difference between in mean scores of children and adults in recall of
design test is not significant.
Descriptive Statistics
Group Mean Std. Deviation N
corsi span (2 correct) Children 11.0167 1.78023 60
Adult 18.5921 2.32767 76
Total 15.2500 4.31835 136
recall of designs-total raw score Children 14.1667 2.21831 60
Adult 14.3947 1.89792 76
Total 14.2941 2.04076 136
19

Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Corrected
Model
corsi span (2 correct) 1924.161a 1 1924.161 434.554 .000 .764
recall of designs-total
raw score 1.744b 1 1.744 .417 .520 .003
Intercept corsi span (2 correct) 29394.544 1 29394.544 6638.484 .000 .980
recall of designs-total
raw score 27351.744 1 27351.744 6539.146 .000 .980
Group corsi span (2 correct) 1924.161 1 1924.161 434.554 .000 .764
recall of designs-total
raw score 1.744 1 1.744 .417 .520 .003
Error corsi span (2 correct) 593.339 134 4.428
recall of designs-total
raw score 560.491 134 4.183
Total corsi span (2 correct) 34146.000 136
recall of designs-total
raw score 28350.000 136
Corrected
Total
corsi span (2 correct) 2517.500 135
recall of designs-total
raw score 562.235 135
a. R Squared = .764 (Adjusted R Squared = .763)
b. R Squared = .003 (Adjusted R Squared = -.004)
DISCUSSION
 As the mean scores of children and adults are statistically different in corsi span test, the
hypothesis 1 is accepted stating the mean scores of children’s corsi span (visual) are
different than adults.
 As the mean scores of children and adults are not statistically different in recall of design
test, the hypothesis 2 is rejected stating the mean scores of children’s recall of design
(spatial) are different than adults.
20

REFERENCES
Books and Journals
Best, J.R., 2010. Effects of physical activity on children’s executive function: Contributions of
experimental research on aerobic exercise. Developmental review. 30(4). pp.331-351.
Hafer, C.L. and Begue, L., 2005. Experimental research on just-world theory: problems,
developments, and future challenges. Psychological bulletin. 131(1). p.128.
Koole, S.L., Greenberg, J. and Pyszczynski, T., 2006. Introducing science to the psychology of
the soul: Experimental existential psychology. Current Directions in Psychological
Science. 15(5). pp.212-216.
Repovš, G. and Baddeley, A., 2006. The multi-component model of working memory:
Explorations in experimental cognitive psychology. Neuroscience. 139(1). pp.5-21.
H. Logie, R. and Pearson, D.G., 1997. The inner eye and the inner scribe of visuo-spatial
working memory: Evidence from developmental fractionation. European Journal of
cognitive psychology. 9(3). pp.241-257.
Kwon, H., Reiss, A.L. and Menon, V., 2002. Neural basis of protracted developmental changes
in visuo-spatial working memory. Proceedings of the National Academy of
Sciences. 99(20). pp.13336-13341.
Logie, R.H. and Marchetti, C., 1991. Visuo-spatial working memory: Visual, spatial or central
executive?. In Advances in psychology (Vol. 80, pp. 105-115). North-Holland.
Pickering, S.J., 2001. The development of visuo-spatial working memory. Memory. 9(4-6).
pp.423-432.
21

APPENDIX
APPENDIX-1
Full Factor ANOVA results:
Univariate Analysis of Variance
Between-Subjects Factors
Value Label N
Taskemotion 1 pleasant 30
2 unpleasant 30
Reward 1 None 20
2 Sweets 20
3 Money 20
Descriptive Statistics
Dependent Variable: time
Taskemotion reward Mean Std. Deviation N
Pleasant none 13.00 2.708 10
sweets 12.40 2.221 10
money 12.40 1.897 10
Total 12.60 2.238 30
Unpleasant none 16.90 2.514 10
sweets 15.80 1.549 10
money 12.90 1.792 10
Total 15.20 2.578 30
Total none 14.95 3.236 20
sweets 14.10 2.553 20
money 12.65 1.814 20
Total 13.90 2.729 60
22

Tests of Between-Subjects Effects
Dependent Variable: time
Source
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model 189.200a 5 37.840 8.167 .000
Intercept 11592.600 1 11592.600 2502.000 .000
Taskemotion 101.400 1 101.400 21.885 .000
Reward 54.100 2 27.050 5.838 .005
taskemotion * reward 33.700 2 16.850 3.637 .033
Error 250.200 54 4.633
Total 12032.000 60
Corrected Total 439.400 59
a. R Squared = .431 (Adjusted R Squared = .378)
Estimated Marginal Means
1. taskemotion
Dependent Variable: time
Taskemotion Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
Pleasant 12.600 .393 11.812 13.388
Unpleasant 15.200 .393 14.412 15.988
2. reward
Dependent Variable: time
reward Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
none 14.950 .481 13.985 15.915
sweets 14.100 .481 13.135 15.065
money 12.650 .481 11.685 13.615
23

