Introduction to Quantitative Research Methods, Exams of Mathematics

An overview of quantitative research methods, including the key concepts, types of research questions, and the research process. It covers topics such as the importance of quantitative data, the different types of variables, hypothesis testing, and the elements of reliability and validity in measurement. The document also includes examples of quantitative research questions and the scientific controversy around the 'tea example'. Overall, this document serves as a comprehensive introduction to the fundamental principles and applications of quantitative research methods, which are essential for understanding and conducting evidence-based research in various fields, including sports science, health, and social sciences.

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2024/2025

Available from 09/30/2024

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W6 - Introduction to
Quantitative Methods
Exam Questions and
Complete Solutions
Graded A+
systematic examination of phenomena through;
• Do this via testing a hypothesis
• development of statistical models to explain observable phenomena
• Nomothetic (generalizability) - taking findings and generalising what is being said
• Without an objective look at data, all we have is opinion
• Without reliable data, it's impossible to make evidence-based decisions - Answer: What is quantitative
data:
many people dont give proof . - you have to be really careful about what you read as not all supported. -
Answer: Why do we need research in sport?
• 2012 study explored evidence underpinning performance-enhancing claims for 104 different products
- Olympics 2012
• More than half (52.8%) of the websites that made performance claims did not provide any references,
and the authors were unable to perform critical appraisal for approximately half (72/146) of the
identified references
• Only 3 of the 74 (2.7%) studies were judged to be of high quality and at low risk of bias - Answer: proof
of this lack of research
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W6 - Introduction to

Quantitative Methods

Exam Questions and

Complete Solutions

Graded A+

systematic examination of phenomena through;

  • Do this via testing a hypothesis
  • development of statistical models to explain observable phenomena
  • Nomothetic (generalizability) - taking findings and generalising what is being said
  • Without an objective look at data, all we have is opinion
  • Without reliable data, it's impossible to make evidence-based decisions - Answer: What is quantitative data: many people dont give proof. - you have to be really careful about what you read as not all supported. - Answer: Why do we need research in sport?
  • 2012 study explored evidence underpinning performance-enhancing claims for 104 different products
  • Olympics 2012
  • More than half (52.8%) of the websites that made performance claims did not provide any references, and the authors were unable to perform critical appraisal for approximately half (72/146) of the identified references
  • Only 3 of the 74 (2.7%) studies were judged to be of high quality and at low risk of bias - Answer: proof of this lack of research

descriptive comparative relationship/casual - Answer: types of Quantitative research QUESTIONS

  • Wanting to understand a situation, facts
  • When you want to describe what is going on or what exists
  • Non-intervention - not doing something to somone (just asking questions) Example: What was the top sport by participation in England in 2018? Running - Answer: 1. Descriptive questions and examples
  • Two or more things are compared in the aim of finding something about one or all of them
  • None interventional
  • Trying to understand differences, not trying to order intervention though. Examples of comparative questions:
  • How common is racial abuse experienced by professional footballers compared to professional rugby union players?
  • What is the participation rate in organised sport of girls aged 12 15 years compared to boys of the‐ same age? - Answer: 2. Comparative
  1. Collect data in unambiguous and organised manner (try and stop problems of different units etc)
  2. Plot or tabulate data in an appropriate form
  3. Build a statistical model
  4. Analyse data using this model
  5. Report, in simple English, the answers and use graphs where appropriate to ease interpretation - Answer: The research process: o Working with human participants - lots of variability - trying to understand that so we can generalise to wider populations is important to us o Reduce our uncertainty and lack of understanding. o data collection and analysis: Fan engagement - how clubs can engage better through marketing- the amount of data collection and anysis. - Answer: Why do we need stats? Science that involves collecting, summarising, analysing and interpreting data. Wider term. - Answer: What are stats? A single number summarising a variable of interest. - Answer: What is A statistic? o To test a hypothesis o Are the results real? o Do the results matter? - Answer: Why do we undertake research using statistics? collection of facts or information - Answer: define data

characteristics associated with the group being studied - Answer: variable

  • Explanatory variable (Independent): What are you manipulating/ what you think is associated with outcome
  • Response variable (Dependent): Outcome variable - what are we seeking to change - Answer: Two types of variable and example Study into the difference in taste of tea if milk was added first or after - this can be tested as a hypothesis. 20 different cups, in different orders - the lady could actually tell the difference - Answer: Scientific controversy: 'tea example'
  • Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Example:
  • Null hypothesis (H0): There will be no difference in the taste of tea whether milk is added first or not
  • Alternative hypothesis (H1 or HA): There will be a difference in the taste of tea depending on whether the milk is added first - Answer: Hypothesis and null hypothesis for this example general statement stating that there are no relationship between two measured phenomena - Answer: null hypothesis a total set of observations that can be made. Population doesn't have to be person, its contextual of what you're studying. - Answer: population

Continuous (ratio) - Answer: Quantitative data types

  • Integer values (whole numbers)
  • E.g. time of day, temperature No meaningful zero value- Cannot multiply or divide - Answer: Discrete (interval)
  • Variables can take any value and start at zero
  • E.g. height, BMI, age, crowd, size Meaningful zero value Can multiple and divide. - Answer: Continuous (ratio)
  • Drives the research process
  • Should be considered from the moment research question is determined
  • Generally continuous outcomes need smaller samples
  • Must be appropriate to answer the research question
  • Guides the statistical analytic method - Answer: Why does data matter?
  • Stability - e.g. If you got on scale 3 times after and after it should be consistent
  • Internal reliability - want to know if the equipment is calibrated.
  • Inter-observer consistency - idea of people doing things differently - wouldn't be a way in which the researcher asks the question that will change the results of the data. - Answer: elements that come under Reliability in measurement: If you got on scale 3 times after and after it should be consistent - Answer: Stability want to know if the equipment is calibrated. - Answer: Internal reliability idea of people doing things differently - wouldn't be a way in which the researcher asks the question that will change the results of the data. - Answer: Inter-observer consistency

Internal validity: Manipulation or other causes (variables)? External validity Generalisability to a wider population? Comparability with other literature - Answer: Validity in measurement: (types)