Vol 8 No 3 - June 2016

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International
Journal of
Humanities &
Social Sciences
Vol. 8, No. 3
IJHSS.NET
e-ISSN: 1694-2639
p-ISSN: 1694-2620
June 2016

Vol 8, No 3 – June 2016
Table of Contents
Assessing the relationship between climate and patterns of wildfires in
Ghana
1
Daniel L. Kpienbaareh
Influence of students’ self perception on biology achievement among
secondary school students in Nakuru county, Kenya
21
Nyambura Rose
The influence of clothing in the negotiation of identities. A study between
students and lecturers.
31
Simon Ntumi and Esther Quarcoo
Some unobtrusive indicators of psychology’s shift from the humanities
and social sciences to the natural sciences
44
Dr Günter Krampen and Lisa I. Trierweiler
Challenges of Bible/Liturgical Translations in the Efik Language Group 67
Christopher Naseri (Ph.D)
AAJHSS.ORG

1 http://aajhss.org/index.php/ijhss
International Journal of Humanities and Social Sciences
p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 3, pp. 1-20, ©IJHSS
Assessing the relationship between climate and patterns of
wildfires in Ghana
Daniel L. Kpienbaareh
Department of Geosciences, University of Akron, OH
Abstract
Wildfires are a common occurrence in many areas with a distinct dry season. The objective of
this study is to investigate the relationship between wildfires (bushfires) and the climate in
Ghana. I establish the correlation between fire data, mean monthly temperatures and average
monthly precipitation. I also assess the pattern of wildfire occurrence in Ghana with respect to
the pattern of movement of the Intertropical Convergence Zone (ITCZ). Using climate data for
period November 2000 to March 2010 at a 0.5o
by 0.5o
resolution, from the University of East
Anglia‟s Climate Research Unit (UEA CRU TS3.23), and MODIS Climate Modelling Grid
(MOD14CMH) Active Fire Products at a 0.25o
by 0.25o
resolution, obtained from the Active
Fire Products data maintained by the University of Maryland, also from November 2000 to
March 2010, it was found there is no meaningful correlation between the fire data and individual
mean monthly temperatures and average monthly precipitation. However, there is a strong
relationship between the pattern of fire occurrence and the pattern of movement of the ITCZ in
Ghana. I conclude that there is a strong relationship between wildfire occurrence and climate in
Ghana based on the closeness of the relationship between the movement of the ITCZ and the
pattern of wildfire occurrence.
Key words: ITCZ, harmattan, bushfires/wildfires
Introduction
Wildfires are a common occurrence in areas with a high amount of vegetation and a period of
dryness in the course of the year (Balling, Meyer & Wells 1992). The more foliage there is in an
area, the more fire there is likely to be, all other things being equal (Agee 1998, cited in
McKenzie et al. 2004). The vegetation amount and type, and the weather conditions which
creates a „fire weather‟ is determined by the type of climate (Heyerdahl, Brubaker & Agee 2002).
This implies that the more moisture there is in the atmosphere, the less risky there is for a
possible ignition and vice versa.
Ghana lies within the tropical zone and hence has high temperatures for most of the
year, with distinct periods of dry season and wet/rainy seasons in the year, which vary from the
north to the south, in line with the variations in the climate (McSweeney et al. 2010). The
northern part of the country has a guinea savanna type of vegetation where there are high
temperatures all year round and a long dry season, and the southern part has a short dry season

with the rainy season divided into a major and a minor rainy season (McSweeney et al. 2010).
The climate of an area is fairly fixed, and so the risk of an area getting burnt depends on the
weather. In addition to the weather, the risk of ignition depends on the fuel load (amount of dry
vegetation) (Bowman et al. 2009). Therefore, climate and weather determine the trends of
wildfires in an area, and so any changes in the climate will affect the pattern of wildfires.
Some studies have linked wildfires with vegetation amount in the savannah climate zones
in West Africa. For instance, Devineau, Fournier & Nignan (2010) studied the relationship
between wildfires, land cover and plant species in Burkina Faso and concluded that areas that
have high amount of foliage are more susceptible to fire outbreaks. However, areas with land use
such as residential and commercial are less likely to be burn, because they are protected to
prevent damage properties, highlighting human influences on wildfire occurrence or non-
occurrence. Kugbe et al. (2012) studied the annual seasonal burnt area in the savannah region of
Ghana and realized there was a similar, distinct inter-annual burnt area which coincides with the
dry seasons in the northern region of Ghana.
Studying the relationship between wildfire and climate in Ghana is challenging. There is
an insufficient amount of high resolution data for both climate variables and fire data. Studies
that have been done use data that are relatively coarse so the relationship between the two is
often unclear.
Some studies have linked wildfires to climate and meteorological (weather) variables in
current climatic conditions. For instance, in a study of the state of severe temperatures and
wildland fire in Spain, Cardil, Eastaugh & Molina (2015) discovered that high temperatures
played a significant role in number of fires in Spain. This was the case in areas which were high
in the amount of winds. Winds serve as catalysts which can increase the extent that fire will burn
and the direction in which it burns. By implication therefore, even in lower temperatures with
dry fuel load, fire ignition can still be possible even though the speed of burning may be slower.
Other studies concur with this assertion. Flannigan & Wotton (2001) concluded in their study
weather and climate are important determinants of wildfires. The climate determines the extent
of foliage in an area and the weather determines whether temperatures are high or if it is windy
etc. Consequently, an interaction between these two – climate and weather - strongly influence
the risk of fire outbreaks and the extent the fire burns. Severe temperatures also result in
heatwaves which have the potential of triggering large scale wildfires (Trigo et al. 2006).
On days when temperatures are high, there is low moisture content in foliage - fuel for
wildfires - (Westerling et al 2006) implying on such days the likelihood of fire ignition is more
imminent and fire response could be severe and unpredictable. Consequently, wildfires can
spread faster and may be difficult to put off (Molina et al. 2010). Wildfires tend to be
concentrated in the dry season in areas with mainly two seasons (in the tropical areas).
There is also a relationship between wildfire risks and the amount of rainfall in an area.
For fires to occur, there should be sufficient fuel for the fire to consume (Hargrove et al. 2000
cited in Fannigan et al. 2009). This means there has to be sufficient amount of rainfall during the
rainy season to allow vegetation to grow in abundance (Meyn et al. 2007). Rainfall also
determines the extent to which fire can spread in an area. Wet fuel loads do not spread too
quickly as compared to drier fuel loads. The dryness depends on whether there was some
precipitation just before or during an ignition (Flannigan et al. 2005). In savannah regions of
West Africa where there is a long dry season and high temperatures, spread of wildfires will be
relatively faster than areas with moister fuel loads because the foliage does not completely dry
out, especially in areas with tropical rainforests and deciduous forests.
Despite most models assuming close relationships between fire and climate, Archibald et
al. (2010) present a contrasting view point. They contend that the assumptions supporting these

models must be re-examined in areas such as the African savannah, where the “human impact
on fire regimes is substantial, and acts to limit the responsiveness of fires to climatic events”.
Therefore, even though wildfires are determined by climate and weather variables, there
are non-climate influences to the ignition and spread of fire such as human interaction with the
environment (Bleken, Mysterud and Mysterud 1997). Humans use fires for various economic
activities, a basis for the conclusion by Pyne et al. (1996) that “fire problems are socially
constructed problems” (cited in Westerling et al. 2006). Fire is commonly used for agricultural
purposes, especially in the tropical areas. There is always a high potential that the fire may stray
into the wild and destroy larger areas. Most wildfires are intentional, but due to poor control,
they spread to areas that were not intended for burning. Wildfires are usually set for social and
economic reasons, including forest management, animal grazing and crop cultivation and
hunting among other (Bowman et al. 2011), especially in the sub-Saharan Africa.
Even though human activities can cause ignitions, they are also capable of reducing the
amount of wildfires occurring in an area. Fire suppression policies and firefighting can reduce
the amount and spread of wildfires. In Burkina Faso for instance, strict laws and regulations have
been put in place in some rural areas to guard against cutting of trees and wildfires (Kugbe et al.
2012). It remains a challenge though for the burning to be completely eliminated.
The objective of this study is to assess the relationship between wildfires in Ghana and
climate variables. Specifically, I will correlate average monthly precipitation and mean monthly
temperature values for the driest months in the country, (November 2000 to March 2010), with
MODIS Climate Modelling Grid (CMG) Active Fire Products. The study will also investigate the
pattern of fire occurrence in terms of the north-south direction and its relationship with the
seasonal movement of the ITCZ in the country. The ITCZ is the major natural determinant of
climate and weather in Ghana. The hypothesis of this study is that there is no relationship
between mean monthly temperature, average monthly precipitation and wildfires in Ghana.
Methods
Study area
The study covered the entire Ghana. Ghana is located on the geographical coordinates 8o
N and
2o
W, covering a total area of 239,460 km2
(CIA World Factbook). The northern part of the
country is mostly hot and dry for most parts of the year and the vegetation in the area is mostly
savannah. The vegetation is influenced by precipitation/rainfall, lithology and the human
activities (Lane 1962). The climate in the area gives it two distinct seasons: rainy season and dry
season (harmattan).
The dry season lasts for five to six months (usually November to April), and the rainy
season lasts for six to seven (May to October), with the severity of the harmattan increasing from
north to south, in line with the movement of the Intertropical Convergence Zone (ITCZ) (figure
1a), which influences the pattern of rainfall in the country. Rainfall reliability is low and large
digressions from monthly and annual averages are common (Owusu & Waylen 2009).
The southern part of Ghana (the deciduous, moist evergreen and wet evergreen forests)
(figure 1b) experience two rainy seasons which match the northern and southern movements of
the ITCZ across the region. The major rainy season occurs from March to July (with a peak in
May- June), and a minor rainy season occurs in September to November, interspersed by a
relatively short dry season in August and September, but rainfall occurs all year round
(McSweeney et al. 2010). The southwest part of Ghana (wet evergreen,) is especially hot and
moist but the southeast (coastal savannah zone) is relatively drier (Owusu & Waylen 2009).

