research methods

Allocation Concealment:

P’s shouldn’t know which group they are going into to avoid bias results and treatment effects.

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Arm:

Refers to a group of participants allocated to a particular treatment. In a randomised controlled trial, allocation to different arms is determined by the randomisation procedure. Many controlled trials have two arms.

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Analytical observation

Quantify relationship between 2 factors to establish if there’s an association. Cohort/case control studies.

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Attrition:

The loss of participants during the course of a study. (Also called loss to follow up). Participants that are lost during the study are often called dropouts.

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Attrition bias:

Systematic differences between comparison groups in withdrawals or exclusions of participants from the results of a study. For example, participants may drop out of a study because of side effects. risk of over estimating the effectivness.

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Baseline characteristics:

Values of demographic, clinical and other variables collected for each participant at the beginning of a trial, before the intervention is administered.

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Bias:

A systematic error or deviation in results or inferences from the truth. In studies of the effects of health care, the main types of bias arise from systematic differences in the groups that are compared (selection bias)

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Bias prevention:

Aspects of the design or conduct of a study designed to prevent bias. For controlled trials, such aspects include randomisation, blinding, and concealment of allocation. Bias data: caused from attrition. Intention to treat applied.

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Blinding:

The process of preventing those involved in a trial from knowing to which comparison group a particular participant belongs. Participants, caregivers, outcome assessors and analysts are all candidates for being blinded. Not always possible however.

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Case-control studies:

Backwards studies, mostly retrospective. P’s usually have the condition. And compared to those without.

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Causal effect:

An association between two characteristics that can be demonstrated to be due to cause and effect, i.e. a change in one causes the change in the other. Causality can be demonstrated by experimental studies such as controlled trials.

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Coding/Categorizing data:

Qualitative design analysis method.

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Clinical trial:

An experiment to compare the effects of two or more healthcare interventions. Clinical trial is an umbrella term for a variety of designs of healthcare trials, including uncontrolled trials, controlled trials and randomised controlled trials.

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Cohort studies:

Forward looking in direction. P’s not yet developed outcome of interest so looking to see effects of exposure to risk. A comparison group is also observed not know to the risk.

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Concealment of allocation:

The process used to ensure that the person deciding to enter a participant into a randomised controlled trial does not know the comparison group into which the individual will be allocated. This is distinct from blinding, and is aimed at preventing s

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Confirmability:

(Qualitative research) Do ideas clearly link together. Looking at accuracy and clear meaning.

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Confidence Interval:

Estimates the precision of results and how confident sample is representative. A measure of uncertainty around findings. usually presented as a point estimate and a 95% confidence interval

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Confidence Level:

The specific probability of obtaining some result from a sample if it did not exist in the population as a whole.

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Confounder:

: A factor that is associated with both an intervention (and exposure) and the outcome of interest. Age is then said to be a confounder, or a confounding variable. Randomisation is used to minimize imbalances in confounding variables between experime

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Control group:

The arm that acts as a comparator for one or more experimental interventions. Controls may receive placebo, no treatment, standard treatment, or an active intervention, such as standard drug

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Controlled trial:

A clinical trial that has a control group. Such trials are not necessarily randomised.

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Credibility:

: (Qualitative research) internal validity. Believability of the data/ confidence you have in the truth of the findings.

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Data dredging

Performing many analyses on the data from a study, for example looking for associations among many variables.

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Dependability:

Qualitative research)-confirmability- strength of data over time/context.

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Detection bias:

Systematic difference between comparison groups in how outcomes are ascertained, diagnosed or verified (Also called ascertainment bias).

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Descriptive data:

Simplify/make sense of data analysis. Eg: mean, median & mode.

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Descriptive observation:

Provide insight into what is happening. Include case reports/surveys. Look at attitudes and beliefs. Describe situations over time.

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Dependent Variable:

measured for the effect.

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Effectiveness:

The extent to which a specific intervention, when used under ordinary circumstances does what it is intended to do.

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Estimate of effect

: The observed relationship between an intervention and an outcome expressed as, for example, a number needed to treat to benefit, odds ratio, risk difference, risk ratio, standardised mean difference, or weighted mean difference.

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Ethnography:

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Ethnomethodology:

A form of ethnography that studies activities of group members to see how they make sense of their surroundings

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External Validity

The extent to which the results of a study are generalizable or transferable.

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False negative:

A falsely drawn negative conclusion also called a type II error.

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False positive

A falsely drawn positive conclusion also called a type I error.

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Forest Plot:

Graphical representation of a meta-analysis.

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Funnel Plot:

Identifies publication/reporting bias. Commonly used in STRs.

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Grounded theory

Generates theory from data sources.

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Hawthorne effect:

Observer effect. Changing behaviour when aware being observed/measured/tested.

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Hypothesis:

An unproved theory that can be tested through research. To properly test a hypothesis, it should be pre-specified and clearly articulated, and the study to test it should be designed appropriately. 1 tailed predicts direction of change, 2 doesn't

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Hypothesis test:

A statistical procedure to determine whether to reject a null hypothesis on the basis of the observed data.

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Independent Variable

A variable that is part of the situation that exist from which originates the stimulus given to a dependent variable. Includes treatment, state of variable, such as age, size, weight, etc.

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Inductive Research:

Generates theory from observation (Qualitative design).

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Internal Validity:

The rigor with which the study was conducted (e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and wasn't measured) and (2) extent study has taken into account alternative explanations

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Interrater Reliability:

The extent to which two or more individuals agree. It addresses the consistency of the implementation of a rating system

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Inferential data:

test predictions, look at if certain occurrences occur more frequently, and identify any differences between groups.

