Quantitative Research

The two main research models of qualitative and quantitative research are separated by numerical and non-numerical data which means that one (quantitative) uses numbers to measure while qualitative refers to statements (Babbie, 2007). For example, stating a person’s weight as a quantitative figure such as he weighs 120 kilos versus qualitatively saying he looks over-weight. The first instance uses a specific measure transforming words into numbers, with the latter expressing a viewpoint (Babbie, 2007; May, 1997).

Quantitative social research is positivistic in nature and based on the belief that society can be studied scientifically in an ordered manner (Babbie, 2007). It tests and verifies theories based on available data then theory is ‘deduced’ or removed from the data and

the researcher seeks a truth or an explanation. Quantitative methods build a traditional, structured model which explores relationships and the effects between variables then links them back to general theory (Miller & Brewer, 2003). It attempts to explain the relationship between variables with standards or measures. In this method the

researcher remains neutral and objective towards a sample.

Quantitative methods are based on the scientific method, conducted in an ordered manner using numbers, giving a sense of precision and exactness. This makes them highly regarded and accepted in research, especially in the social sciences (Berg, 2007). It uses statistical analysis to answer research questions or to test hypotheses based on attitudes or behaviours and is able to test theory through experiments using research tools and statistics (Creswell, 2003). In testing theory the researcher measures

phenomena or concepts converting them into numbers or indicators. In the analysis the researcher tests for reliability (how dependable the test is for repeatability), and validity, (is the test actually measuring what it purports to do?) (Thomas & Nelson, 2001).

When employing quantitative methods the collection of data is done in a number of ways: some researchers conduct experiments in laboratories; others use pre-existing data sets such as the participation rates of women in sport, or social capital in safe communities; some use surveys; and others use content analysis or analyse books or articles (Miller & Brewer, 2003).

Data is collected then prepared for analysis by checking for clarity and accuracy, with unclear information edited and missing information accepted or rejected (Denscombe, 1998). Data is coded and converted from text to an agreed numbering system, then entered and grouped in categories for statistical analysis (Neuman, 2012; Saratankos, 2005).

Statistical analysis provides frequency tables (how often something occurs), the mid points or measures of central tendency including mean or average, median (midpoint) and the mode or most common value (Denscombe, 1998). This analysis also gives the range or spread of the data responses, standard deviation or spread related to the mean,

associations or links between the data sets, and levels referring to differences between data (Denscombe, 1998; Neuman, 2012).

3.3.1 Statistical Analysis

Multivariate analysis allows for analysis of the relationships among a number of variables. It includes a number of statistical techniques that are used in the social sciences to measure attitudes, behaviours and relationships between items. One of these techniques is factor analysis.

Factor analysis is a mathematical technique which allows for a reduction in a data set by analysing relationships between variables and grouping them under headings, or factors (Thomas & Nelson, 2001). It assigns a weight to each item in a scale and through repeated analysis reducing items belonging to that factor. The theory is based on manipulation, using statistics and relationships between indicators to show a factor that is related to all indicators.

Exploratory factor analysis is descriptive and shows the number of factors required to represent data, and which variables influence each other. It uses tables (factor matrix) to illustrate the relationship between variables and factors and provides a score (factor score) for each figure in the sample (Miller & Brewer, 2003). Therefore, conducting an exploratory factor analysis can reduce data (data reduction), and allow the researcher to analyse any new emerging factors. It can also be used to check validity for items in a scale, confirming if the items measured make up a valid scale and are working together. Factor analysis therefore, is useful in understanding data and interpreting the

relationships contained within clusters of variables. It is applied in social research, especially in relationship to the measurement of behaviour and attitudes through scales (Onyx & Bullen, 2000).

Correlation is another statistical technique which is used to denote or explain the relationship between variables. It may involve two or more variables and can be described as the degree of the relationship that exists between a person’s performance on a half marathon and cardiovascular health. Factor analysis can use correlation with a

number of variables to highlight the relationships between the various variables referred to as factors (Thomas, Nelson & Silverman, 2011). Correlations are positive where a small score in one variable results in a small score with another or when one increases with the other at the same rate in the same direction. Negative correlations are when a small increase in one variable results in a large increase in another. The correlation coefficient is a numerical value showing the relationship between the variables and can be positive or negative and ranges from .00 to 1.00. A perfect correlation is 1.00 and no relationship has a score of .00 (Thomas, Nelson & Silverman, 2011).

Analysis of variance (ANOVA), refers to tests that allow for testing of the null

hypothesis between group means by noting the difference between groups. One way, or simple ANOVA, predicts the strength of the relationship between two or more variables by evaluating the null hypothesis. It calculates the score of the group’s level of the independent variable (Thomas, Nelson & Silverman, 2011).

MANOVA is an extension of analysis of variance (ANOVA) where the researcher can examine more than one dependent variable. It is a generalised form of univariate ANOVA used when there are two or more dependent variables (Stevens, 2002).

MANOVA helps to answer:

1. do changes in the independent variable(s) have significant effects on the dependent variables?

2. what are the interactions among the dependent variables? and

3. what are the interactions among the independent variables?

3.3.2 Use of Quantitative Methods in Leisure and Social Capital Research

In reviewing the literature it was noted that the use of quantitative methods in research was prevalent in both leisure research and research on social capital. Much of this was in the field of social psychology where a quantitative focus was important to highlight large numbers of responses which indicated significance in testing theory (Iso-Ahola, 1980b; Wakefield & Sloan, 1995; Wann & Hamlet, 1995). Use of figures is

exemplified by the Australian Bureau of Statistics annual survey on participation rates in sport and physical activity (ABS, 2006). The figures express actual participant numbers and percentages and are easy to understand. Quantitative researchers use the logic of science, measuring behaviour and showing how often something is done (Horna, 1994; Nau, 1995). This allows for flexibility in data analysis and can include checking validity and reliability. It can produce objective data such as analysis of sports fans’ attitudes (Branscombe & Wann, 1991; Madrigal 1995; Murrell & Deitz, 1992). This research compared fan attendance, satisfaction, and evaluation of team performance (Madrigal, 1995; Wann & Dolan, 1994). It developed and employed the Sport Spectator Identification Scale which measured significance in validity and reliability (Wann & Branscombe, 1993).

Development and measurement of variables is a significant feature of quantitative research and enables it to test social behaviour through scale measurement (Neuman, 2012). It has been used in research in social capital by Brown (2007), Onyx and Bullen (2001), Narayan and Deepa (2001), Okayasu, Kawahara, and Nogawa (2010), Stone and Hughes (2002), and Sabatini (2006). These researchers used scale items to measure levels of social capital with the employment of quantitative methods (e.g. multivariate analysis). Quantitative methods using a valid and reliable scale measuring a person’s attitude and beliefs in a theory such as social capital reduces the margin of error through analysis of their responses. Studies in social capital have used variations in this

manner, however there is currently no agreed scale of measurement across the field (Claridge, 2004). Social capital and leisure studies have been conducted, but the literature supports the need for further research (Brown, 2007; Hoye & Nicholson, 2008; Tonts, 2005; Zakus, et.al, 2009).

This discussion of quantitative methods highlights the importance of this dominant method in theory testing and development which is the main phase used in this research. The next section will discuss mixed methods and their use in social capital and in leisure research.