Figure 1a: North – south movement of the ITCZ in SSA, including Ghana. This results in dry
and wet seasons in Ghana. (Source: Encyclopaedia Britannica Online)
Figure 1b is the vegetation map of Ghana. The type of vegetation is determined by the
climate which is influenced by the movement of the ITCZ. More than half of the country
consists mainly of savannah grasslands and forest transitions, the type of vegetation which are
very prone to wildfires (Devineau, Fournier & Nignan 2010).

Figure 1b: The type of vegetation in Ghana. The pattern is influenced by the North – South
movement of the ITCZ. (Source: http://exploringafrica.matrix.msu.edu/curriculum/unit-
five/module-twenty-four/module-twenty-four-activity-one/).
Study design
The study was designed to examine how two climate variables – mean monthly temperature and
average monthly precipitation/rainfall – influence wildfire patterns in Ghana. A correlation
analysis and the maps of mean monthly temperatures and average monthly rainfall were used to
measure the relationships and how they climate variables influence the patterns of wildfires. The
dependent variable is the mean monthly fire for the study period and the independent variables
are the mean monthly temperatures and the average monthly precipitation/rainfall.
Materials

MODIS Climate Modelling Grid (CMG) (MOD14CMH) Active Fire Products data were
downloaded at a 0.25o
by 0.025o
resolution from an ftp server maintained by the University of
Maryland, hosting the CMG and MCD14ML products (ftp://fuoco.geog.umd.edu). The data
acquired was for six months of the harmattan season, starting from November to March for the
period 2000 to 2010. These months are roughly the months when the harmattan season is across
the entire country. The fire data covered the entire world.
To extract fire pixels which fall within the confines of Ghana, the raster map of each
monthly data was opened in ArcMap (v10.3.1) and exported to .tif format using the „Export
Raster‟ tool. The contents of the world files in the .tif files were replaced with a new coordinate
system. The new file were then re-opened with a new blank ArcMap document. A shapefile
containing world map of countries was added to the new raster layer and the exact location of
Ghana was identified. A „selection by attribute‟ was done to select the boundaries of Ghana and
mark out the pixels containing fire data from it.
The clip tool was used to extract the map of the country together with the fire pixels.
The symbology of the pixels were changed in a manner that will indicate low – high number of
fires in a color ramp. These maps will be used for comparing with the climate variables to
investigate the pattern of wildfires in the country.
The climate data (namely mean monthly temperature and average monthly precipitation)
for the study were obtained from the Climate Research Unit (CRU) of the University of East
Anglia, United Kingdom, (1901-2014: CRU TS3.23 (land) 0.5°) (UEA CRU Jones and Harris
2008), downloaded from the KNMI Climate Explorer (http://climexp.knmi.nl). The November,
2000 to March 2010 data for both temperature and precipitation were extracted from this. TS
(time-series) datasets are month-by-month variation in climate over the last 100 years, produced
by the CRU. These are calculated on high-resolution (0.5o
x 0.5o
) grids, which are based on a
database of mean monthly temperatures provided by more than 4,000 weather stations spread
across the world (UEA CRU Jones & Harris 2008). They allow variability in climate to be
observed, and include variables such as cloud cover, daily minimum and maximum temperature
ranges, frost day frequency, precipitation, daily mean temperature, monthly average daily
maximum temperature, potential evapo-transpiration and number of wet days. They are thus
useful for studies such as this.
Procedure
To establish the relationship between MODIS fire data and climate, a correlation analyses be
conducted between the mean monthly temperature of the country between November 2000 and
March 2010 and the average monthly fire occurrence, and correlation between the average
monthly precipitation and the average monthly fire occurrence calculated from the fire pixels for
the same time period.
To investigate the pattern between the average monthly precipitation, mean monthly
temperature and fire occurrence, maps will be plotted (using the November 2000 to March 2010
data) of the average monthly temperature and the mean monthly precipitation using the Grid
Analysis and Display System (GrADS v2.1.a3, which is used online with the KNMI Climate
Explorer), and the fire pixels for the same period as the climate variables, clipped out of the
global fire data using the Clip raster tool in ArcMap and the same color ramp (indicating low -
high) is applied to make them uniform.
Results
Figure 2a and figure 2b show observed mean monthly temperatures and average monthly
precipitation respectively, between November 2000 and March 2010. Note that the month with

the highest average monthly rainfall of the five months is November but the month with the
highest mean monthly temperature during the period is March. Both graphs trend with the
passage of the ITCZ (southwest monsoon winds) and the northeast trade winds which result in
the wet and dry seasons respectively. The dry season starts in November when the ITCZ begins
it southwards retreat and is replaced by the northeast trade winds (harmattan). Also, in the five
months period, November has the highest amount of rainfall for the time period under study
whereas the month with the highest mean temperature varies between the months of February
and March.
Figure 2. (a) Indicates the mean monthly temperature. (b) Shows the average monthly
precipitation for the period November 2000 to March 2010 in Ghana (UEA CRU Jones and
Harris 2008).
Figure 2c shows the mean number of fire occurring in each of the months for the period
November 2000 to March 2010. There is a slow start to the mean number of fires in November,
a peak in December and steady decline to very limited number of fires in March. This is closely
related to figures 2a and 2b because the number of fires coincide with the start of the dry season
and increases as the rainfall amount diminishes and the temperatures begin to rise. On average,
the month of November has more rainfall, indicating more moist grasses and foliage, which
means lesser probability of ignition, hence the relatively lower number of wildfires for that
month. December has more fires because the ITCZ would have retreated further south, resulting
in more dryness.
24
25
26
27
28
29
30
31
2000 2005 2010
Temperature(oC)
Year
Nov Dec Jan Feb Mar
10.0
30.0
50.0
70.0
90.0
110.0
130.0
150.0
2000 2005 2010
Precipitation(cm)
Year
Nov Dec Jan Feb Mar
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
MeanNo.offires
Year
Nov Dec Jan Feb Mar

Figure 2c. Mean fire occurrence. There is a pattern for almost the years (except 2008 and 2010)
in which the number of fires start low in November rise in December and decrease again
afterwards. (Source: MODIS Active Fire Products).
Table 1a and b indicate the mean number of fires and the total number of fires in each
pixel for each month in the study period. Both tables indicate the pattern of rising fires in
November, peaking in December and a gradual reduction up to March. November is the
beginning of the dry season and so wildfires start at about the same time and increases as the
vegetation gets drier. By March most of the vegetation is burnt and so results in the low number
of fires in the period.
Table 1a: Mean fire values for the period November 2000 to March 2010 in Ghana (MODIS
Active Product).
Year Nov Dec Jan Feb Mar
2000 33.4 86.6 62.6 29.5 5.6
2001 15.0 99.8 47.3 17.0 3.5
2002 18.6 86.6 49.6 13.5 6.0
2003 21.9 49.3 43.0 23.1 3.3
2004 25.7 71.8 70.9 15.7 2.4
2005 31.8 89.5 35.9 11.3 2.4
2006 9.5 76.5 58.1 13.9 6.4
2007 15.9 71.6 74.4 19.1 2.9
2008 18.0 65.0 60.8 7.3 2.9
2009 9.6 54.3 56.5 21.4 2.1
2010 14.9 104.8 68.3 24.5 1.3
Table 1b: Total number of fires in each pixel by month on the MODIS Active Fire Products
(November 2000 to March 2010)
Year Nov Dec Jan Feb Mar
2000 2738 7101 5132 2418 462
2001 1234 7682 3875 1398 285
2002 1524 7101 4066 1106 492
2003 1796 4041 3523 1895 274
2004 2107 5889 5811 1287 199
2005 2610 7343 2940 929 197
2006 782 6275 4763 1138 522
2007 1303 5870 6103 1569 241
2008 1476 5328 4985 602 234
2009 791 4449 4632 1754 176
2010 1225 8592 5598 2010 106

