Methodology and Methods
4.10 Justifications for Using Statistical Analysis Tools
This section explains the various methodologies which were incorporated in the analysis of the survey. Data from the completed questionnaires were analysed using the Statistical Package for the Social Sciences SPSS (version 19.0 for Windows).
The three main techniques used for the analysis were: descriptive statistical analysis, cross-tabulation matrices and regression and multi-regression analysis. In addition, reliability and correlational techniques were applied to investigate the consistency of each multiple scale and to examine the association between all the indices to be created later on. However, I carried out many other tests; I initially attempted to perform T-tests in order to compare groups (e.g.
age, gender, educational level). However, after testing for normality and equality for
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variances it became clear that I could not satisfy the key assumption required for performing T-tests. Hence, the alternative was a non-parametric test [Mann-Whitney test], which was carried out. The Mann-Whitney test was performed to test selected groups, in order to compare the key dependent variables. The results indicated that most of the groups compared differed with a small effect size; therefore, I did not concentrate on these, as there was a lack of statistical significance.
Prior to performing different techniques of analysis, it was necessary to identify the independent variables and dependent variables. The independent variables which were important for understanding DVAW in this sample, were: gender, age, educational level, length of residence in the UK, marital status, and whether the respondents came from a small town or a large city in Libya. The dependent variables discussed in the thesis were selected from the items on the questionnaire. These related to definitions of DVAW, beliefs about causes of DVAW, justifications for DVAW and the impact of migration on attitudes towards DVAW. It is important to note that the above variables were re-coded to smaller categories excluding gender (as `gender` only contained the category `male-female`). Likertscales were re-coded into two categories: `agree` and `disagree` whilst the category `neither agree nor disagree` was re-coded as missing. Many scales in this survey are Likert scales, where the statement which the respondent is asked to evaluate according to any kind of subjective or objective criteria; generally the level of agreement or disagreement is measured.
It was also necessary to create composite scales or indices which compressed sub-questions into one single section, in order to identify factors which could influence attitudes towards DVAW. The questionnaire included multiple items for measuring key concepts, including
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multiple-item scales on `Definition of violence`, `Behaviour considered to be DVAW`,
`Economic and social elements of DVAW`, `Educational and cultural reasons for violence`, and `Justifications for DVAW`. Each index was changed into a single variable from a group of sub-questions. The indices were created for items/sub-questions by first investigating the consistency of each multiple scale item through the use of reliability analysis. A composite measure was created by replacing the scale with the mean of its components and the resulting indices were used in the descriptive analysis, cross tabulations, and to perform multiple regression analysis.
Cronbach’s alpha is the most common measure of internal consistency or `reliability`; that is, how closely related a set of items are as a group. It provides an overall reliability coefficient for a set of variables (e.g. questions). This measure most commonly used when data have multiple `Likert questions` in a survey which form a scale and when it is to be determined if the scale is reliable. Performing reliability analysis using SPSS provided the value for Cronbach’s alpha. This provided an overall reliability coefficient for the set of the sub-questions in the questionnaire; values of 0.7-0.8 are usually regarded as satisfactory.
The scale used for `definition of DVAW` in my survey included five items or questions 19 which yielded a Cronbach’s alpha of .772, which indicated a high level of internal
19 Five sub-question/ items were included in the index `Definitions of DVAW` are:
i. DVAW includes mental cruelty including verbal abuse.
ii. DVAW includes deprived of money, and clothing.
iii. DVAW includes being threatened with force or violence, even though no actual physical violence occurs.
iv. DVAW includes physical violence that results in actual bodily harm.
v. DVAW includes sexual violence.
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consistency for this scale in the sample. A composite `index of definition of DVAW` was created by replacing all the individual questions with the mean score of all the individual questions by computing variables using the SPSS dataset. The scale/index of `Behaviour considered to be DVAW` was created from nine questions20. The Cronbach’s alpha for the nine items was .847, suggesting that the items had a high internal consistency. The index was then created using the same method discussed above. The scale/index of `Economic reasons for DVAW` included six sub-questions21 with a Cronbach’s alpha of .744, which indicated a high level of internal consistency in the scale. A scale measuring `Educational and cultural reasons for DVAW` included four items22 with a Cronbach’s alpha of .654, which suggested that the items had slightly low internal consistency, and an index was created. Finally, the
20 Nine sub-question/ items were included in the index ` Behaviours considered to be DVAW `:These are:
i. The husband denies his wife access to household money.
ii. The husband forbids the wife to leave the house alone.
iii. The husband shouts at the wife.
iv. The husband curses the wife.
v. The husband pulls or pushes his wife.
vi. The husband slaps the wife.
vii. The husband punches his wife.
viii. The husband breaks things in the house.
ix. Forced marriage is a type of DVAW.
