Quantitative Research SOHP503 Project

Describes the data you've collected.

Type of data where values are numerical.

Type of data where values are ordered categories.

Type of data where values are unordered categories.

A table showing the count of occurrences for each value in a dataset.

A table showing the frequency distribution of two or more categorical variables.

A descriptive statistics technique that measures central tendency, like mean, median, and mode.

A descriptive statistics technique that describes the distribution of data, like range, variance, and standard deviation.

A descriptive statistics technique that compares the means of two groups.

A statistical value indicating the likelihood of obtaining observed results if there were truly no difference between groups.

A measure of how much a treatment or intervention affects a particular outcome. It's calculated as the difference in outcome between the intervention group and the control group.

A statistical test that examines the difference in means between two groups. It's used to determine if the groups are significantly different.

A measure of the average glucose levels in your blood over a period of time. It's used to monitor diabetes management.

A measure of a person's overall well-being, taking into account physical, mental, and social aspects. It's often used in healthcare to assess the impact of treatments or interventions.

A visual representation of data points where the horizontal axis represents one numerical variable, and the vertical axis represents another numerical variable.

A type of visual representation that displays the relationship between two or more numerical variables. Often used to identify trends or patterns in the data.

A data point used to represent a measured value in a dataset.

A variable that can be categorized into groups, such as eye color or gender.

A visual representation that displays the trend or the relationship between two numerical variables, visually highlighting the direction and strength of the association.

A plot that displays two numerical variables and one categorical variable, with the data points colored based on the categorical variable, allowing for visual analysis of relationships across different categories.

Choosing the most appropriate type of data visualization for the given data and analysis objective.

Quantitative Research enables the analysis and interpretation of numerical data, drawing conclusions from the results to answer research questions.

Descriptive statistics summarize the key features of a data set, allowing researchers to understand the distribution, central tendency, and variability of the data.

Inferential statistics allow researchers to draw conclusions about a population based on a sample of data, making generalizations beyond the observed data.

Data visualization refers to the presentation of data in a graphical format, using charts, graphs, and other visual aids to communicate insights and patterns.

It refers to the process of critically evaluating the limitations and strengths of statistical techniques, considering factors like appropriateness for the research design and the potential for bias.

The numerical context refers to the meaning and significance of a number in relation to its scale, units, and reference points. This is essential for understanding the magnitude and implications of the data.

The topic context refers to the specific research area or domain to which the data pertains, providing context for interpreting findings and understanding their implications.

Quantitative Research methods provide researchers with a systematic approach to collect, analyze, and interpret numerical data, leading to objective findings and conclusions.

The variable that is changed or manipulated in an experiment, e.g., type of diabetes care received (MDC app vs. usual care)

The variable that is measured in an experiment to see if there's a change, e.g., HbA1c levels, health-related quality of life

A group in an experiment that receives the treatment or intervention being studied.

A group in an experiment that does not receive the treatment or intervention being studied.

The process of assigning participants to different groups in a study randomly.

A measurement of average blood sugar levels over a period of time, used to assess diabetes control

A p-value is a statistical measure that indicates the probability of observing a result as extreme as the one obtained, assuming the null hypothesis is true. A lower p-value (typically less than 0.05) suggests that the observed result is unlikely to have occurred by chance, providing evidence to reject the null hypothesis.

Statistical significance refers to the likelihood of observing a result as extreme as the one obtained, assuming the null hypothesis is true. A statistically significant result has a low p-value (typically less than 0.05), suggesting that the observed result is unlikely to have occurred by chance.

A meaningful difference is a statistically significant difference that is also practically relevant. It refers to the real-world impact or importance of the observed difference.

Parametric statistics rely on assumptions about the distribution of data, such as normality and linearity. They are used when data meets certain criteria and allows for more powerful tests.

Non-parametric statistics are used when data does not meet the assumptions of normality or linearity required for parametric tests. They are more flexible but generally have less power.

Correlation measures the strength and direction of the linear relationship between two variables. A positive correlation indicates that both variables move in the same direction, while a negative correlation means they move in opposite directions.

A correlation coefficient is a numerical value that quantifies the strength and direction of a correlation. Values range from -1 to +1, with 0 indicating no correlation. A value of 1 indicates a perfect positive correlation, and -1 indicates a perfect negative correlation.

A scatter plot is a graphical representation that shows the relationship between paired data points. It helps to visually assess the strength and direction of a correlation.

Pearson's r is a parametric correlation coefficient used to measure linear association between two continuous variables. Its assumptions include normality, linearity, and no outliers.

A t-test is a statistical test used to compare the means of two groups. It determines whether there is a statistically significant difference between the group means.

Outliers are data points that are significantly different from other data points in a dataset. They can influence the results of parametric analyses, so it's important to identify and address them.

A trend line is a line on a scatter plot that shows the general direction of the relationship between two variables. It helps to visualize the correlation between the variables.

Causation refers to a cause-and-effect relationship between two variables, where one variable directly influences the other. Correlation does not imply causation. Just because two variables are correlated does not mean that one causes the other.

An intervention is a planned set of actions or treatments implemented to change a specific outcome or behavior. It can be used to test the effectiveness of a new treatment or intervention.

Normal distribution is a bell-shaped distribution that represents the distribution of many natural phenomena. It occurs when data is symmetrically distributed around the mean.

Linearity refers to a straight-line relationship between two variables. Data is linear when the relationship between variables can be represented by a straight line.