Table 2a indicates the coefficients (r) for the correlation between mean monthly
temperatures, average monthly precipitation and MODIS Active Fire Products and table 2b
represents the correlation between mean monthly temperature and average monthly
precipitation. It can be seen that there is no meaningful correlation between the climate variables
and the fire data. This is probably due the variations in both the vegetation types and the
variations in climate variables between the northern and middle belts and the southern zone. The
northern part of the country is mostly dry and largely savannah vegetation (which are more
prone to wildfires) and the southern parts are deciduous, moist evergreen and wet evergreen
forest (which are less prone to wildfires).
Table 2a. Correlation coefficients (r) of MODIS Active Fire Products and climate variables.
Mean monthly
temperature
Average month
precipitation
November 0.69 0.15
December -0.53 0.54
January -0.24 -0.09
February -0.32 -0.03
March -0.35 0.10
Table 2b. Correlation coefficients (r) of mean monthly temperatures and average monthly
precipitation.
Month Correlation coefficient
November 0.22
December -0.30
January -0.34
February 0.30
March 0.34
Figure 3 indicates the month-by-month correlation between mean monthly temperatures
and mean number if fires. Figure 4a shows the month-by-month relationship between average
monthly precipitation and mean number of fires for the corresponding months and figure 4b
shows the relationship between mean monthly temperatures (o
C) and average monthly
precipitation from November 2000 to March 2010. The scatter plots and the trend lines in both
cases highlight that there is no significant relationship between the individual monthly climate
variables and the MODIS Active Fire Products used.

Figure 3. Scatter plots indicating the relationship between mean number of fires and mean
monthly temperatures for November 2000 to March 2010.
The nature of the dots and the trend lines clearly indicate that there was not significant
relationship between the individual mean monthly temperatures and the mean number of fires
for the period.
y = 11.962x - 310.16
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
26.5 27.0 27.5 28.0 28.5
MeanNo.offire
Mean Temperature(oC)
November
y = -19.972x + 615.59
40.0
50.0
60.0
70.0
80.0
90.0
100.0
110.0
26.5 27.0 27.5 28.0
MeanNo.offire
December
y = -3.6225x + 153.66
30.0
40.0
50.0
60.0
70.0
25.0 26.0 27.0 28.0 29.0
MeanNo.offire
January
y = -2.514x + 92.043
0.0
10.0
20.0
30.0
40.0
28.0 29.0 30.0 31.0
MeanNo.offire
February
y = -1.4115x + 46.022
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
29.0 29.5 30.0 30.5 31.0 31.5
MeanNo.offire
March

Figure 4a: the relationships between mean number of fires and average month precipitation for
November 2000 to March 2010.
y = 0.0741x + 11.875
7.0
12.0
17.0
22.0
27.0
32.0
37.0
70.0 120.0 170.0
MeanNo.offire
Avearge Precipitation(cm)
November
y = 0.781x + 27.113
40.0
60.0
80.0
100.0
120.0
45.0 55.0 65.0 75.0 85.0 95.0
MeanNo.offire
Average Precipitation (cm)
December
y = -0.0502x + 59.27
32.0
42.0
52.0
62.0
72.0
82.0
25.0 35.0 45.0 55.0 65.0 75.0
MeanNo.offire
Average Precipitation (cm)
January
y = -0.0179x + 18.497
5.0
10.0
15.0
20.0
25.0
30.0
35.0
20.0 30.0 40.0 50.0 60.0
MeanNo.offire
Average Precipitation(cm)
February
y = 0.0217x + 2.2532
1.0
2.0
3.0
4.0
5.0
6.0
7.0
42.0 52.0 62.0 72.0
MeanNo.offire
Average Precipitation(cm)
March

Figure 4b: the relationships between mean monthly temperature (o
C) and average monthly
precipitation for November 2000 to March 2010.
y = 8.088x - 120.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
26.5 27.0 27.5 28.0 28.5
Averageprecipitation(cm)
Mean temperature (oC)November
y = -7.828x + 275.6
0.0
20.0
40.0
60.0
80.0
100.0
26.5 27.0 27.5 28.0 28.5
Meantemperature (oC)December
y = -4.852x + 174.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
25.0 26.0 27.0 28.0 29.0
Meanmonthlyprecipitation
(cm)
Average monthly temperature (oC)January
y = 3.833x - 77.38
0.0
10.0
20.0
30.0
40.0
50.0
60.0
28.0 29.0 30.0 31.0
Averagprecipitation(cm)
Mean temperature (oC)February
y = 6.779x - 145.1
0.0
20.0
40.0
60.0
80.0
29.0 29.5 30.0 30.5 31.0 31.5
Mean temperature (oC)March

Figure 5 shows the pattern of fires from the beginning of the dry season in November to March
when majority of dry fuel load would have been burnt. The number of pixels indicating fire in
the month of November are limited mainly to the north-western corner of the map. This pattern
is also visible in table 1b (where the total number of fires in November is lower in and increases
in December). The burnt area increases as seen in the maps in December and begins to decrease
until in March, when there is a very limited number of fire pixels. There is a concentration of fire
pixels in the northern part of the country and very few number of fire pixels in the southern
part.
Figure 6 shows the observed mean monthly temperatures for the period November 2000
to March 2010, the same time period as the MODIS Active Fire Products. These maps also
indicate, in general, decreasing mean monthly temperatures from the north to the south of the
country. The northern and middle belts have higher mean monthly temperatures than the
southern and coastal parts.
Figure 7 shows the pattern of observed average monthly precipitation for the same time
period as both MODIS Active Fire Products and the mean monthly temperature data. Even
though the period coincides with the dry season, it is apparent that some parts of the country,
mainly the southern portions receive some amount of rainfall (coinciding with the southern
passage of the ITCZ). The northern parts are drier than the south and the dryness reduces
southwards, and this could also explain the lack of correlation between the individual monthly
climate data and mean fire occurrence (shown on table 2).
Comparing the figures 5, 6 and 7 show that there is a close relationship between the
pattern of wildfires and the movement of the ITCZ which gives rise to the pattern of the climate
in the country. As one moves southwards of the country the number of fire pixels increase with
the months. The northern and middle belts indicate there is more fire that there is in the
southern and coastal belt. The southwestern corner of the country indicates that there is virtually
no fire. The south-western corner has the highest amount of rainfall in the country and the
ITCZ does not completely retreat from that portion of the country. Hence vegetation in that
part of the country never completely dries out. In addition, the area has moist evergreen and wet
evergreen vegetation types (figure 1b), resulting from the climatic. The vegetation in the area
does not completely dry out in the short dry season and so reduces ignition possibility and
escalation of wildfires.

Discussion
This study aimed at establishing the relationship between MODIS CMG Active Fire Products
and the climate variables mean monthly temperatures and average monthly precipitation over a
five month period of the dry season, using data from November 2000 to March 2010, for both
climate variables and fire. The climate variables were at a 0.5o
by 0.5o
resolution, whereas the fire
data were at a 0.25o
by 0.25o
resolution. The study also aimed at investigating the patterns of
wildfire occurrence and the pattern of climate in the country, using the north-south movement
of the intertropical zone of convergence (ITCZ), which brings the southwest monsoon winds to
the country.
It was discovered that there is no significant correlation between the individual mean
monthly temperatures and the average monthly precipitation and the MODIS Active Fire
Products. However, taken together (figures 5, 6 and 7) there is a strong relationship between the
pattern of wildfire occurrence and the pattern of climate. As the mean monthly temperatures
increase southwards, the average monthly precipitation decreases and the burnt area increases.
This pattern coincides with the annual movement of the ITCZ which controls the wet and dry
seasons in Ghana.
In general, SSA fire patterns are closely related to the southward movement of the ITCZ
across the region (Swap et al. 2002 & N‟Datchoh et al. 2015). This pattern is also observed in
Ghana in this study. As the ITCZ starts to retreat southwards in November (figure 5), the extent
of wildfires are very limited to just portions of the area in north western corner of the November
map. The extent of burnt areas and total number of fires per pixel in a month (table 1b) are also
very small in November compared to that of the month of December. As the ITCZ retreats in
the subsequent months, the burnt areas extend southwards because the area becomes drier due
to lack of rains and the influence of the dry northeast trades (harmattan winds). In February, the
burnt area extends to almost the middle of the country (the transition zone), because the ITCZ
has retreated to the southwestern corner of the country. By March, fire pixels are limited to only
few areas (figure 5: comparing the month of March to December). Kugbe et al. (2012) also
observed this reduction in the number of fires in March in Ghana and attributed it to a reduction
in the amount of fuel load available for burning (as observed in figure 5). By March almost all
dry foliage would have been exhausted and that accounts for the limited number of fire pixels in
that month.
The study reveals the seasonality of wildfire occurrence in Ghana. This seasonality is
influenced by the climate of the various areas in the country. Areas with prolonged dry seasons
have high number of fires than areas with relatively shorter dry seasons. The savannah and
transitional zones have relatively longer and more intense dry seasons than the deciduous, moist
evergreen and wet evergreen areas (figure 1b). These are influenced by the climatic patterns. Le
Page et al. (2010) observed such seasonality of wildfires on a global scale. They observed that
Sub-Saharan African fires start in November, and move along the ITCZ. They reported that the
pattern of fire goes with agricultural activities such as harvesting and preparation of lands for
cultivation (which are triggers of wildfires in Ghana). Westerling et al. (2003) in a study of
climate and wildfires in Western United States also concluded that wildfires were strongly
seasonal. Heyerdahl, Brubaker & Agee (2002) further confirm the seasonality of wildfires based
on climate influences. Studying the decadal occurrence of fires, they observed that large fires
mostly occurred in the dry season and during El Nino years in interior Pacific Northwest of the
United States of America.
Wildfires are more common in areas that have dry seasons because the vegetation dries
up and provides fuel for combustion (Mbow, Nielsen & Rasmussen 2000). Thus, wildfire
occurrence in Ghana vary from north to south, as is the case with the climate pattern in Ghana.