21 Six sub-question/ items were included in the index ` Economic reasons for DVAW ` these are:
i. Women who earn more than men are more likely to become victims of domestic violence.
ii. Women who keep on demanding money from men are likely to become victim of domestic violence.
iii. Women depending on men for food, shelter and other material things are more likely to become victims of domestic violence.
iv. Unemployed men tend to get frustrated and depressed, which leads to domestic violence.
v. The lack of resources (e.g. house, money etc.) increases DVAW
vi. Situations where women do not wish to work or do not give their earning to men can cause DVAW
22 Four sub-question/ items were included in the index ` Educational and cultural reasons for DVAW ` these are:
i. Lack of education of women is a cause of violence against them.
ii. Misinterpretation of religious texts in which men have rights to use DVAW in order to correct women.
iii. Low educational level of men leads to domestic violence.
iv. The high level of women’s education can lead to violence against them.
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scale measuring `Justifications for using DVAW` included a nine items scale with a Cronbach’s alpha of .799. Subsequently, the index was created.
Further analyses were performed to examine the association between all the indices created to understand whether there existed any association between them. The associations were performed using Pearson’s correlation23. The analyses undertaken using SPSS indicated a significant association between the indices. For example, the index of `Behaviour considered to be DVAW` and index of `Definition of DVAW` were strongly related to one another (r=.573, 𝑅2 = 32.8 ) suggesting that 32.8% of variability in the definition of DVAW can be explained as behaviour considered to be DVAW. In other words, the two indices have a similar variance or spread. (See Figures 4.1 and Table 4.1)
23The Pearson product-moment correlation coefficient is a measure of the strength of the linear relationship between two variables. It is referred to as Pearson's correlation or as the correlation coefficient. If the relationship between the variables is not linear, then the correlation coefficient does not adequately represent the strength of the relationship between the variables. The symbol for Pearson's correlation is `r`. Pearson's r can range from -1 to 1. An r of -1 indicates a perfect negative linear relationship between variables, an r of 0 indicates no linear relationship between variables, and an r of 1 indicates a perfect positive linear relationship between variables.
For example, when r is .561, this means there is a positive correlation between two indices (Power and Xie, 2008).
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Figure 4. 2 Correlation of Indices
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**. Correlation is significant at the 0.01 level (2-tailed).
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After undertaking the reliability analysis tests, which indicated that the items in the questionnaire were related to one another, creation of the overall indices of the repeatability or internal consistency of the scale as a whole was performed. The survey data were then analysed.
Three types of statistical analyses were performed to assess the study’s results, comprising:
(a) descriptive statistical analysis; (b) Cross-tabulation matrices and Chi-square tests; (c) regression and multi-regression analyses. Firstly, the descriptive statistics were calculated.
The data was analysed using the `frequencies procedure` for describing many types of variables. The frequencies procedures were calculated to reflect the relative frequency of attitudes towards DVAW reported by the women and men in the sample. Categorical variables24 (Information that can be sorted into categories) were expressed as frequencies and percentages. The frequency distribution of each variable was assessed and presented by the gender of the respondents, then compared with their educational levels. The frequency distributions of the categories reflected in the responses to each statement for the questions are presented in the results. In addition, I presented the frequency distribution of the participants’ responses to the statements comprised together in the `indices’ (see later discussion).
24Categorical variables record a response as a set of categories. These variables have values that describe a 'quality' or 'characteristic' of a data unit, such as ‘what type' or 'which category. Such variables are further described as: Nominal, Ordinal, and Interval. Nominal variables have categories that have no natural order to them. Ordinal variables, on the other hand, do have a natural order. Examples of these could be pesticide levels:
high, medium, and low or an injury scale: 0, 1, 2, 3, 4, and 5. Caution should be used with some tests designed for ordinal variables because they may assume equal 'distance' between the levels. Such distances may be hard to actually quantify. The last type is the interval variable and it is, as the name implies, created from intervals on a continuous scale (Power and Xi, 2008).
