Quantitative Research

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Quantitative research is used to answer questions that have numerical value answers. Quantitative research is also used to establish cause and effect relationships  between variables.

Quantitative research designs

  • Randomised controlled trial – considered to be the best design to establish cause and effect relationships. Key features of a RCT include a treatment arm/group and a control arm/group.
  • Quasi-experimental – similar to RCTs with no randomisation.
  • Cohort studies – follow a predetermined sample group to measure the incidence of outcomes. The purpose of cohort studies is to link an exposure to an outcome. Purely observational with no intervention from the researcher.
  • Case control studies – the retrospective form of a cohort study. Individuals with the desired outcome are chosen, with the researcher attempting to discover the exposure that the outcome can be attributed to. Highly prone to recall bias.
  • Cross sectional studies – used to determine the prevalence of an outcome within a specific group. Often conducted using surveys, cross sectional studies are common in healthcare due to being cheap and easy to conduct.

Types of data collection within quantitative research

  • Biophysical
  • Pre-existing data
  • Observation of behaviour
  • Self-reporting

Strengths and limitations of quantitative research 

Strengths:

  • Data can be interpreted using statistical analysis
  • Can establish cause and effect relationships
  • Computer software available to analyse data – saves time and helps to minimise risk of human error
  • Easy to replicate and generalise

Limitations:

  • Do not reflect real life due to the high control applied.
  • Reductionist – simplifying complex situations into simpler versions
  • All confounding variables cannot be controlled
  • Lacks breadth within data

Terminology associated with quantitative research

  • Internal validity – whether the results are based on the intervention or an unknown variable.
  • External validity/Generalisability – how well what is being measured can be generalised to the wider population.
  • Confidence interval – usually expressed as a percentage. Represents how certain the researchers can be that the mean for the entire population would fall within the identified range.
  • Hypothesis – a theory or idea that needs to be tested.
  • P value – a measure of the strength of evidence against the null hypothesis. a small p value < 0.05 indicates evidence against the null hypothesis, this is then rejected and an alternative hypothesis developed.
  • Independent variable – the variable manipulated by the researcher to measure its effect on the dependent variable.
  • Dependent variable – what the researcher is interested in measuring in the study.
  • Confounding variable – an outside influence that can affect the results of a study.

 

Love,

T x

 

Qualitative Research

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Qualitative research aims to answer questions about individual beliefs, opinions and experiences. The data collected within qualitative research is in text form and is used to grasp emotions and attitudes – essential to person-centred nursing and healthcare in general.

Qualitative research designs

  • Ethnography – where researchers seek to understand a group experience, focusing on behaviours and norms within the selected group. Often used to study social relationships between humans. A key feature of ethnography is the long-term study of participants, with the researcher both observing and participating within the group.
  • Grounded Theory – where there is no available theory for the topic, the researcher attempts to create one. Grounded theory is used to generate new theories around practice and understanding within healthcare, making it one of the most popular forms of research methodologies used by nurse researchers.
  • Phenomenology – aims to understand the lived experience of individuals. Can be described in two ways: descriptive phenomenology (credited to Edmund Husserl) or interpretative phenomenology (credited to Husserl’s student, Martin Heidegger). There are slight differences between the two, you can read more about those here.
  • Case study – based on in-depth studies of an individual or group. Case study research is seen as highly flexible and often uses multiple methods of data collection.

Types of data collection within qualitative research

  • Interviews – can be structured, semi-structured or unstructured.
  • Focus groups
  • Observation
  • Diaries

Strengths and limitations of qualitative research 

Strengths:

  • Ability to explore the cultural and social aspects of living with an illness or disability.
  • Rich, detailed data is collected and analysed.
  • The structure of qualitative research data collection can be flexible, allowing the researcher to follow any tangents that arise within the study if needed.
  • Smaller sample sizes are used, possibly maintaining low financial input and being completed quickly in some cases.
  • Allows for greater understanding of patient care experiences.

Limitations:

  • People’s opinions and experiences are hard to replicate as they can differ over time and in different situations – due to this, findings are subjective and context bound, making them hard to transfer to other settings.
  • Lacks rigour and credibility due to focusing on individual beliefs and experiences.
  • Can be time consuming due to the amount of data collected and analysed.
  • Researcher/interviewer influence on the participant.
  • Results do not have any statistical representation.

Terminology associated with qualitative research

  • Credibility – representation of the truth.
  • Transferability – would the findings apply to another individual within the same context?
  • Dependability – if the research was conducted again, would you achieve the same results?
  • Confirmability – results are able to be traced back to the data collected.
  • Reflexivity – the questioning of one’s attitudes, values and prejudices and to appreciate how these could affect the outcome of the research.
  • Rigour – overall quality of the study ie strength of the research design, how well it fits the original aim etc.

 

Love,

T x

 

Research methods – common terminology

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Here are some of the most common terminology used that it would be useful to understand for your research module.

critical appraisal – examines the practical application of research, assessing how valid and relevant it is to the practice area.

intervention fidelity – how well an intervention is delivered as intended

generalisability – attempting to apply study findings to settings/contexts other than the ones they were originally tested in. Applies to quantitative research.

transferability – how findings can be transferred to another setting/context. Applies to qualitative research.

standard deviation – the spread of results occurring around the mean. For example, the mean age of participants may be 40 with a standard deviation of 25 – 55. Often represented as S.D. A smaller S.D is preferred as this shows a small spread of data around the mean, a large S.D shows a wide spread of data, meaning it is less reliable.

confidence interval – usually expressed as a percentage. Represents how certain the researchers can be that the mean for the entire population would fall within the identified range.

hypothesis – a theory or idea that needs to be tested.

null hypothesis – no significant difference apparent between two groups.

alternative hypothesis – results are the result of a difference between two groups.

p value – a measure of the strength of evidence against the null hypothesis. a small p value < 0.05 indicates evidence against the null hypothesis, this is then rejected and an alternative hypothesis developed.

quantitative – research where the results are numerical such as statistics, percentages etc. Studies cause and effect relationships.

qualitative – research where the results are text based and may follow themes. Includes thoughts, feelings, descriptions etc.

mixed method – where researchers use both quantitative and qualitative data within the same study.

rct – randomised control trial.

randomisation – making something random ie the allocation of participants into a treatment or control arm. A good way of minimising the risk of bias.

treatment arm – where participants receive the treatment/intervention. Characteristic of a rct.

control arm – participants receive no treatment/intervention or they receive a placebo. Characteristic of a rct.

internal validity – whether the results are based on the intervention or an unknown variable.

external validity – how well what is being measured can be generalised to the wider population.

independent variable – the variable manipulated by the researcher to measure its effect on the dependent variable.

dependent variable – what the researcher is interested in measuring in the study.

Reflexivity – the questioning of one’s attitudes, values and prejudices and to appreciate how these could affect the outcome of the research.

homogenous sample – when participants have similar or identical traits ie same age, gender, employment etc.

heterogeneous sample – where every participant has a different value for their characteristics ie different ages, gender etc. Indicative of diversity.

blinding – where participants or researchers are prevented from knowing which intervention group participants are allocated to. Can be single blinded or double blinded.

T-test – used to determine if there is a significant difference between the means of two groups.

bias – a form of error that can affect the outcome of studies.

triangulation – using more than one method to collect data. A way of assuring validity within the research.

primary research – new research studies, carried out through experiments, trials etc.

secondary research – analysis or interpretation of existing research studies.

cause and effect – where one event (the effect) is the result of another event happening (the cause). Randomised control trials are the best method able to establish a cause-effect relationship.