Devineua, Fournier & Nignan (2010) and Kugbe et al. (2012) have observed that the northern
portion Ghana has a large savannah and grassland area comprising mainly of herbaceous and
scrubland which are more amenable to wildfires. The tropical savannah ecosystems are produce
very rapidly and are very flammable (Bowman et al. 2009) because they are composed of grasses,
trees and scrubs, which provide sufficient fuel load for combustion.
Areas in Ghana which are dominated by vegetation with high amount of dry foliage in the
dry season are more prone to wildfires than areas with relatively wet foliage. The type of
vegetation in Ghana, as it is everywhere, is closely related to the climate. Comparing figure 1b
and figure 5, it is obvious that the areas that contain the most fire pixels coincide with the
savannah belt and transition zones in the country, whereas areas in the deciduous, moist
evergreen and wet evergreen forests show fewer fire pixels. The climate in the savannah and
transition zone have a prolonged dry season than areas with deciduous, moist evergreen and wet
evergreen forests because of the combined influence of the retreat of the ITCZ and the advance
of the harmattan winds from the Sahara Desert. On average, the area occupied by the
deciduous, moist evergreen and wet evergreen forests is smaller than the area occupied by the
savannah and transition belts. This mismatch is a probable cause of the lack of correlation
between the individual monthly climate variables and the fire data. Archibald et al. (2010) in a
study of Southern African fire regimes realized that areas with vegetation types that are
dominated by a grass-layer (savannah, grassland and forest transitions) burnt more extensively
than areas characterized by rainforest and semi-deciduous forest. The savannah zones of SSA are
very prone to wildfires (Giglio et al. 2010) due to agricultural activities such as slash-and-burn,
nomadism and hunting (Archibald et al 2010).
Conclusion
From the results above using our methodology, it is clear that wildfires in Ghana are influenced
largely by climate because the number of fires follow the pattern of the movement of the ITCZ,
the major phenomenon influencing weather and climate in Ghana, even though there were no
correlation between individual monthly climate variables and mean monthly fire occurrence.
It also confirms the results by other studies which conclude that the pattern of wildfire
occurrence and the extent of burnt areas in savannahs are influenced by the movement of the
ITCZ. Further, the study observed the link between the vegetation of an area and its climate
which determines its susceptibility to wildfires. By implication the vegetation type, which is
determined by the climate, plays a significant role in determining the amount and type of fuel
load needed for ignition.
I suggest that further studies should be on establishing the relationship between fire and
climate variables in various ecological zones in Ghana rather than looking at the entire country as
a whole, because the differences in vegetation masks the full effect of correlation between
monthly climate variables and bushfires.

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C. A. (2011). The human dimension of fire regimes on Earth. Journal of biogeography, 38(12), 2223-
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Bowman, D. M., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A. ... &
Johnston, F. H. (2009). Fire in the Earth system.science, 324(5926), 481-484.
Cardil, A., Eastaugh, C. S., & Molina, D. M. (2015). Extreme temperature conditions and
wildland fires in Spain. Theoretical and Applied Climatology, 122(1-2), 219-228.
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Devineau, J. L., Fournier, A., & Nignan, S. (2010). Savanna fire regimes assessment with MODIS
fire data: their relationship to land cover and plant species distribution in western Burkina Faso
(West Africa). Journal of Arid Environments, 74(9), 1092-1101.
Exploring Africa website: Vegetation map of Ghana. Accessed 12/15/2015.
http://exploringafrica.matrix.msu.edu/curriculum/unit-five/module-twenty-four/module-
twenty-four-activity-one/.
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p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 3, pp. 21-30, ©IJHSS
Influence of students’ self perception on biology
achievement among secondary school students in Nakuru
county, Kenya
Nyambura Rose
Department of Curriculum and Educational Management
Laikipia University, Kenya.
Abstract
Acquisition of biology knowledge and skills at Kenya’s secondary school is measured by
administration of tests especially at national level. Achievement in biology has not been satisfactory
in Kenya Certificate of Secondary Education examination (KCSE) and scholars have fronted various
reasons that contribute to this unsatisfactory achievement. The factors include students’ negative
attitude towards the subject, lack of teaching/learning resources and inadequate staffing.
Researchers have also investigated students’ entry behavior which varies from individual to
individual and so do learning outcomes. Irrespective of entry behavior, when meaningful learning
takes place, the expectation is improved academic achievement. However this is not always the case
and this study aimed at assessing the impact of students’ self perception on achievement in biology.
This study was guided by self perception theory (SPT) and adopted ex-post facto research design.
Random sampling was used to select a sample size of 390 Form three students from three randomly
selected secondary schools in Nakuru County, Kenya. The data was collected by use of a
questionnaire (Students’ questionnaire). The data collected was coded, categorized and then analyzed
using descriptive and inferential statistics with the help of statistical package for social sciences
(SPSS) version 22.0. Null hypothesis were tested at .05 significance level. The Study findings showed
that students had positive self perception which had no statistical significant influence on biology
achievement among students in Nakuru County, Kenya.
Key words; Self Perception, Biology Achievement, Science Education and Millennium
Development Goals (MDG’S).
Introduction
Science Education is emphasized in Kenya’s secondary school curriculum in order to produce a
scientifically literate populace and professionals in science and technology. Keraro and Shihusa
(2005) argue that biology makes a contribution towards this objective. Biology is a unique science in
that experiments with living organisms can take place in the laboratory and in the field making the
subject fun to learn. Prokop, Prokop & Tunicliffe, (2000) however pointed out that there is increased
use of virtual environments instead of practical investigation in biology teaching. This perhaps has

impacted on overall achievement in biology in the Kenya Certificate of Secondary Education
Examination (K.C.S.E) and at Nakuru county level as shown in Table 1.
Table 1
KCSE Biology Achievement Scores between 2011 and 2013 at National and County level.
Year National Nakuru County
Mean Score (%) Mean score (%)
2011 32.42 35.5
2012 31.6 38.2
2013 32.3 38.3
Source: KNEC Examination Report (2012-2014); Nakuru County Education day booklet (2012-
2014).
Table 1 shows that Nakuru county KCSE biology achievement scores are higher than
national scores. However both scores are far below 50% meaning low achievement in biology which
triggers concern among educationists. If all counties in Kenya explore various options of boosting
achievement in biology like adopting measures that enhance students’ self perception, then the
national biology average score might go up. This study explored the influence of self perception on
form three students’ achievement in continuous assessment tests in biology from randomly selected
secondary schools in Nakuru county. The scores used were form three end of term one, term two
and term three biology continuous assessment test scores.
Biology knowledge is a pre-requisite for national development as highlighted in Kenya’s blue
print for development -Vision 2030. Samikwo (2013) highlighted that Kenya is lagging behind in its
development agenda and such developments require workforce that achieves highly in science
subjects like biology. Biology knowledge contributes in new discoveries for example in the field of
medicine, population control, food security, pollution control and sustainable utilization of natural
resources. Biological knowledge is also necessary to ensure natural resources are in sufficient
replenishment and supply (Ongowo & Hungi, 2014).
Kenya made primary and secondary education free in 2003 and 2008 respectively. The
country aimed at meeting the objectives of education for all (EFA) by the year 2015 and millennium
development goals (MDG’S). However, recent studies indicate that most developing countries
including Kenya are far from achieving MDG’S (Murunga, Kilaha and Wanyonyi, 2013). This
necessitates acquiring of adequate biological knowledge by the students and subsequent utilization
of the acquired skills so as to be in a position to participate fully in scientific development.
In science education, students should take personal responsibility and control during the
learning process. A number of studies have been directed towards the affective components of
cognition like motivation and self regulation. These two components are crucial in the process of
cognitive engagement and conceptual change (Tang & Neber, 2008). Self perception of students has
also been investigated with emphasis on self concept so as to develop and maintain positive attitude.
Self perception is an important source of self concept among other sources like reflected appraisals
and social comparisons.

