Validity test was carried out so as to ensure that the research instrument measured what it was supposed to measure. The four methods of measuring external validity are: face validity (Selltiz et al, 1976; Phillips, 1976 and Bailey, 1987); content validity (Goode and Hatt, 1952; Kerlinger, 1964; Bailey, 1987; Singleton et al, 1993); criterion validity (Phillips 1976; Selltiz et al 1976; and Bailey 1987) and construct validity (Stevens 1951, Kerlinger 1964 and Bailey 1987, Singleton et al, 1993). Face and content validity were carried out on this work. Content validity measures the appropriateness of the wording of the instrument and the objectives of the study while the face validity enables the researcher to make an assertion to claim to have measured what he or she intended to measure (Stevens, 1951). The validity measurement of this study was justified using the works of Levine (1981), Kerlinger (1983), Bailey 1987, Ekpo-Ufot (1992), Singleton et al, (1993). To ensure face and content validity of the instrument (content-related evidence), senior academics on entrepreneurship and enterprise development studies, specialists and experts on the topic of research measured by the instrument were asked to make their inputs and judge the appropriateness of the items on the instrument. This is to find out if the instrument covered the breath of the content area (and to ascertain if the instrument contains a representative sample of the content being assessed). The researcher also confirmed if the format used in designing the instrument is appropriate for obtaining the information required from the respondents.
3.12 Reliability of the Research Instrument
Reliability test ensures that the instrument measures consistently as required by this work. It also shows the extent to which the researcher can confidently rely on the information obtained through the use of the instrument adopted to gather data for the research work. Consequently, data collected were subjected to reliability analysis to establish the reliability of the measures and ensuring consistent measurement among the various items in the instrument (Goode and Hatt, 1952, Kerlinger, 1964, Phillips, 1976, Selltiz et al, 1976, Bailey, 1987, Singleton et al, 1993). Analysis to the reliability of coefficient showed that Cronbach Alpha for all variables under revalidation and this met Nunally’s (1978) suggestion of 0.50 or above criterion. The reliability measures were justified using the works of Goode and Hatt (1952) and Zikmund (1994). Three major categories of reliability test were carried out to ensure the reliability of the instrument. These include test-retest, equivalent form, and internal consistency. Each of these reliability test measures consistency a bit differently. For instance, test-retest measures consistency from one time to the next. Equivalent-form measures consistency between two versions of an instrument. Internal-consistency measures consistency within the instrument (consistency among the questions).
(i) To ensure test-retest, the instrument was given the second time to the same group of respondents, reliability was confirmed through the correlation between the scores on the two independent instruments. The purpose of test-retest reliability is to determine the period of time to wait between the two administrations. In fact, we waited long enough to ensure that the subjects do not remember how they responded the first time they completed the instrument and also ensure that it was not too long a time to influence change in the knowledge of the material being measured. In fact the test-retest was carried out within one month interval. This was calculated using Crobanch Alpha with Statiscal Package for Social Sciences (SPSS) and the result yielded r = 0.67
(ii) To ensure equivalent-form (parallel or alternate-form) validity, two different versions of the instrument were created. Apart from administering the instrument to the women entrepreneurs, the same instrument was administered to some men entrepreneurs. The researchers assumed that the two instruments measure the same thing. The respondents completed the instruments during the same time period. The scores on the two instruments were correlated to calculate the consistency between the two forms of instruments and the result yielded r=0.64 using Cronbach Alpha with SPSS.
(iii) The internal-consistency of the instrument or split half method was also used. The total score for the odd number statements was correlated with the total score for the even number statements. The Spearman-Brown Prophecy Formula was applied to the correlation to determine the reliability. Cronbach's Alpha was equally used because the items on the instrument were not scored as “right versus wrong”. Cronbach's alpha is often used to measure the internal consistency. This was calculated with SPSS and the result yielded r= 0.70
3.13 Method of Data Analysis
Data collected were analyzed with both manual and electronics based methods using a data preparation grid and SPSS. The utilization of structured grids allowed specific responses to be located with relative ease and facilitate the identification of emerging patterns (Munn and Drever, 1990). Descriptive, statistical and content analyses were used in analyzing the collected data (Asika, 2001; Osuagwu, 2002; Otokiti, Olateju and Adejumo, 2007). Using descriptive analysis we were able to calculate; the mean, frequency distribution and percentage analysis of the study. Statistically, the researcher was able to utlized the following statistical tools: Analysis of Variance (ANOVA), Chi-square, correlation coefficient and factor analysis in testing stated hypotheses. For example, (i) ANOVA: The Analysis of Variance was used in testing the hypothesis one. This enabled the researcher to analyze the degree of variance between two variables (independent and dependent variables) of the tabulated data. The total variance is partitioned into the variance which can be explained by the groups of independent variables (Between) and the variance which can be explained by all the units of the independent variables (Within) and the Sums of Squares for the Between and Within add up to the Total, reflecting the fact that the Total is partitioned into Between and Within variance. Sums of Squares are usually associated with the three sources of variance, Total, Between and Within. Degree of freedom is associated with the sources of variance. The total variance has N-1 degree(s) of freedom. The between degree of freedom corresponds to the number of groups minus 1 (K-1). In this case, it is 4-1 (since there were 4 independent variables). The Within degrees of freedom is the ‘df total’ minus the ‘df between’. Mean Square is the Sum of Squares divided by their respective ‘df’. These are computed so as to find the F-ratio, dividing the Mean Square between by the Mean Square within to test the significance of the independent variables on dependent variables.
