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RMC811S-RESEARCH METHODS FOR NATURAL SCIENCES-1ST OPP-JUNE 2025 |
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nAmlBIA unlVERSITY
OF SCIEnCE Ano TECHnOLOGY
FACULTY OF HEALTH, NATURAL RESOURCES AND APPLIED SCIENCES
DEPARTMENT OF NATURAL RESOURCES SCIENCES
QUALIFICATION: BACHELOR OF NATURAL RESOURCES MANAGEMENT HONOURS
QUALIFICATION CODE: 08BNRH
COURSE CODE: RMC811S
LEVEL: 8
COURSE NAME: RESEARCHMETHODS FOR NATURAL
SCIENCES
DATE: JUNE 2025
DURATION: 3 HOURS
MARKS: 150
FIRST OPPORTUNITY EXAMINATION QUESTION PAPER
EXAMINER(S) Dr Tendai Nzuma (Section A: Scientific Writing)
Dr Meed Mbidzo (Section B: Statistics)
MODERATOR: Prof M. Mwale
INSTRUCTIONS
1. Answer ALL the questions.
2. Write clearly and neatly.
3. Number the answers clearly.
PERMISSIBLE MATERIALS
1. Examination question paper
2. Answering book
3. Calculator
THIS QUESTION PAPER CONSISTS OF 9 PAGES (Excluding this front page)
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SECTION A: SCIENTIFIC WRITING
[SO]
QUESTION 1
a) Describe the structure and content of a scientific report. Explain the role of each major section.
(10)
b) List and explain any five ethical considerations in scientific writing and reporting.
(10)
QUESTION 2
a) Using the information below, write an appropriate Results section (including at least one sentence (10)
summary and a table): "A field experiment compared plant height in three maize varieties (Variety A,
B, and C) treated with the same compost. After 12 weeks, the average plant heights were: A= 142.3
cm, B = 135.7 cm, and C = 147.9 cm. An ANOVA test revealed significant differences (p < 0.05)."
b) Draft a brief Methods section suitable for the study in (a), making appropriate assumptions.
(10)
c) Identify four common writing problems in undergraduate scientific reports and propose practical (10)
strategies to overcome them.
SECTION B: STATISTICS
QUESTION 3
What statistical procedure would you use for the following research questions and/or scenarios?
[100)
[10]
a) A researcher is studying the effect of light exposure and altitude on plant chlorophyll content. (2)
They have three light exposure levels: full sun, partial shade and full shade, while altitude is
categorised as low and high. What statistical test should be used to determine if light exposure
and altitude impact chlorophyll content in leaves?
b) A researcher determined the presence of a specific intestinal parasite in each animal from a
(2)
random selection of mice of each of two species. You want to determine if there is a
relationship between mice species and occurrence of the parasite.
c) You take a sample of the weights of 20 male elephant tusks from Etosha National Park (ENP)
(2)
and a sample of 18 male elephant tusks from the Bwabwata National Park (BNP). You want to
test if there is a difference in tusk weights between elephants from ENPand BNP. Note: You
find that the tusk weights for BNP were not normally distributed and that there were significant
outliers in the data.
d) Concentrations of nitrogen oxides was determined in two urban suburbs. You want to test the (2)
hypothesis that the air pollutant was present in the same concentrations in the two suburbs.
e) Based on an anxiety score, students are divided into three groups: "low-stressed students",
(2)
"moderately-stressed students" and "highly-stressed student. Exam performance is measured
from 1 to 100. You want to test the hypothesis that exam performance differs based on exam
anxiety levels amongst students? The data are non-normally distributed because of high
variability among samples. What test should be used to compare the exam performance of
students across the three anxiety levels, and why is this test appropriate?
2
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QUESTION 4
A Marine biologist measures the abundance of fish species (number of fish per square meter) in [16]
protected vs. unprotected marine areas. Use the SPSSoutputs provided to answer questions that
follow, and provide evidence for your answers.
a) What statistical test would you use to determine whether the abundance of fish species
(2)
differed between the two marine areas?
b) State the null and alternative hypotheses.
(2)
c) State whether the assumption of significant outliers in the test mentioned in (a) is met or not. (3)
d) State whether the assumption of normality in the test mentioned in (a) is met or not.
(3)
e) State whether the assumption of homogeneity of variances is met or not.
(3)
f) Does fish abundance differ significantly between protected and unprotected marine areas?