Self perception theory (SPT) developed by Daryl Bem in 1965 postulates that people induce
attitudes without accessing internal cognition and mood. However, Weiner (1999) observed that
teenagers consciously or subconsciously look inward at themselves and weigh whether other
peoples’ thoughts, attitudes, actions and reactions will work for them until they begin to see
themselves in their own way. Self perception therefore may vary from time to time impacting on
academic achievement. SPT sufficiently guided this study which aimed at finding out the influence
of students’ self perception on achievement in biology among secondary school students in Nakuru
County, Kenya.
STATEMENT OF THE PROBLEM
Relationship between self-perception and academic achievement is well established in literature but
little research has been done on the topic in Kenya especially in secondary schools within Nakuru
county. This study therefore aimed at making a contribution towards filling this gap. To this end the
study examined the relationship between students’ self perception and achievement in biology
among students in secondary school in Nakuru county.
Objectives of the study
1. To determine if there is a relationship between students’ self perception and achievement
in biology among secondary school students in Nakuru county
2. To find out if there is a difference in self perception among male and female students in
secondary schools in Nakuru county.
3. To investigate if there is a difference in biology achievement among male and female
secondary school students in Nakuru county
Null Hypotheses
1. There is no statistically significant relationship between students’ self perception and
achievement in biology among secondary school students in Nakuru county
2. There is no statistically significant difference in self perception among male and female
students in secondary schools in Nakuru county.
3. There is no statistically significant difference in biology achievement among male and female
secondary school students in Nakuru county
METHODOLOGY
Design
This study was guided by self perception theory (SPT) and adopted ex-post facto research design
which is applied in those studies where the independent variables have interacted with dependent
variables. Consequently, the effect of interaction between the variables is determined retrospectively
(Kerlinger, 2002).
Participants
The target population was secondary school students and the accessible population was
approximately twenty eight thousand form three secondary school students in Nakuru county.
Sample size was determined by formula developed by Krejcie and Morgan in 1970 (Kathuri & Pals,
1993). 390 randomly selected form three students participated in the study. Simple random sampling
was used to select 3 secondary schools and 130 form three students were randomly selected from
each of the participating school.

Data collection and analysis
Data was collected by use of students’ questionnaire (SQ). The instrument was pilot tested in one
secondary school in neighbouring Nyandarua county to test reliability. Cronbach’s alpha was used to
assess whether items in the instrument measured students’ perception. An alpha level of at least .70
was accepted and considered suitable to make possible group inferences that are accurate enough
(Orodho, 2008).
The researcher administered the questionnaire to the study sample and collected it
immediately the participants completed filling in the required information. The items were scored,
coded and analysed using SPSS version 22.0.
Results and discussion
Self perception among students
It was found out that most students have a positive self perception since they agree and strongly
agree with positive statement on self perception and disagree and strongly disagree on negative
constructed statements. Majority of the students like the person they are (96%), make decisions on
their own (88%), are comfortable with their physical appearance (97%), pretty sure of themselves
(78%) and always do the right things (93%). Moreover the students are not scared to talk in front of
the class (53%), are not clumsy (59%) and are very agreeable (59%). In addition, 47% of the
students are not sure whether their classmates like them a lot while 25% are not sure whether most
times they do the right things. These findings are shown in the table 2 below
Table 2
Self perception percentages among students
perception Strongly
disagree Disagree
Not
sure Agree
Strongly
agree
I like the person that i am .5% 1.6% 2.3% 14.8% 80.8%
I make decisions on my own with ease 1.6% 4.8% 5.3% 45.3% 42.9%
I rely on my friends for most decisions
in my life
32.6% 40.7% 10.4% 13.2% 3.1%
I like to be called upon in class 11.3% 16.8% 26.3% 33.2% 12.4%
Most of my friends like me 2.3% 1.6% 37.3% 30.8% 27.9%
I am fun to be with 3.0% 5.5% 24.3% 29.6% 37.6%
I am comfortable with my physical
appearance
1.5% 1.0% .5% 16.5% 80.4%
I am pretty sure of myself 4.6% 5.2% 12.9% 20.6% 56.7%
I often doubt myself 37.4% 30.2% 12.7% 10.9% 8.8%
I wish i was someone else 65.6% 18.5% 4.1% 5.4% 6.4%
I am scared to talk in front of the class 29.6% 23.7% 20.9% 15.5% 10.3%
Most times, i do the right things 3.4% 11.7% 25.5% 35.7% 23.7%
I rarely get worried 15.9% 23.3% 19.5% 32.3% 9.0%

My classmates like me a lot 1.8% 2.6% 47.4% 30.7% 17.5%
I can solve most problems in my life 3.1% 10.8% 17.5% 34.5% 34.0%
I rarely disagree with people around me 8.7% 25.9% 20.0% 31.5% 13.8%
I am a clumsy person 35.1% 24.1% 25.7% 4.2% 11.0%
I would never change a thing about
myself
13.9% 21.6% 11.3% 11.3% 41.8%
I always try to do the right things 1.0% 1.5% 4.4% 39.7% 53.3%
I take long to adapt to something new 13.3% 26.3% 9.6% 32.3% 18.5%
i am often sorry for the things i do 9.5% 18.8% 18.3% 34.0% 19.3%
I am unattractive 52.3% 26.2% 13.0% 2.8% 5.7%
I have no problem expressing my
opinion
3.1% 9.6% 19.5% 21.4% 46.4%
I am very agreeable person 3.9% 14.8% 22.3% 35.6% 23.4%
Self perception and biology achievement
The Average continuous assessment marks in biology year 2015 per student were calculated by
getting the mean marks of individual scores in biology in the three school terms of the year. For the
390 students, the mean average marks in biology year 2015 is 28.08% with a standard deviation of
11.372% and a range between 8% and 69%. This is illustrated in the table 3.
Table 3
Average marks in biology year 2015
Number
of
students
Minimum
mark
Maximum
mark Mean Std. Deviation
390 8 69 28.08 11.372
Self perception was computed as a variable using median as measure of central tendency in the likert
scale where values were assigned as follows; 1=Strongly Disagree 2=Disagree 3= Not Sure 4=Agree
5=Strongly Disagree. The study reveals that majority (51%) of students agreed upon the statements
about their self perception whereas 39% of the respondents were not sure of their self perception.
The Percentages of computed self perception 4-5 does indicate a clear trend of either decrease or
increase of positive self perception with average marks in biology. This is as shown in the table 4.
However A chi square test gives a p-value = 1.635 at ∞ = 0.05 level of confidence i.e. p-value =
1.635 > 0.05 the null hypotheses is not rejected therefore, no statistically significant relationship
between students’ self perception and achievement in biology among secondary school students in
Nakuru county
Table 4
Average marks in biology year 2015* Computed self perception Cross tabulation
Computed self perception Total

Average biology
marks
2.00 2.50 3.00 3.50 4.00 4.50 5.00
Percentages
of computed
self
perception 4-
5
Less than 20
21-30
31-40
41-50
51-60
61 and above
Total
0 0 26 13 66 0 2 107 63.5
0 0 65 2 76 4 12 159 57.8
2 0 33 2 29 0 2 68 45.6
0 2 12 4 14 2 2 36 50
0 0 4 0 6 0 0 10 60
0 0 8 0 2 0 0 10 20
2 2 148 21 193 6 18 390
A two tailed test on Pearson correlation between self perception and average marks in biology
shows that there exist a very weak positive correlation of +0.023.
Gender difference in self perception
A two tailed test on Pearson correlation between self perception and gender shows that there exist a
very weak positive correlation of +0.042 almost zero to show that there is no relationship. More
males than females have a positive self perception as shown by 70% and 40% respectively in the
table 5.
Table 5
Gender* Computed self perception cross tabulation
Computed self perception
Total
Percentages of
computed self
perception 4-52.00 2.50 3.00 3.50 4.00 4.50 5.00
Sex Female 0 0 50 4 113 6 8 181 70.16
Male 2 2 98 17 80 0 10 209 43.06
Total 2 2 148 21 193 6 18 390
However A chi square test gives a p-value = 1.635 at ∞ = 0.05 level of confidence , p-value = 1.635
> 0.05 thus the null hypotheses retained. There is no statistically significant difference in self
perception among male and female students in secondary schools in Nakuru county
Gender difference in biology achievement
Males outdo females in biology achievement in the grouped marks of 21-60 while females outdo
males in biology achievement in the grouped marks of 61 and above (Figure 1).