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Intention to treat analysis:

strategy for analysing data from a RCT. All p's are included in the arm to which they are allocated, whether or not they received/completed the intervention given to that arm. prevents bias from drop out

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Interval Variable:

A variable in which both order of data points and distance between data points can be determined/equal distances, e.g., percentage scores and distances

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Matching:

Process of corresponding variables in experimental groups equally feature for feature.

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Mean:

An average value, calculated by adding all the observations and dividing by the number of observations.

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Measures of Central tendency:

Mean, median and mode.

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Median:

The value of the observation that comes half way when the observations are ranked in order.

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Meta- Analysis:

Statistical method to summarise results of a study.

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Multiple comparisons:

The performance of multiple analyses on the same data. Multiple statistical comparisons increase the probability of making a Type I error.

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Number needed to treat:

An estimate of how many people need to receive a treatment before one person would experience a beneficial outcome.

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Nominal Variable:

A variable determined by categories which cannot be ordered, e.g., gender and colour. Putting data into groups.

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Non-probable sampling:

Convenience, purposive, *********** and quota.

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Normal distribution:

A normal frequency distribution representing the probability that a majority of randomly selected members of a population will fall within the middle of the distribution. Represented by the bell curve.

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Observational research:

observe/describe events in a population. Forward looking studies begin after exposure and follow through to identify outcomes. Backwards looking studies know the outcome and look back to identify exposures.

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Ordinal Variable:

A variable in which the order of data points can be determined but not the distance between data points, e.g., letter grades or Likert scale.

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Odds:

A way of expressing the chance of an event, calculated by dividing the number of individuals in a sample who experienced the event by the number for whom it did not occur. Eg, if in a sample of 100, 20 died and 80 survived the odds= 20/80 or 0.25

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Odds ratio (OR):

The ratio of the odds of an event in one group to the odds of an event in another group.

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PICO:

Participant, Intervention, Comparison, Outcome.

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Placebo:

An inactive substance/procedure administered to a p, usually to compare its effects with those of a real drug or other intervention, but sometimes for the psychological benefit to the participant through a belief that s/he is receiving treatment

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Phenomenology:

A qualitative research approach concerned with understanding certain group behaviours from that group's point of view

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Point estimate:

The results (e.g. mean, odds ratio or risk ratio) obtained in a sample which are used as the best estimate of what is true for the relevant population from which the sample is taken.

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Power calculation

: Estimates the size of your sample mathematically so results are statistically accurate and large enough to show an effect.

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Pragmatic test:

Data must be normally distributed and interval or ratio data. More likely to find a type11 error. Non-pragmatic test: less powerful and more likely to find a type 1 error. Used when data isn’t normally distributed or data is ordinal.

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Primary outcome:

The outcome of greatest importance.

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Prospective observational study:

Occur before outcome has occurred.

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Power calculation

: Estimates the size of your sample mathematically so results are statistically accurate and large enough to show an effect.

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Pragmatic test:

Data must be normally distributed and interval or ratio data. More likely to find a type11 error. Non-pragmatic test: less powerful and more likely to find a type 1 error. Used when data isn’t normally distributed or data is ordinal.

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Primary outcome:

The outcome of greatest importance.

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Prospective observational study:

Occur before outcome has occurred.

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Probability sampling

: Random, systematic, cluster and stratified.

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P-value:

The probability (ranging from zero to one) that the results observed in a study could have occurred by chance if in reality the null hypothesis was true.

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Qualitative Research

explores relationships using textual data. Case study, observation, and ethnography. Results are not usually considered generalizable, but are often transferable. Interpretive approach to the world studying the natural setting.

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Quantitative Research:

researcher explores relationships using numeric data. Survey is generally considered.

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Random allocation

uses chance to assign participants to comparison groups e.g. random numbers table or a computer-generated random sequence. Random allocation implies that each individual or unit being entered into a trial has the same chance. Avoids selection bias

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Randomised controlled trial:

Experiment in which 2 or more interventions, possibly including a control intervention or no intervention, are compared by being randomly allocated to participants. TEST EFFECTIVNESS!! They must have a system of randomisation and control.

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Ratio scale data:

Equal distances with an exact 0 point. The numbers have meaning. Eg: age.

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Recall bias:

Arising from mistakes in recollecting events, both because of failures of memory, and looking at things ‘with hindsight’ and possibly changed views. People’s reports of what is happening to them currently, therefore, can be more accurate than

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Reliability:

Repeatability of the findings.

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Reporting bias:

caused by only a subset of all the relevant data being available. The publication of research can depend on the nature and direction of the study results. Studs in which an intervention is not found to be effective are sometimes not published.

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Rosenthal Effect:

observer-expectancy effect. Expectations of others causing bias.

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Standard deviation:

spread around the mean.

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Statistically significant:

A result that is unlikely to have happened by chance. The usual threshold for this judgement is that results or more extreme results would occur by chance with a probability of less than 0.05 if the null hypothesis was true. Statistical tests produce

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Stratified randomisation:

Ensure equal numbers of ps with a characteristic thought to affect prognosis or response to the intervention will be allocated to each comparison group. Eg, a trial of women with breast cancer, having similar numbers of pre/post menopausal

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Systematic Review

Lit review using systematic methods to create overview and statement of findings on objectives, materials, methodology… The quality of this is improved through having pre-specified outcomes, +1 reviewer, and a pre-determined extraction process

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Surveys

Establish an overview of a given issue in a group (questionnaires and interventions).

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Transferability:

(Qualitative research) ability to transfer findings to other groups- external validity.

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Triangulation:

Evidence from different sources. Strengthens validity. Looks to see if people come to the same conclusions. Aims for assertion- different kinds of evidence coming to the same conclusions.

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Validity:

Believability/credibility of the results. Internal: measuring what it proposes. External: generalisability.

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