Assessment of motivational patterns of women entrepreneurs in ngo sector and their impact on economic development case Study

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

below.

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.

Sectors_and_Religions_n=422'>Table 38: Descriptive Statistics of Entrepreneurs by Sectors and Religions n=422,

	Sectors		Religions of the Respondents
Sectors	Frequency	Percentage (%)	Religions	Frequency	Percentage (%)
Agriculture	127	30	Islam	157	37.2
Manufacturing	55	13	Christianity	259	61.3
Trade	122	29	Others	6	1.5
Service	118	28
Total	422	100	Total	422	100