Figure 1: Gender difference in biology achievement
A two tailed test on Pearson correlation between self perception and gender shows that there exist a
very positive correlation of +0.412. A chi square test gives a p-value = 0.831 at ∞ = 0.05 level of
confidence i.e. p-value = 0.831 > 0.05 null hypothesis is retained, thus there is no statistically
significant gender difference in biology achievement among students in secondary schools in
Nakuru county.
Conclusions
Self perception among students is positive. Students can make decisions on their own and
always do the right things. Moreover students are neither clumsy nor scared to talk in front of the
class a show of self confidence. The self perception perspective that people derive their inner
feelings or abilities from external behaviors was noted. However students self perception does not
affect their performance in biology as per the findings of the study. Performance may be attributed
to other factors for example entry behavior and negative attitude towards biology. The sex of the
students had a significance influence on self perception as self perception is portrayed to vary with
gender. A greater percentage of female students as compared to males score more computed self
perception. Performance in biology subject does not significantly differ with the gender of the
student since both mean marks of males and females students in biology coincides with the class
mean mark..
Further research is recommended to find out the cause/s of low achievement in biology
both at county level and national level among secondary school students in Nakuru county, Kenya.
References
Kathuri, N. J & Pals, D. A (1993). Introduction to research. Educational material center, Egerton
University.
Kenya National Examination Council. (2012). The Year 2009 Kenya certificate of secondary examination
report. Nairobi; Government Printers
53
79
25
14 4 6
54
80
43
22
6 4
0
10
20
30
40
50
60
70
80
90
Less than
20
21-30 31-40 41-50 51-60 61 and
above
No.ofstudents
Average biology marks
Female Male

Keraro, F. N & Shihusa, H (2005). Effects Of Advance Organizers On Students' Achievement In
Biology: A Case Study Of Bureti District, Kenya Journal of Technology and Education in
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STUDENTS’ QUESTIONNAIRE (SQ)
This study aims at finding out the influence of self perception and academic self concept on
achievement in Biology among secondary school students. Please fill in the questionnaire and the
information collected will be used for academic purposes only.
SECTION A; SUDENTS’ BIODATA
1. Sex : Female Male
2. Age in years:
3. KCPE Marks:
4. Class
SECTION B: STUDENTS’ SELF PERCEPTION
Please tick the statement which describes how you feel about yourself according to the scale given.
There is no right or wrong answers to these statements.
Statement Strongly
Agree
Agree Not
sure
Disagree Strongly
disagree
1.I like the person that I am
2.I make decisions on my own with ease

3.I rely on my friends for most decisions in
my life
4.I like to be called upon in class
5.Most of my friends like me
6.I am fun to be with
7.I am comfortable with my physical
appearance
8.I am pretty sure of myself
9.I often doubt myself
10.I wish I was someone else
11.I am scared to talk in front of class
12.Most times, I do the right things
13.I rarely get worried
14.My classmates like me a lot
15. I can solve most problems in my life
16.I rarely disagree with people around me
17. I am a clumsy person
18.I would never change a thing about
myself
19.I always try to do the right things
20.I take long to adapt to something new
21.I am often sorry for the things I do
22.I am unattractive
23.I have no problem expressing my
opinion
24.I am a very agreeable person
SECTION C: SELF ACADEMIC CONCEPT ASSESMENT
Please tick the statement which describes how you feel about academics according to the scale given.
There is no right or wrong answers to these statements, so feel free to answer.
STATEMENT Definitely
true
True Not
Sure
Not
true
Definitely
not true
1.I am a hard working student at school
2.I do my school work with a lot of ease
3.I enjoy studying biology more than any
other subject

4.I spend most of my time doing my
school work
5.I enjoy participating in class activities
6.I often lead my classmates in tasks
assigned by our teacher
7.My peers often consult me in class
assignments
8.I am happy with my academic
achievements
9. I can never achieve highly in biology
10.I learn most concepts in biology very
fast
11.I have trouble with most school
subjects
12.I often need help in most school
subjects
13.I enjoy studying biology
14.I get good marks in biology
15.I don’t like most school subjects
16.I often look forward to biology lessons
17.I often need help in most school
subjects
18. I really feel good about my
achievement in most school subjects
19.I hope to excel in my study
20. My grades in biology and all other
subjective will secure for me a position in
university
21.I am well aware of my future
profession
22.I have trouble understanding anything
in biology
23.Biology is the easiest subject in biology
24.Biology is my favourite subject
25.I never want to continue studying
biology after secondary school
Thank you for answering this questionnaire

p-ISSN: 1694-2620
e-ISSN: 1694-2639
Vol. 8 No. 3, pp. 31-43, ©IJHSS
The influence of clothing in the negotiation of identities. A
study between students and lecturers.
Simon Ntumi
University of Cape Coast, Ghana
Department of Educational Foundations
Email: simon.ntumi@stu.ucc.edu.gh
Esther Quarcoo
University of Cape Coast, Ghana
Department of Vocational and Technical Education
Email: esther.quarcoo@ucc.edu.gh
Abstract
Identity negotiation is the process by which perceivers target comes to agreement regarding the
identities that the targets are to assume in the interaction. In this contemporary era, the notion of
uniqueness shows that humans differ from one another, whilst what we do in the same way as well
as what we share and have in common with others is understood as the social aspects of ourselves.
The objective of the study was to find out the influence of clothing in the negotiation of identities in
terms of the relationship between students and lecturers from the students perspective. For the
study to be materialized, the researchers employed descriptive survey as the design for the study.
Questionnaire was the sole instrument used to elicit response from respondents. A reliability of 0.71
using the Cronbach’s Alpha was obtained. The sample size for the study was 248 respondents. The
key findings of the study revealed that most people consider their values, attitudes, status and mood
in choosing clothing and are also able to identify the moods and values of others through their
clothing. The findings further gave evidence that clothing plays a major role in helping student’s
identify their lectures. It was recommended that workshops and seminars about clothing should be
organized for both lecturers and students. And also on how the impact of clothing influence
negotiation of identity. Again, lecturers should be encouraged by the school authorities to put on
clothes that will differentiate them from their students. This will help them gain the necessary
recognition.
Key words: Identity, Negotiation, Lecturers, Students, Attitudes, Values, Status, Moods.
Introduction
The first scholar to use the term identity negotiation in the context of this study can be attributed to
William B. Swann, a professor of social and personality psychology. Notwithstanding, the term has
been around and has been used by several other authors in the social sciences. Swann (2007) a
learned scholar in field of social and personality psychology pioneered the study identity negotiation

theory. Swann described self-verification model as the theory which is based on the influence
individuals have over the manner in which they are perceived. This model is based on the notion
that individuals want people to understand them just as they understand themselves and therefore,
they deliberately act in a way such as to achieve this goal. On the other hand, the reverse process of
self categorization focuses attention on how individuals identify themselves with existing groups in
accordance with how their self-perception is influenced by others. Both models occur concurrently
and are connected although researchers most often analyze them separately. Identity can be defined
in this context as the outcome of a negotiation.
Based on this background, identity can be seen as the result of a negotiation process involving the
culture of individuals, self-conception and interaction among the individuals. Further, identity also
involves the processes by which individuals in a given society reach agreements regarding their
personalities. The process of identity negotiation thus establishes what people can expect of one
another. In the work of Turner- Bowker (2001), he views identity negotiation as a key concept which
provides the interpersonal cohesion among individuals. The fundamental principle of Swann’s
identity negotiation theory is based on conscious individual ambition to feel good in his society in
regards to the individual’s aspiration of psychological and interactional rationality. This emerges
from the assumption that individual’s desire is influenced by the world around them therefore, they
engage in social interaction with the anticipation of approving the expectations they have in life.
It is worthwhile for one to note that people engaged in notation identities so as to establish their
relationship with others in the society to foster coherence. Taking into consideration the fact that
negotiating identities plays a key role in social interaction, it is indeed evident that human
interpersonal relationships, emotions, values, attitudes and perceptions are influence by negotiation
of identity. “Just as identities define people and make them viable as humans, identity negotiation
processes also define relationships and make them viable as a foundation for organized social
activity” (Swann & Bosson, 2009. pp 69-71). Swann and Bosson (2006) further maintain that the
idea of identity negotiation is mostly applicable to a specific situation. To them, it is true that
humans persistently adopt some aspects of identities, but identity negotiation is indeed a concept
addressing an implicit, unconscious phenomenon, informal, automatic, open-ended issues.
Humans are social animals therefore human life is interwoven with one another. Their actions,
behaviour, perception, values, and above all appearance, is greatly influenced by people around
them. Individual’s styles of outfit and adornment are largely determined by the way in which people
around view and treat them. In effect, individual’s self- concepts offer some guidelines for
appropriate styles of dress. However, our perceived selves may not always coincide exactly with our
idealized selves. Since we realize the impact of appearance on others in interaction, we try to
improve the visible images that we present to them. We generally strive to present ourselves to
others in a manner consistent with our most positive self-interpretations. Each time we interact with
different individuals, we may modify our self-perceptions and re-evaluate our self-presentations. A
study by Baigh and Williams (2006), tested the idea that well-dressed individuals would present
themselves more positively than poorly dressed ones. The symbolic communication with others
serves asessential processes of individuals’ reflexive self-conceptions (Baigh & Williams, 2006).
Where school teachers mostly find themselves determines the type of clothes they should wear.
However, in many instances the principal usually decides on the dress code for the staff within a
particular school. As a result, the types of clothing teachers wear to work can vary. Some schools
require that teachers adhere to a business casual dress code. A business casual style of dressing

