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Quantitative Research

Involves the analysis of numeric data, employing statistical methods to draw conclusions and make inferences. This type of research focuses on variables and aims to measure and quantify relationships between variables, providing numerical insights.

Example of Quantitative Research

Studying the efficacy and safety of a specific medication in treating a particular medical condition compared to other treatments or placebo.

Example of Quantitative Research

Investigating the correlation between specific genetic markers and the risk of developing certain diseases or conditions, such as the BRCA gene and breast cancer risk.

Example of Quantitative Research

Analyzing the cost-effectiveness of a particular medical intervention or treatment, such as a new drug or surgical procedure.

Example of Quantitative Research

Assessing the impact of a specific public health intervention, such as a vaccination program, on disease incidence and prevalence.

Key Characteristics of Quantitative Research

Numeric - measurable, graphs, tables, etc.

Key Characteristics of Quantitative Research

Verifiable, accurate - value of the data is in the number.

Key Characteristics of Quantitative Research

Examples are sample size (n), mean, median, mode, range, standard deviation.

Qualitative Research

Involves the analysis of non-numeric data, describing observations, experiences, or phenomena. Qualitative research relies on methods such as interviews, observations, or content analysis to gather and interpret subjective information. The data is often categorized before summarizing, allowing for a deeper understanding of complex and context-specific phenomena.

Qualitative Research: Examples related to chronic illnesses

The experiences of patients living with chronic illnesses, such as cancer, diabetes, or mental health disorders.

Qualitative Research: Examples related to healthcare disparities

Investigate disparities in healthcare access, utilization, and outcomes among different population groups, such as racial or ethnic minorities, socioeconomically disadvantaged individuals, or rural communities.

Qualitative Research: Examples related to healthcare quality assessment

Study assesses the quality of healthcare services from the perspective of patients, caregivers, or healthcare providers.

Qualitative Research: Examples related to healthcare interventions

Evaluate the implementation and effectiveness of healthcare interventions, such as new treatments, programs, or policies.

Qualitative Research: Examples related to end-of-life care

Explore the experiences and preferences of patients, families, and healthcare providers regarding end-of-life care and decision-making.

Qualitative Research: Examples related to health behaviors

Investigate factors influencing health behaviors, such as diet, exercise, smoking, or medication adherence.

Nominal Variable

Involves names or categories. Examples includes various cancer types, different insurance companies, specialties offered

Ordinal Variable

Involves order of a category. For example, a pain scale, a stage of cancer, a trimester of pregnancy

Ordinal Variable

Type of variable where the order or ranking of values matters, but the differences between values may not be uniform. In these, the intervals between values are not consistent. An example is a pain scale where the order of pain severity matters, but the difference between a rank of "7" and "5" may not be the same as between "5" and "3".

Interval Variable

Difference between two values or numbers. For example, body temperature, height, weight, age etc

Ratio Variable

Defines rates or numbers. For example, UTI rates a nursing home, male female proportion of a disease, percentage of hospital readmissions etc

Independent Variable

Control. Doesn't change. It is manipulated or controlled in experiments to observe its impact on the outcome.

Dependent Variable

Experimental, changeable. The outcome or response being studied.

Confounding Variables

May obscure the effect of the variables in the data. They are often uncontrollable by the researcher.

Continuous Variable

Meaningful difference between values

Dichotomous Variable

Occurs in two possible states. For example; diabetic or non-diabetic

Randomized Control Trial

A scientific study design in which individuals are randomly assigned to receive one of several clinical interventions. The interventions include the experimental treatments and a control, which can be standard practice, placebo, or no intervention at all. Helps minimize bias and ensured that any observed effects are likely due to the treatment rather than the other

Demographic Statistic

Looking at a specific population or group. Example; women between 40-50 who live in Shelby, Montana

Focus Group

A demographically diverse group of people assembled to participate in a guided discussion about particular products or processes.

Case Study

A type of non-participant observation in which researchers investigate one person, one group, or one institution in depth.

The p-value

Helps to assess whether differences between the observed value and expected value represent chance

P-Value

Level of probability. The lower the value, the less probable the results occurred by chance.

Null hypothesis

Two groups being studied that are the same. It serves as the default assumption to be tested against the alternative hypothesis

Alternative Hypothesis

Asserts that the two groups being studied are different. It is the researcher's hypothesis, representing this model under consideration.

Sensitivity Analysis

The examination of how uncertainties in the output of a mathematical model or system can be attributed to various sources of uncertainty in its inputs.