(3)
rrests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Type of area Statistic df Sig. Statistic df Sig.
fish abundance protected
.126
8 .200· .969 8 0.889
unprotected .170
8 .200· .952 8 0.736
sof--------------------------
40>-------
.,
..u
.C.,
...5n ~f--------------------------
.-r.;=,
,of--------------------------
protected
Type of area
unprotected
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Independent Samples Test
Leven e's
[Test for
Equality of
Variances t-test for Equality of Means
Significance
95% Confidence
Interval of the
Difference
F Sig. t
One- Two- Mean
Std. Error
df Sided p Sided p Difference Difference Lower
Upper
fish
Equal 1.836 .197 16.189 14
abundance variances
assumed
<.001
<.001 23.250
1.436
20.170
26.330
Equal
variances
not
assumed
16.189 11.603 <.001 <.001 23.250
1.436
20.109
26.391
Group Statistics
fish abundance
[Type of area
N
protected
8
unprotected
8
Mean Std. Deviation
40.00 3.464
16.75 2.121
Std. Error Mean
1.225
.750
QUESTION 5
Environmental scientists measure air pollution levels (in µg/m 3 ) in residential, commercial, and
[30]
industrial zones of a city. Answer the questions that follow using the SPSSoutputs provided below.
a) What test would be appropriate to test the hypothesis that air pollution levels in all the three (2)
city zones are the same?
b) State the null and alternative hypotheses.
(2)
c) Name three assumptions related to how your data fits the test mentioned in (a)
(6)
d) State whether the three assumptions mentioned in (c) are met or not (provide evidence for
(9)
your answers).
e) Report on the descriptive statistics of the air pollution levels for the different city zones.
(4)
f) Determine whether the air pollution levels are the same in the different city zones.
(4)
g) If air pollution levels differ significantly among different city zones, explain where the
(3)
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difference lies and provide evidence.
O,J)O
7000
60.00
''""
40.00
>JOO
,ODO
residential
•
•
commercial
City zone
industrial
rTestsof Homogeneity of Variances
Levene Statistic
df1
Pollution levels Based on Mean
.096
2
Based on Median
.051
2
Based on Median and with adjusted df .051
2
Based on trimmed mean
.098
2
df2
Sig.
12
.909
12
.950
12.000 .950
12
.907
Descriptives
Pollution levels
95% Confidence Interval for Mean
N Mean
Std. Deviation Std. Error Lower Bound Upper Bound
Minimum Maximum
residential 5 27.2000 1.92354
.86023
24.8116
29.5884
25.00
30.00
commercial 5 47.6000 2.07364
.92736
45.0252
50.1748
45.00
50.00
industrial
5 72.8000 1.92354
.86023
70.4116
75.1884
70.00
75.00
Total
15 49.2000 19.39146
5.00685
38.4614
59.9386
25.00
75.00
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Tests of Normality
Kolmogorov-Smirnov• Shapiro-Wilk
City zone
Statistic df Sig.
Statistic
df Sig.
Pollution levels residential .141
5
.200·
.979
5
.928
commercial .180
5
.200·
.952
5
.754
industrial
.141
5
.200· .979
5
.928
ANOVA
Pollution levels
Between Groups
Within Groups
Total
Sum of Squares df
5217.600
2
46.800
12
5264.400
14
Mean Square
2608.800
3.900
F
668.923
Sig.
<.001
Multiple Comparisons
Dependent Variable: Pollution levels
Mean
(I) City zone (J) City zone Difference (1-J) Std. Error Sig.
Tukey HSD residential commercial -20.40000*
1.24900 <.001
industrial -45.60000*
1.24900 <.001
commercial residential 20.40000*
1.24900 <.001
industrial -25.20000*
1.24900 <.001
industrial residential 45.60000*
1.24900 <.001
commercial 25.20000·
1.24900 <.001
*. The mean difference is significant at the 0.05 level.
95% Confidence Interval
Lower Bound Upper Bound
-23.7322
-17.0678
-48.9322
-42.2678
17.0678
23.7322
-28.5322
-21.8678
42.2678
48.9322
21.8678
28.5322
QUESTION 6
A researcher is interested in determining whether the effects of deforestation on soil erosion rates
[22]
were different based on soil type. Deforestation is categorized into three levels (none, partial and
complete) and there are two types of soil (sand and clay). Use the SPSSoutputs provided to answer
the questions that follow.
(a) What statistical test would you use to determine whether the effect of deforestation level on
(2)
soil erosion rates is different for sandy and clay soils?
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(b) State whether the assumption of normality in the test mentioned in (a) is met or not.
(4)
(c) Discuss how you would deal with outliers resulting from data entry errors.
(2)
(d) State whether the assumption of homogeneity of variances is met or not.
(4)
(e) Explain how profile plots can be used to determine whether an interaction exists between two
(5)
independent variables.
(f) Determine whether deforestation level, soil type or their interaction significantly influence soil (5)
erosion rates.