usually includes khaki pants, blouses, polo shirts and comfortable skirts and dresses of a modest
length. Teachers may work at schools that allow them to wear blue jeans in good condition. They
might also wear t-shirts, tennis shoes, and sandals. Despite this informal dress style, teachers must
exercise good judgment when choosing clothing for work. In rare cases, teachers may be required to
wear professional clothing. This may include suits and ties for men and skirts, dresses and pant suits
for women. Teachers who work in public or private schools may sometimes be asked to dress in this
manner (Polzer & Caruso, 2007).
Problem statement
The problems of modern contemporary era flow from the attempt of the individual to maintain
independence and individuality in his existence against the sovereign powers of society, and how to
live to the expectation of the society demands (Howard, 2000). The quest by individuals to resist the
forces and pressures levelled by culture and society, while still depending entirely on the society is
the most interesting of issues that needs consideration. Largely, the most discussed issue in social life
today is the duality of the individual in regards to his identity. It is imperative that today, the idea of
identity negotiation demonstrates the autonomy we have as independent entities that help in
expressing of our differences from others. Despite the impact and influential aspect of negotiating
identities in many societies, the issue of identity negotiation appears to be a tendency and
characteristic of only more developed societies than the less privileged societies. It is evident that a
lot of studies have been conducted on the negotiation of identities including works by Touche-
Spelcht (2004) but it appears that not much study have been done on the influence of clothing in the
negotiation of identities in terms of the relationship between lecturers and students. The conflict
resulting from this internal dialog within individuals, sometimes labelled as an identity crisis, is the
motivational background for this work. This therefore gives the researchers the impetus to conduct
an empirical study to investigate the influence of clothing in the negotiation of identities in relation
to students and lecturers in University of Cape Coast Campus.
Rationale for the study
The general purpose of this study was to find out the influence of clothing in the negotiation of
identities. However, specifically, the study aimed to come out with some factors that people consider
in selection of clothing, the role clothing plays in the negotiation of identities and finally, the role
clothing plays in the negotiation of identities between lecturers and students.
Research questions
The following research questions were formulated for the study;
1. What factors do people consider in clothing selection?
2. What role does clothing play in the negotiation of identities?
3. What role does clothing play in the negotiation of identities between lecturers and students?
Significance of the study
The findings of the study aimed to help know how clothing influences the negotiation of identities.
The findings again aimed to serve as guidance and counselling treatments and will also serve as basis
for education on proper dressing. The study again hopes to generate enough data to serve as a
reference point for other researchers interested in researching into this similar issue.

REVIEW OF RELATED LITERATURE
Theoretical groundings
Negotiated identities are constructed so that individuals can mutually develop a form of
interpersonal realities that will allow them to interact with one another. In order for symbolic
interaction to occur, these individuals must be capable of interpreting one another’s interests to the
extent that they can empathize with one another, or take the role of the other (Kaiser, 1985).
Clothing and personal appearance cues are used by individuals in interpreting roles, intents, and
personal attitudes and values. Stone (2007) has indicated that the meanings of appearance can be
symbolic of identities, values, moods and attitudes.
Attitudes
The appearance of individuals are anticipated by the reviewers in relation to their attitudes. One’s
present and past actions can be influence by appearance. There are a variety of stimuli toward which
we can propose attitudes through clothing. These involves objects (including clothes themselves),
social groups or institutions, people, places, events or situations, and issues. Attitudes toward
specific clothing styles are, of course, reflected through the clothes that we wear, by wearing certain
styles, we represent the groups to which we belong and express our degree of commitment to these
groups. Using Stone’s interpretation of communicated attitudes, it may be asserted that behaviours
are likely to be anticipated as a result of group memberships (Stone, 2007).
Moods
In the view of Stone (2007), mood may be compared to feelings of pride one has about his or her
appearance, with regard to a set of values that serve as a reference for self-evaluations. Mood is
largely related then to social feedback received from others. Others may use visible cues (for
example, bright colours, grooming) in interpreting and defining mood in a given situation. Mood is a
very intangible, transient quality that is difficult to study or measure. It may be susceptible to regular
change and is not necessarily accurately reflected through an individual’s clothes at any given time.
Our moods may change from the wary we feel when selecting what to wear in the morning. Thus,
clothes are not a very reliable cue for assessing another’s mood
Values
Values are abstract principles of behaviour to which we feel committed. They provide organization
for our behaviour and allow us to compare our own goals with those of others (Beaudoin &
Lachance, 2006). In this way, they provide us with a means of self-evaluation. Values may be
compared to beliefs, or the many inferences we make about the world, in that values are entirely
located in a particular belief system. Thus, values are much more generalized and entail to the self-
concept than beliefs. Values guide our perception and purchase of clothes and styles and accessories
as well as our planned selections of these items for our interactions. Some degree of commitment is
associated with personal values, and we tend to be somewhat emotional with respect to our
attachment to values. Our values tend to affect what we perceive to be important. This is often
referred to as selective perception.

METHODOLOGY
Descriptive survey research design was used for the study. The descriptive research design was
deemed appropriate for the study because as described by Creswell (2003) it offers the researcher
the opportunity to get the opinion of the population concerning some issues of interest relevant to
the study. It is suitable for selecting a sample and describing the real situation or phenomena as it
exists and hence more likely to give accurate information. The study sought to find out and
describe the behaviour of the respondents in respect of how clothing influences the negotiation of
identities. From the Krejcie and Morgan (2007) table for determining sample size from a given
population, a population of 700 has a corresponding sample size of 248 respondents. This was
done through the use of simple random sampling.
The instrument for data collection was solely questionnaires. The questionnaires items were drawn
in relation to the research questions set for the study. Questionnaires was considered most
appropriate for the study because it provides anonymity of the respondent and also because
respondents can read and write. Structured question items of closed ended nature were used in
collecting data from respondents. The questionnaires were in four sections; each section gathering
information on a specific variable. The first section elicited information on background of
respondents with the other three sections eliciting information on factors considered in clothing
selection, how clothing affects peoples’ identity and negotiation of identities respectively.
Reliability validity of the instruments
In order to enhance the validity of the study, the questionnaire was given to an expert for
assessment. This ensured both face and content related evidence of the items and also examined
whether the items relate to the research questions and also comprehensively cover the details of the
study. For the reliability of the instrument, a pre-test results was be used to determine the reliability
of the instruments which obtained Cronbach’s Alpha of 0.71 measure of internal consistency.
The data collected was edited, coded and analysed using the descriptive statistics of the Statistical
Product and Service Solution (SPSS version. 22.0) and presented in tables showing frequency and
percentage distribution, to help describe the status of the issue as it prevailed within the population
used for the study.The results of the findings were interpreted.
DATA ANALYSIS
Table 1a
Ages of Respondents (N=248)
Age Frequency Percentage (%)
17-20 years 67 27.0
21-24 years 114 46.0
25-28 years 66 26.6
Total 248 100.0
Source, Field Data (2016).
The table above indicates that, out of the total sample of 248, 67(27.0%) fell between the ages of 17
to 20, 114(46.0%) fell between the ages of 21 to 24 and 66(27.0%) out of the sample fell between

the ages of 25 to 28. This means that a large percentage of the respondents were within the ages of
21-24.
Graphical representation of age of respondents
Table 1b
Gender of respondent (N=248)
Gender Frequency Percentage (%)
Male 82 33.1
Female 166 66.9
Total
248 100.0
Gender is an important social, cultural and psychological construct, which describes the expected
attitudes and behaviours a society associates with sex (Alami et al, 2013). This therefore suggests
that sex of respondent’s forms an integral part in a study. It is evident from the table above that
82(33.1%) of the respondents were males whereas 166(66.9%) were females. This means that the
number of females who took part in the study were more than the males.
0
100
200
300
17-20 years 21-24 years 25-28 years Total
Age of Respondentse
Series1 Series2

Graphical representation of gender of respondents
Table 1c
Level of respondents (N=248)
Levels Freq. Percentage (%)
100 82 33.1
200 74 29.8
300 59 23.8
400 33 13.3
Total 248 100.0
It can be seen from the table above that 82 (33.1%) were level 100 students whilst 74(29.8%) were
level 200 students. It was also confirmed that 59(23.8%) were level 300 students whereas 33(27.0) %
were level 400 students. This means that there were more level 100 students in the study than all the
other levels.
Graphical representation of levels of respondents
MALE FEMALE TOTAL
Male, 82
Female, 166
Total, 248
Male, 33.1
Female, 66.9
Total, 100
gender of respondent
Series1 Series2
100
200
300
400
Total
level of respondents