(ii) Similarly, Chi-square was considered appropriate for the analysis of the study. This became necessary for multinomial probability in which the sample size of the study was randomly selected to establish the relationship between women motivational factors and their performance in business. This was used in analyzing hypothsis two. (iii) Coefficient correlation which measures the relationship between two variables was used in testing hypotheses one, two, three, four and five. The Pearson Product-Moment Correlation Coefficient (r) is a measure of the degree of linear relationship between two variables, usually labeled independent and dependent. In correlation, the emphasis is on the degree to which a linear model may describe the relationship between two variables and the interest is non-directional, the relationship is the critical aspect. The coefficient of correlation can vary from positive one (indicating a perfect positive relationship) through zero (indicating the absence of a relationship) to negative one (indicating a perfect negative relationship).
Motivation and variables such as business performance, type of business ownership, challenges women face in business and environmental factors were tested using the correlation analysis. (iv) Factor analysis was also used to reduce the volume of the questions in the questionnaire into a smaller unit for easy usage in the analysis. This technique requires a large sample size before their stabilility can be managed. This is based on the report of Tabachnick and Fidell (2001). Factor analysis was used to reduce the factors motivating women entrepreneurs into four (social, psychological, financial and environmental). Factor analysis is a method of data reduction. It does this by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables). There are many different methods that can be used to conduct a Factor Analysis (such as principal axis factor, maximum likelihood, generalized least squares and unweighted least squares). There are also many different types of rotations that can be done after the initial extraction of factors, including orthogonal rotations, such as varimax and equimax, which impose the restriction that the factors cannot be correlated, and oblique rotations, such as promax, which allow the factors to be correlated with one another.
This study also adopted the usage of the Lorenz Curve to determine the degree of concentration and diversification in the spread of entrepreneurship. This technique was propounded by Lorenz (1905). It was used in economics and ecology to describe inequality in wealth distribution (Kotz et al, 1983). It can also be used to determine the nature or size of industrial concentration and diversification (Otokiti, 2005). The Lorenz Curve functioned as the cumulative proportion of ordered individuals mapped into the corresponding cumulative proportion of their size. Through its graphical representation of the proportionality of a distribution (the cumulative percentage of the values) all the elements of a distribution were ordered from the most important to the least important. Then, each element plotted according to their cumulative percentage in a graph of X and Y, X being the cumulative percentage of elements and Y being their cumulative importance. In this study, Lorenz Curve was used to determine the concentration or otherwise of women entrepreneurs in the three states used as the case study of this research work.
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CHAPTER FOUR DATA PRESENTATION, ANALYSIS AND INTERPRETATION OF RESULTS 4.0 Introduction
The primary purpose of this study is to examine the different motivational patterns that exist among women entrepreneurs in SMEs across different industrial sectors in the Nigerian economy with regard to starting and developing their own businesses. The secondary purpose is to examine the relationship between motivation and the performance of women entrepreneurs, the challenges they face in their businesses, their type of business ownership and environmental factors. The findings of the hypotheses tested in this study are discussed
This chapter begins with the information on the survey results and the description of the respondents' demographic information. The descriptive analyses of the variables used in this study were also presented. This was followed closely by the testing of the hypotheses formulated for this study and presented in the order of the hypotheses. Each hypothesis focused on the variables of the research with (motivational patterns as independent variables and women entrepreneurs as dependent variable). The analysis of the hypotheses was carried out based on the statistical tools adopted. The researcher’s position in this study was clearly stated under result presentation and discussion. These views were within the theoretical framework of this study.
4.1 Survey Results
Survey Results of this study were analyzed using SPSS 12 (SPSS, Inc., 2003) statistical program. Frequency distributions mean and standard deviation were developed and based on the respondent’s responses for each item as regards to the demographic data, data on the business, challenges facing women entrepreneurs and other aspects relating to information on the research questions. The results of the survey are shown below.