3. taskemotion * reward
Dependent Variable: time
Taskemotion reward Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
Pleasant none 13.000 .681 11.635 14.365
sweets 12.400 .681 11.035 13.765
money 12.400 .681 11.035 13.765
Unpleasant none 16.900 .681 15.535 18.265
sweets 15.800 .681 14.435 17.165
money 12.900 .681 11.535 14.265
Post Hoc Tests
reward
Multiple Comparisons
Dependent Variable: time
Tukey HSD
(I) reward (J) reward
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
none sweets .85 .681 .430 -.79 2.49
money 2.30* .681 .004 .66 3.94
sweets none -.85 .681 .430 -2.49 .79
money 1.45 .681 .093 -.19 3.09
money none -2.30* .681 .004 -3.94 -.66
sweets -1.45 .681 .093 -3.09 .19
Based on observed means.
The error term is Mean Square(Error) = 4.633.
*. The mean difference is significant at the .05 level.
24

Homogeneous Subsets
time
Tukey HSDa,b
reward N
Subset
1 2
money 20 12.65
sweets 20 14.10 14.10
none 20 14.95
Sig. .093 .430
Means for groups in homogeneous subsets are
displayed.
Based on observed means.
The error term is Mean Square(Error) = 4.633.
a. Uses Harmonic Mean Sample Size = 20.000.
b. Alpha = .05.
25

APPENDIX-2
Full results of MANOVA test:
Between-Subjects Factors
N
Time 1 21
2 21
3 21
4 21
Multivariate Testsa
Effect Value F Hypothesis df Error df Sig.
Intercept Pillai's Trace .929 515.659b 2.000 79.000 .000
Wilks' Lambda .071 515.659b 2.000 79.000 .000
Hotelling's Trace 13.055 515.659b 2.000 79.000 .000
Roy's Largest Root 13.055 515.659b 2.000 79.000 .000
Time Pillai's Trace .428 7.265 6.000 160.000 .000
Wilks' Lambda .577 8.343b 6.000 158.000 .000
Hotelling's Trace .725 9.431 6.000 156.000 .000
Roy's Largest Root .714 19.028c 3.000 80.000 .000
a. Design: Intercept + Time
b. Exact statistic
c. The statistic is an upper bound on F that yields a lower bound on the significance level.
26

Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of
Squares df Mean Square F Sig.
Corrected Model Weight 37.845a 3 12.615 9.247 .000
Preocc 16.131b 3 5.377 14.956 .000
Intercept Weight 390.012 1 390.012 285.873 .000
Preocc 348.107 1 348.107 968.245 .000
Time Weight 37.845 3 12.615 9.247 .000
Preocc 16.131 3 5.377 14.956 .000
Error Weight 109.143 80 1.364
Preocc 28.762 80 .360
Total Weight 537.000 84
Preocc 393.000 84
Corrected Total Weight 146.988 83
Preocc 44.893 83
a. R Squared = .257 (Adjusted R Squared = .230)
b. R Squared = .359 (Adjusted R Squared = .335)
Multiple Comparisons
Tukey HSD
Dependent Variable (I) Time (J) Time
Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Weight 1 2 -1.33* .360 .002 -2.28 -.39
3 -1.29* .360 .003 -2.23 -.34
4 -1.81* .360 .000 -2.76 -.86
2 1 1.33* .360 .002 .39 2.28
3 .05 .360 .999 -.90 .99
4 -.48 .360 .552 -1.42 .47
3 1 1.29* .360 .003 .34 2.23
2 -.05 .360 .999 -.99 .90
4 -.52 .360 .470 -1.47 .42
4 1 1.81* .360 .000 .86 2.76
2 .48 .360 .552 -.47 1.42
3 .52 .360 .470 -.42 1.47
Preocc 1 2 -.90* .185 .000 -1.39 -.42
27

3 -1.00* .185 .000 -1.49 -.51
4 -1.10* .185 .000 -1.58 -.61
2 1 .90* .185 .000 .42 1.39
3 -.10 .185 .955 -.58 .39
4 -.19 .185 .733 -.68 .30
3 1 1.00* .185 .000 .51 1.49
2 .10 .185 .955 -.39 .58
4 -.10 .185 .955 -.58 .39
4 1 1.10* .185 .000 .61 1.58
2 .19 .185 .733 -.30 .68
3 .10 .185 .955 -.39 .58
Based on observed means.
The error term is Mean Square(Error) = .360.
*. The mean difference is significant at the .05 level.
Weight
Tukey HSDa,b
Time N
Subset
1 2
1 21 1.05
3 21 2.33
2 21 2.38
4 21 2.86
Sig. 1.000 .470
Means for groups in homogeneous subsets are
displayed.
Based on observed means.
The error term is Mean Square(Error) = 1.364.
a. Uses Harmonic Mean Sample Size = 21.000.
b. Alpha = .05.
Preocc
Tukey HSDa,b
Time N Subset
28