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Secondly, cross-tabulation analyses were conducted. Associations of attitudes towards DVAW with socio-demographic variables were examined. Relationships between variables were presented in tables. The data were initially analysed using a Chi-Square tests25 to examine the associations between two categorical variables. This tested the relationship between each factor of attitudes towards DVAW and the socio-demographic characteristics.
For Chi- Square tests, the critical level for statistical significance was at the 5% level. If the result was considerably higher than that, then the hypothesis was rejected. It would, in such a case, be concluded that there was no relationship between the independent variables and dependent variables. These derive from the part of the SPSS output labelled `sig`26. The p-value is a number that ranges between 0 and 1. The p-p-value is the probability of obtaining the result, assuming that the two groups compared are the same. Generally, if the p-value is less than 0.05, the difference observed is considered statistically significant.
Additionally in this part of the analysis, the indices were re-coded into three categories:
`agreement with the statements`, `neutral` and `disagreement`. Further hypotheses were tested to explore the relationship between gender and the indices 27(as discussed above). Variables, that of `education, length of residence in the UK, original place of residence in Libya, age,
25The chi-square statistical test studies the relationship between two categorical variables. The association between two categorical variables is assessed by creating a table of all the possible combinations of responses of the two different variables (Power and Xi, 2008).
26 Usually, interest is in whether this value is above or below `p<.05`. The p-value is a number that ranges between 0 and 1. The p-value is the probability of getting the result, assuming that the two groups compared are the same. Generally, if the p-value is less than 0.05, the difference observed is considered statistically significant.
27 The indices are: `Behaviours which are included in the term domestic violence`, `Definitions of DVAW`,
`economic reasons for DVAW`, `Educational reasons for DVAW` and `Justifications for DVAW`.
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marital status and having children` were introduced as controls to explore their impact upon the bivariate relationships for gender as independent variable and each of these indices were used in order to examine whether there were differences between males and females, linked with their level of education, their age for instance.
Third, after conducting the Chi square analyses, it was necessary to extend the analysis beyond single measures of a variable. Regression and multiple regression28 analysis was conducted for each of the DVAW indices, to examine the extent to which the independent variables/socio-demographic characteristics predicted attitudes towards and perception of DVAW. Multiple regression allows additional factors to be entered separately into the analysis and the effect of each can be estimated. It is valuable for quantifying the impact of various simultaneous influences upon a single dependent variable. Because of omitted variables bias with simple regression, multiple regression is often essential even when the investigator is only interested in the effects of one of the independent variables (Sykes, 1993).
Several other studies of DVAW have conducted regression and multiple regression analyses to determine the extent to which the aforementioned dependent-criterion variables can be predicted by socio-demographic characteristics (Haj-Yahia, 1995, 1998, 2000; Straus, 1990;
Straus et.al, 1996).
28Multiple regression is a flexible method of data analysis that may be appropriate whenever a quantitative variable (the dependent variable) is to be examined in relationship to any other factors (expressed as independent or predictor variables). Relationships may be nonlinear, independent variables may be quantitative or qualitative, and one can examine the effects of a single variable or multiple variables with or without the effects of other variables taken into account (Cohen et al., 2003).
152 4.11 Conclusion
This chapter has discussed the research methods and methodological issues, including the considerations that influenced the design and implementation of the research. It has attempted to justify and to reflect upon the research methodology. It also explored factors that influenced the choices and decisions I made during the stages of fieldwork and analysis of data.
Feminist methodology as used in this research has been advanced as a legitimate research model (Roberts, 1981). It considers gender to be a basic organising principle, which deeply shapes the situations of lives. Feminism can be seen as a form of attention, a lens that brings into focus particular questions about gender and how it affects our lives (Blair and Holland, 1985: 394).
This study employs a mixed method approach to achieve the aims of the research. The use of questionnaires offered a practical means of obtaining data from 175 participants, providing a quantitative perspective in an otherwise interpretive study. The semi-structured interviews allowed for exploration of questionnaire findings and further encouraged the participants to express personal experiences.
The following chapters discuss the analysis of data from this research project.
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