Table 2
Research Question One
Factors people considered in clothing selection (N=248)
S
/
N
Statements SA (%) A (%) D (%) SD (%) Mean
(M)
Std.
Deviat
ion
1 I wear clothes to improve
my social status
92(37.1) 97(39.1) 43(17.3) 16(6.5)
1.9 .894
2 I consider my values in
choosing clothes
83(33.5) 124(50.0) 30(12.1) 11(4.4)
2.1 .998
3 I dress to impress others 52(21.0) 136(54.8) 51(20.6) 9(3.6)
2.0 .747
4 I dress to express my
mood
70(28.2) 155(62.5) 23(9.3) 0(0.0)
1.8 .583
5 I always want to look
good
83(33.5) 124(50.0) 30(12.1) 11(4.4)
1.8 .787
6 My attitudes determines
what I wear
93(37.5) 105(42.3) 38(15.3) 12(4.8)
1.8 .842
Total 248(100) 248(100) 248(100) 248(100) 11.4 4.811
Key: SA- Strongly agree, A- Agree, D- Disagree, SD- Strongly disagree, %- Percentage
Source: Field data, 2016
From the table, out of a total sample size of 248, it reveals that 189 (76.2%) agreed that they wear
clothes to improve their social status whereas 59(23.8 %) disagreed that they wear clothes to
improve their social status. Also, 207(83.5%) agreed that they consider their values in choosing
clothes and 41(16.5%) disagreed to that fact. It is again evident from the table that 188(75.8%)
agreed that they dress to impress others whereas 60(24.2%) disagreed that they dress to impress
others. Again, 225(90.7%) agreed that they dress to express their mood whilst 23(9.3%) disagreed
that they dress to express their mood. Two hundred and seven (83.5%) agreed that they dress to
always look good whilst 41(16.5 %) disagreed to that fact. Lastly, the table indicates that 198(79.8%)
agreed that their attitudes determined what they wear whilst 50(20.1%) disagreed that their attitudes
determine what they wear.
Further, the overall mean and standard deviation of (M=11.4, SD=4.811) of the respondents shows
that the responses on factors people consider in cloth selection is significantly higher. (M=11.4 out
of 15.81, SD= 4.811 out of 4.17).

Table 3
Research Question Two
How clothing affects peoples’ identity (N=248)
S/
N
Statements SA (%) A (%) D (%) SD (%) Mean
(M)
Std.
Devi
ation
1 My identity is sometimes
misinterpreted because of
what I wear
40(16.1) 122(49.2) 50(20.2) 36(14.5)
2.33 .915
2 I focus on brands in
choosing my clothing
45(18.1) 162(65.3) 39(15.7) 2(8.00)
1.99 .610
3 Social expectations
influence the way I dress
19(7.7) 172(69.4) 46(18.6) 11(4.40)
1.99 .590
4 The style of clothing
affects my identity
40(16.1) 173(69.8) 35(14.1) 0(0.00)
2.19 .634
5 I identify people mood by
the way they dress
22(8.9) 181(73.0) 45(18.0) 0(0.00)
1.97 .550
6 I identify peoples values
by the way they dress
20(8.1) 178(71.8) 46(18.5) 4(1.60)
2.09 .512
Total 248(100) 248(100) 248(100) 248(100) 12.56 3.811
Source: Field data, 2016
The table above confirmed that 162 (65.3%) of the respondents agreed that their identities are
sometimes misinterpreted because of what they wear whereas 86(34.7%) disagreed that their
identities are sometimes misinterpreted because of what they wear. Also, 207(83.4%) agreed that
they focused on brands in choosing their clothes and 41(23.7%) disagreed to that fact. It is again
evident from the table above that 191(77.1%) agreed that social expectations influence the way they
dress whereas 57(23%) disagreed that social expectations influence the way they dress. Again,
213(85.9%) agreed that the style of clothing affects people’s identity whilst 35(14.1%) disagreed that
the style of clothing affects people’s identity. Two hundred and three (81.9%) agreed that they
identified people’s mood by the way they dress whilst 45(18.0%) disagreed to that fact. Lastly,
198(79.9%) agreed that they identified people’s values by the way they dress whilst 50(20.1%)
disagreed that they identified people’s values by the way they dress. Also the overall mean and
standard deviation obtained from the responses (M=12.56, SD= 3.811) shows that responses with
respect to the attitudes of respondents on how clothing affects peoples’ identity is significantly
higher. Thus (M=12.56 out of 15.81, SD= 3.811 out of 4.17)

Table 4
Research Question Three
Negotiation of identities between lecturers and students (N=248)
S
/
N
Statements SA (%) A (%) D (%) SD (%) Mean Std.
Deviat
ion
1 Lecturers are easily
identified by their physique
0(0.0) 66(26.6) 158(63.7) 24(9.7)
2.13 .5595
2 Lecturers put on classy
clothes
3(1.3) 41(16.5) 169(68.1) 35(14.1)
2.83 .5792
3 Lecturers are easily
identified by the way they
dress.
2(8.8) 41(16.5) 157(63.3) 48(19.8)
2.95 .5949
4 Do you easily approach
lecturers by the way they
dress?
26(10.5) 166(66.9) 54(21.8) 2(8.80)
3.01 .6265
5 Are you able to identify the
mood of lecturers by the
way they dress?
43(17.3) 173(69.8) 30(21.1) 2(2.80)
2.12 .5827
6 Are you able to identify the
attitudes of lecturers by the
way they dress?
16(6.5) 170(68.5) 61(24.6) 1(0.40)
1.96 .5715
Total 248(100) 248(100) 248(100) 248(100) 15.99 3.5048
Source: Field data, 2016.
From the above table, it reveals that out of 248 respondents sampled for the study, 66 (26.6%)
agreed that lecturers are easily identified by their physique and 182 (73.4%) disagreed that lecturers
are easily identified by their physique. 44 (17.8%) agreed that lecturers put on classy clothes whereas
204 (82.2%) disagreed that lecturers put on classy clothes. Forty three (25.3%) agreed that lecturers
are easily identified by the way they dress whereas 205 (83.1%) disagreed that lecturers are easily
identified by the way they dress. A total of 192 (77.3%) agreed that they easily approach lectures by
the way they dress and 56 (30.6%) disagreed that they easily approach lectures by the way they
dress.Two hundred and sixteen (87.1%) agreed that they are able to identify the mood of lectures by
the way they dress whilst 32(23.9%) disagreed that they are able to identify the mood of lectures by
the way they dress. The table finally shows that 186 (75.0%) agreed they are able to identify the
attitudes of lectures by the way they dress whereas 62 (25.0%) disagreed they are able to identify the
attitudes of lectures by the way they dress. further, the overall mean and standard deviation of
(M=15.99, SD=3.5048) shows that the responses shows that negotiation of identities between
lecturers and students is significantly higher. (M=15.99 out of 15.81, SD= 3.5048 out of 4.17)

RESULTS AND DISCUSSIONS
Factors people considered in clothing selection
The rationale behind this research question was to explore the factors that influence the choice of
peoples’ clothing. It was revealed from the study that, there are a number of factors that influence
the choice of peoples’ clothing. Noticeable among them include; attitudes, moods, social status and
values. A high percentage (90.7%) confirmed that their mood influences their choice of clothes.
83.5% of the respondents also indicated that their values determine what they wear. 79.8% believed
that their attitudes influence their selection of clothes. A large number of respondents (76.2%) also
indicated that their choice of clothing is based on their social status. It can therefore be concluded
from the study that, factors such as attitudes, moods, social status and values play a major role in
clothing selection. The results is in conformity with the work of Stone (2007) who indicated that
peoples mood, values, identities and attitudes are some factors they consider in their clothes
selection.
How clothing affects peoples’ identity
The research question two was also to investigate how clothing affects people’s identity. The results
of the study gave ample evidence that people’s identity influence what they wear. A large number of
the respondents affirmed that their choice of clothing is greatly influenced by their values, moods
and attitudes. These results are parallel with the study of Beaudoin and Lachance (2006), who
affirmed that, values guide our perception and purchase of clothes and styles and accessories as well
as our planned selections of these items for our interactions. The findings of the study agrees with
the idea of Kaiser, 1985 that the elements of attitudes influence clothing preferences and taste.
Negotiation of identities between lecturers and students
The last research question to the study was to investigate and come out with the negotiation of
identities between lecturers and students. The results of the study confirmed that even though
clothing plays a major role in negotiating identities, the respondents revealed that do not easily
identify their lectures by their appearance. The results again revealed that students’ expectations,
such as having a particular type of physique or appearing in classy clothes are not met.
CONCLUSION, RECOMMENDATION AND IMPLICATIONS FOR PRACTICE
Based on the findings of the study it can be concluded that some influential factors that peoples
based in selection of clothes are attitudes, moods, values and status. The same factors influence the
choice of peoples’ clothing. Finally, it can be drawn from the study that on the University of Cape
Coast campus, lectures are not easily identified by their students by the way they dress.
Based on the findings, the researchers recommended the following;
Workshops and seminars on clothing should be organized for both lecturers and students on how
the impact of clothing influences negotiation of identities. Lecturers should be encouraged by the
school authorities to put on clothes that will differentiate them from their students. This will help
them gain the necessary recognition and full expectation from their students.
Implications for practice
The findings of the study serves as a very useful documents for the department of fashion in the
University of Cape Coast, as it has provided enough evidence to help the department come out with

clothing to meet peoples’ desires and expectations of individual societies. The study has also
generated enough data that brings a call for further studies in improving issues in negotiation
identities and also serve as a reference point for other researchers interested in researching into this
similar issue.
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