1 2
1 21 1.29
2 21 2.19
3 21 2.29
4 21 2.38
Sig. 1.000 .733
Means for groups in homogeneous subsets are
displayed.
Based on observed means.
The error term is Mean Square(Error) = .360.
a. Uses Harmonic Mean Sample Size = 21.000.
b. Alpha = .05.
APPENDIX-3
Between-Subjects Factors
Value Label N
Group 1.00 Children 60
2.00 Adult 76
Descriptive Statistics
Group Mean Std. Deviation N
corsi span (2 correct) Children 11.0167 1.78023 60
Adult 18.5921 2.32767 76
Total 15.2500 4.31835 136
recall of designs-total raw score Children 14.1667 2.21831 60
Adult 14.3947 1.89792 76
Total 14.2941 2.04076 136
Box's Test of Equality of
Covariance Matricesa
29

Box's M 8.694
F 2.850
df1 3
df2 3893090.195
Sig. .036
Tests the null hypothesis that
the observed covariance
matrices of the dependent
variables are equal across
groups.
a. Design: Intercept + Group
Multivariate Testsa
Effect Value F Hypothesis df Error df Sig. Partial Eta Squared
Intercept Pillai's Trace .990 6394.716b 2.000 133.000 .000 .990
Wilks' Lambda .010 6394.716b 2.000 133.000 .000 .990
Hotelling's Trace 96.161 6394.716b 2.000 133.000 .000 .990
Roy's Largest Root 96.161 6394.716b 2.000 133.000 .000 .990
Group Pillai's Trace .764 215.670b 2.000 133.000 .000 .764
Wilks' Lambda .236 215.670b 2.000 133.000 .000 .764
Hotelling's Trace 3.243 215.670b 2.000 133.000 .000 .764
Roy's Largest Root 3.243 215.670b 2.000 133.000 .000 .764
a. Design: Intercept + Group
b. Exact statistic
Levene's Test of Equality of Error Variancesa
F df1 df2 Sig.
corsi span (2 correct) 2.728 1 134 .101
recall of designs-total raw score .922 1 134 .339
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
30

Source Dependent Variable
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Corrected
Model
corsi span (2 correct) 1924.161a 1 1924.161 434.554 .000 .764
recall of designs-total
raw score 1.744b 1 1.744 .417 .520 .003
Intercept corsi span (2 correct) 29394.544 1 29394.544 6638.484 .000 .980
recall of designs-total
raw score 27351.744 1 27351.744 6539.146 .000 .980
Group corsi span (2 correct) 1924.161 1 1924.161 434.554 .000 .764
recall of designs-total
raw score 1.744 1 1.744 .417 .520 .003
Error corsi span (2 correct) 593.339 134 4.428
recall of designs-total
raw score 560.491 134 4.183
Total corsi span (2 correct) 34146.000 136
recall of designs-total
raw score 28350.000 136
Corrected
Total
corsi span (2 correct) 2517.500 135
recall of designs-total
raw score 562.235 135
a. R Squared = .764 (Adjusted R Squared = .763)
b. R Squared = .003 (Adjusted R Squared = -.004)
Estimates
Dependent Variable Group Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
corsi span (2 correct) Children 11.017 .272 10.479 11.554
Adult 18.592 .241 18.115 19.070
recall of designs-total raw score Children 14.167 .264 13.644 14.689
Adult 14.395 .235 13.931 14.859
Pairwise Comparisons
Dependent Variable (I) Group (J) Group
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
corsi span (2 correct) Children Adult -7.575* .363 .000 -8.294 -6.857
31

Adult Children 7.575* .363 .000 6.857 8.294
recall of designs-total
raw score
Children Adult -.228 .353 .520 -.927 .470
Adult Children .228 .353 .520 -.470 .927
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.
Multivariate Tests
Value F Hypothesis df Error df Sig.
Partial Eta
Squared
Pillai's trace .764 215.670a 2.000 133.000 .000 .764
Wilks' lambda .236 215.670a 2.000 133.000 .000 .764
Hotelling's trace 3.243 215.670a 2.000 133.000 .000 .764
Roy's largest root 3.243 215.670a 2.000 133.000 .000 .764
Each F tests the multivariate effect of Group. These tests are based on the linearly independent pairwise comparisons
among the estimated marginal means.
a. Exact statistic
Univariate Tests
Dependent Variable
Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
corsi span (2 correct) Contrast 1924.161 1 1924.161 434.554 .000 .764
Error 593.339 134 4.428
recall of designs-total
raw score
Contrast 1.744 1 1.744 .417 .520 .003
Error 560.491 134 4.183
The F tests the effect of Group. This test is based on the linearly independent pairwise comparisons among the
estimated marginal means.
32