Sensitivity Analysis

Examples of this is: When used in clinical trials, it is the increasing of a dose of a new drug in a small increment, to look for changes and strengthen any conclusions

Factor Analysis

Process in which the values of observed data are expressed as functions of a number of possible causes in order to find which are the most important.

Risk Stratification

Used to classify patients into level of risk

Stratification Analysis

The process of dividing a population into homogenous subgroups before sampling. Allows for the examination of specific characteristics within each subgroup, providing more nuanced insights into the overall population

Cross Sectional Research

Type of observational study. Analyzes data collected from a population or subset, at a specific time and point.

Pre and Post Test

Test before and after an intervention

Time Series Analysis

Methods for analyzing time series data in order to extract meaningful statistics and other data characteristics.

Longitudinal Study

Involves repeated observations of the same variables over long periods of time.

Regression Analysis

A statistical process used to estimate relationships among variables. It involves modeling and analyzing multiple variables, especially the relationship between a dependent and independent. Can include multiple predictors simultaneously to assess their combined impact on the dependent variable.

Predictive Modeling

A process used to identify patterns in data that can be leveraged to predict the likelihood of a particular outcome. Involves using current data to make forecasts about future events.

Cohort Study

Establishing Links between risk factors and health outcomes.

Prospective or Retrospective

Looking forward or backwards. Studies a cohort of individuals that share a common exposure factor to determine its influence on the development of disease, and are compared with another group of equivalent individuals that were not exposed to the factor

Literature Review

Analyzing articles written by experts

Systematic Review

Gather all available empirical research to obtain answers to a specific question

Meta-Analysis

A method of systematically combining qualitative and quantitative data from multiple studies to derive a single, more robust conclusion with enhanced statistical power. It involves the use of statistical techniques to analyze aggregated data, often leading to informed decision making and chages in treatment

Standard Deviation

Measures variability that describes the deviation from the average of a frequency distribution

Cluster Analysis

The task of grouping a set of objects in a way that objects in the same group are more similar to each other than those in other groups

One sample T-test

Compares the average of the score. Sample size is small. Looks for differences

U-Test (Mann-Whitney) (Wilcoxon-ran-sum test)

Used when we think we are comparing apples to apples, but we are not sure

Two Sample T-Test

Researchers have a group of subjects, and have two different interventions to apply. They randomize the subjects into two groups, and compare for results

Paired T-Test

Compares a variable measured at two time points on the same subject or comparing values between matched pairs

Anova

Used as a test to compare means between independent variables with similar variance and normality of distribution. Can compare multiple groups

Z-Test

A statistical test utilized to assess whether there is a significant difference between two population means when the variances are known, and the sample size is large.

F Test

Most often used when comparing statistical models that have been fitted to a data set, to identify the model that best fits the population from which the data were sampled.

Chi Square Test

Tests for relationships between categorical variables. Used when you are comparing values you can observe with those you expect

Examples of Chi Square Test

As a person ages, their blood pressure increases.

As a child grows, their height increases

Correlation Coefficient

Measures strength and relationship between two variables on a scatterplot

Examples of Correlation Coefficient

As people age, their likelihood of having arthritic changes increases (positive)

As people increase their exercise activity, their weight decreases (negative)

Descriptive Statistics

Describes and analyzes a given group without drawing any conclusions. Data is measured, organized, graphed.

Inferential Statistics

Draws conclusions about a larger population. Conclusion is based on data from a sample. Make an educated guess

Research Process

Includes research design, study population, data collection, and analysis plan

Research Design

Descriptive, correlational, experimental. The type of study that is being done

Subjective Methods

Observation, watching, taking notes, open-ended questions, in person or phone interviews, focus groups

Experimental Methods

Hypothesis/Null hypothesis parametric tests such as t-tests, ANOVA, linear regression etc

Fidelity

Loyalty, truthfulness

Fidelity

Keeping a promise to a patient about a specific treatment plan and advocating for the patient's rights and well-being during the healthcare process

Autonomy

The ability to make your own decision without being controlled by anyone else

Justice

Fair Selection

Justice

Ensuring that research participants in a clinical trial are selected without discrimination and that the risks and benefits of participation are distributed fairly among diverse populatinos

Nonmaleficence

Not harming

Beneficence

Helping the patient is the goal

Beneficence

Ensuring that participants in a medical study receive the best possible care and protection of their well-being throughout the research process