Deforestation
Level
Soil Type
Kolmogorov-Smirnova
Statistic df Sig.
none
sand Residual for Erosionrate .136
5
.200·
clay Residual for Erosionrate .136
5
.200·
partial
sand Residual for Erosionrate .179
5
.200·
clay Residual for Erosionrate .197
5
.200·
complete
sand Residual for Erosionrate .224
5
.200·
clay Residual for Erosionrate .224
5
.200·
Shapiro-Wilk
Statistic df Sig.
.987
5
.967
.987
5
.967
.984
5
.955
.943
5
.685
.914
5
.493
.912
5
.482
Levene Statistic dfl df2
Sig.
Erosion rate Based on Mean
1.905
5 24
.131
Based on Median
.941
5 24
.472
Based on Median and with .941
adjusted df
5 9.409
.497
Based on trimmed mean 1.795
5 24
.152
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Tests of Between-Subjects Effects
Dependent Variable: Erosion rate
Source
[Type Ill Sum of
Squares
df
Mean Square F
Sig.
Corrected Model
2515.77P
5
503.154
5056.825 <.001
Intercept
5169.281
1
5169.281
51952.576 <.001
Deforestation Level 2028.329
2
1014.164
10192.606 <.001
SoilType
361.921
Deforestationlevel * 125.521
SoilType
1
361.921
2
62.760
3637.400
630.757
<.001
<.001
Error
2.388
24
.100
Total
7687.440
30
Corrected Total
2518.159
29
a. R Squared= .999 (Adjusted R Squared= .999)
Partial Eta
Squared
.999
1.000
.999
.993
.981
QUESTION 7
Suppose we want to investigate the relationship between site temperature (in °C) and plant growth [24]
(in cm). A sample of 30 sites with varying temperatures were selected and plant heights were
recorded. Use the SPSSoutputs provided to answer the questions that follow.
a) Describe the general relationship that exists between site temperature and plant heights.
(2)
Explain your answer fully.
b) Did the data meet the assumption of homoscedasticity? Explain your answer.
(4)
c) Did the data meet the assumption of normality? Explain your answer.
(3)
d) Did the data meet the assumption of no significant outliers? Explain your answer.
(2)
e) What proportion of the variance in plant height is explained by the site temperature? Explain (4)
fully.
f) Determine whether the regression model results are a statistically significantly better
(4)
prediction of plant height than if we just used the mean of the dependent variable. Provide
evidence for your explanation.
g) Compute a regression equation using the SPSSoutput provided to predict the plant height for (5)
the following site temperatures: 25°C, 15°C and 35°C.
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Scatter Plot of Plant Growth (cm) by Temperature fC)
50.00
40.00 f----------------------•~·~·-·----------
0 .......
e
..!!.
•
••0
.s:
•0
j JO.DO
0
Gc..
ii: 20.00
• •• • ••• 0 0
•••
0•
10.00
~o
10.00
20.00
30.00
40.00
50.00
TemperaturefC)
Model Summaryb
Model R
R Square
Adjusted R Square
1
.996"
.993
.993
a. Predictors: (Constant), Temperature (°C}
b. Dependent Variable: Plant Growth (cm)
Std. Error of the
Estimate
.61913
Durbin-Watson
.206
Scatterplot
DependentVariable: Plant Growth(cm)
iii
:::,
., 1>------------------.---~-.-.------------
0
0
0
".0,
0
•
0
0
0
.."0
C
e.n. . -1 .-----------------------------------
O
O
OO
•
CII 9_J~----------=.--------
c
·0.;;,;
0
.. .2f------------------------------~·-----
0•
0
•
0
-3.-----------------------------------
•
-2
_,
Regression Standardized Predicted Value
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Normal P-P Plot of Regression Standardized Residual
Dependent Variable: Plant Growth (cm)
0.8
..c
f
D..
uE:::, 0.6
"C
.,u 0.4
C.
)(
w
0.2
0.4
0.6
0.8
1.0
Observed Cum Prob
ANOVN
Model
Sum of Squares df
1
Regression 1494.081
1
Residual
10.733
28
!Total
1504.814
29
a. Dependent Variable: Plant Growth (cm)
b. Predictors: (Constant), Temperature (0 C)
Mean Square
1494.081
.383
F
3897.752
Sig.
<.0Olb
Coefficientsa
Unstandardized
Coefficients
Model
B
Std. Error
1
[(Constant)
10.640
.394
[Temperature (0 C).801
.013
a. Dependent Variable: Plant Growth (cm)
Standardized
Coefficients
Beta
t
27.034
.996
62.432
Sig.
<.001
<.001
95.0% Confidence Interval
for B
Lower Bound Upper Bound
9.834
11.446
.774
.827
10