4.2 Descriptive Analysis of Variables
This section presents the descriptive analysis of the variables used in this study. All the variables selected and tested as independent and dependent were described in the tables
Entrepreneurial Sector and Types of religions
It was shown that majority of the businesses owned by women entrepreneurs are in the distribution as 127(30%) in agricultural, 122(29%) in trade, 118(28%) in service and 55(13%) in manufacturing sector The study also revealed that out of these, 259 (61%) are Christians and 157 (37%) are Moslems while 6 (2%) of the respondents do not belong to any religion, or cannot be associated with either Christian or Moslem.
Table 38: Descriptive Statistics of Entrepreneurs by Sectors and Religions n=422,
Religions of the Respondents
Source: Field Survey, 2007 Structure of Respondents’ Business
The structure of business covered by the survey of the study shows that 382(91%) are sole trade business, 27(6%) are partnership business, 3(0.7%) belong to the category of company while 10(2.4%) are under cooperative society.
Table 39: Descriptive Statistics of Entrepreneurs by Structure of their businesses.
Structure of Businesses
Joint Stock Company
Others eg. Cooperative Society etc.
Source: Field Survey, 2007
Number of Dependant Relatives and Age of the Respondents
Looking at the number of dependents of the respondents, the survey revealed that 136(32.2%) have one dependant, 171(40.5%) have two dependants, 37 (8.7%) have three dependants, 34(8%) have 4 dependants while 44(10.6%) have five dependants and above. Majority of the women entrepreneurs, 174 (41.23%) interviewed were between the ages of 31-35. This was followed by the age range of 21-26 which is 155 (36.72%). It was observed that few of them are either too young between ages 15 and 20 (4.26%) or too old between the ages of 40 and above 75 (17.78%).
Table 40:Descriptive Statistics of Entrepreneurs by Number of Dependants and Age
No of dependants of the Respondents
Age of the Respondents
No of dependants
5 and above
Source: Field Survey, 2007 State of Origin and Ethnic Background of the Respondents
Table 41 revealed that 138(32.7%) of the women entrepreneurs under the study are from Lagos State, 134(31.7%) of them are from Ogun State and 150 (35.6%) of them are from Oyo State. The ethnic background of those surveyed was Yoruba 388 (92%), Ibo 22(5%), Hausa 10 (2.0%), and minority 2(0.5%).
Table 41: Descriptive Statistics of Entrepreneurs by Tribe and State of Origin
State of Origin of the Resp.
Tribe of the Respondents
State of origin
Source: Field Survey, 2007
Age of Starting the Business and Educational Background
Table 42 was designed to capture the statistics on age of establishment and educational background of the respondents. Few of the respondents 18(4%) were at the age range of 15-20 when they started business, 155(37 %) were at the age range of 21-26 when they started their business, 174 (41%) were at the age range of 31-35 when they started their business while 51(12%) were between the age of 40 and 45 when their business started while 24(6%) of them were 46 old and above when they started their business. Considering the respondents educational qualification, majority of them 344 (82%) have WASE, 54(13%) of them are with OND certificates, 15 (4%) are with HND/BSc certificates while only 4 (1%) obtained MSc status in their certification and only 2(0.5%) have other certificates which was not actually specified.
Table 42: Descriptive Statistics of Entrepreneurs by Education and the Age they Started Business
Age of Business
Education Background of the Respondents
Age of Starting Business
Highest education qualification
Source: Field Survey, 2007 Marital Status and Number of Children of the Respondents
Out of the 422 respondents, it was observed that 263 (62%) were married while 146 (35%) are still single, 7 (3%) of them are divorced while 6(2%) of them are widow. Two hundred and two or 48% of them have two children, 102 (24 %) of them have three children, 48 (11%) had 4 children while 24 (6%) of them have five dependants and above.
Table 43: Descriptive Statistics of Entrepreneurs by Marital Status and Number of Children
The majority of the women entrepreneurs surveyed had prior experience in their fields of endeavour. For example, out of the 422 women entrepreneurs, 10(2.4%) of the respondents had less than one year working experience, 48 (11.3%) of the respondents has one year working experience, 202 (47.8%) of them worked two years, 136 (32.2%) had working experience of three years, while 20(4.7%) and 6(1.6%) had a working experience of between four and five years respectively before they started their business. In other words, out of the women that owned business, more than half of them had related prior experience in their kind of trade. The survey also revealed that 20(4.74%) of the respondents established their business in less than one year ago, 130(30.80%) of the women under the study started their business in the last one year, 66 (15.64%) of them started their business in the last two years, 45 (10.66%) of them started their business in the last three years, 36 (8.53%) started their business in the last four years while 25 (29.6%) of them started their business in the last six years and above, 100 (23.70%) of them started their business in the last six years and above
Table 44: Descriptive Statistics of Respondents by Length of Work Experience and