Quantitative research is defined as social research that employs empirical methods and empirical statements, Cohen (1980). It is a formal, objective and systematic method in which numerical data is used to obtain information. It includes testing a hypothesis or trying to discover differences and relationships. It is conducted in a way so that the researcher would start from a hypothesis and then begin observations to prove the hypothesis. This is all carried out by counting and measuring things, producing in particular estimates or averages and differences between groups.
Moreover, Creswell (1994) has given a very summarising definition of quantitative research as a type of research that is explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics). Quantitative research is most common encountered as part of formal or conclusive research but is also sometimes used when conducting exploratory research. Quantitative research techniques are also part of primary research. Here are some of the advantages when carrying out quantitative research:
- Allows the research and description of social structures and processes that are not directly observable
- Appropriate for quantitative description and comparisons between groups, areas etc.
- Detailed and descriptive answers can be made due to the statistical analysis
- Can generally be repeated therefore making the research reliable
There are several data collection techniques which can be used for quantitative data collection. The main categories are surveys (questionnaires), experimental (tests) and observing figures recorded. The settings in which this data will be collected are either field or laboratory based. The questionnaire is a well-established tool within social science research for acquiring information on participant social characteristics, present and past behaviour, standards of behaviour or attitudes and their beliefs and reasons for action with respect to the topic under investigation, Bulmer (2004). Questionnaires are used when trying to collect a large amount of data from large groups and when the data you want to collect is not in-depth. Interviews are also considered to be a way of data collection in quantitative research, close end questions are also more likely to be used as opposed to open end. Research has also suggested that interviews, compared to questionnaires are more powerful in eliciting narrative data that allows researchers to investigate people’s views in greater depth, Kvale (1996; 2003).
Interviews – In person interview consists of an interviewer asking the respondent questions in a face-to-face situation. The interview may take place at the respondent’s home or a research office. In a structured interview, the researcher asks a standard set of questions and nothing more, Leedy and Ormrod (2001).
Telephone interviews – Are less time consuming and cheaper, the researcher has ready to anyone who has a telephone however this can also be a disadvantage. Also, the sample may be biased as only those people who have landline phones are contacted.
Computer Assisted Personal Interviewing (CAPI) – A form of personal interviewing, but instead of completing a questionnaire, the information is sent directly into the database from a computer. This method saves time involved in processing the data. However, this type of data collection method can be expensive to set up and requires IT skills.
All these types of interviews may have benefits when conducting research as Schostak, (2006) suggests that an interview is an extendable conversation between partners that aims at having an ‘in-depth information’ about a certain topic or subject, and through which a phenomenon could be interpreted in terms of the meanings interviewees bring to it. This means that if the interviewee is asking the right questions that relate to what they are trying to find out, in return they can gain the most accurate information.
In sport, interpreting quantitative data is vital and can allow athletes and teams to successfully understand their strengths and weaknesses allowing them to improve their performance. Recent studies have shown a significance in improved performance between its determinants (Ofoghi, Zeleznikow, MacMahon & Raab, 2013), it was able to interrogate athletes’ existing performance data to identify new strategies to further develop (Chen, Homma, Jin & Yan, 2007).
Quantitative data is the science of collecting, analysing and presenting data. It provides logical framework which enables objective evaluation of hypothesis and research questions. For example, ‘’why did that team dominate so much?’’ or ‘’which male football players have highest goals per game ratio?’’. The data is then plotted or tabulated in an appropriate form to provide a subjective answer to the question posed. Its regularly seen as then put into mathematical model to describe how the data gets generated using mean mode median and range. With this information it can aid teams and managers to decide the best possible outcomes like what tactics can be used and what players to include for team selection as research has suggested that information obtained from interpreting data can be used to help coaches predict changes in sports performance (Cangley, Passfield, Carter & Bailey, 2011).
Opta is an example of a company that delivers quantitative data to football in the premier league. Some examples of data it collects are things like, distance covered, completed passes, possession percentages and goals to game ratio. These statistics can be used by clubs to objectively look at how a player is performing during matches to help improve an area needing development. Prozone is also another example in sport that delivers and provides match performance analysis. The technology they possess offers real-time, post-match and opposition analysis and is used by clubs throughout the UK, USA, Europe and the Middle East. Advances in technology have allowed new methods of assessing movement patterns in football, including the multiple-camera method (Di Salvo et al., 2007). Also, according to Mackenzie and Cushion (2013), it has been suggested performance analysis has helped to identify long term constraints on an individual’s performance which led to improve player recruitment policies. This proves to show that it can benefit other clubs when trying to bring in new players as they are able to analyse statistics like passes completed or goals scored to get the best possible players.
Hypothesis: The more goals a team scores, the more points they will earn.
Null Hypothesis: Scoring more goals does not mean a team will earn more points.
When conducting the T-Test it returned a value of 0.240081, indicating there is not a close significant relationship between the two sets of data. This means the null hypothesis is valid. Pearson’s correlation produced a result of 0.735485 which would indicate a stronger significance.
As you can see from appendix A, it shows that the Pearson’s test suggests that there is a significance. When you consider the result of the t-test however, the result show that the two sets of data are not statistically significant, meaning that if this test was repeated then there is a good chance of you receiving a different set of data making it unreliable in this occasion. From the graph it also has shown that there is an impact between the relationship of goals scored and points gained. The data has allowed us to see that if more points are scored then the more points you are likely to obtain throughout the season as opposed to when you don’t score showing a matching significance.
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