This lecture introduces quantitative data analysis methods for nursing research. It covers different types of data, ways to summarize and present descriptive data, including measures of central tendency, dispersion, and common charts. The lecture also discusses inferential statistics, the normal distribution, sampling error, and confidence intervals.
This document provides an overview of quantitative data analysis methods for nursing research. It discusses inferential statistical tests that can be used for different data types and group structures, including chi-square tests, Mann-Whitney U tests, Kruskal-Wallis tests, t-tests, ANOVA, and correlation analyses. Examples are given of how to interpret the output of these tests and assess their assumptions. Resources for further learning about medical statistics and research methods are also listed.
This document discusses a study that examined how a clinical rotation in labor and delivery affected student nurses' knowledge. The study utilized a pre-test post-test design to determine if a clinical experience in labor and delivery increased student nurses' knowledge of labor and delivery nursing care. The purpose was to see if hands-on experience in labor and delivery positively impacted what the student nurses knew about providing care during labor and delivery.
Interference is a key concept in physics that describes how waves interact when they overlap. The document discusses an in-service course for postgraduate teachers to learn about the physics concept of interference, which is when two waves meet and either cancel each other out or combine to make a bigger wave.
This document provides an overview of systematic reviews and the PRISMA statement. It discusses the importance of systematic reviews in evidence-based practice and their advantages over traditional narrative reviews. The PRISMA statement aims to improve reporting standards for systematic reviews through a 27-item checklist and four-phase flow diagram to help authors and readers.
Clinical practice guidelines are tools that help healthcare professionals and patients make informed decisions about disease prevention and management. Serious quality problems in medicine over the past decade have led to increased development of guidelines for various medical conditions. Guidelines can improve quality by using systematic evidence-based science to promote optimal patient care. Key attributes are that guidelines are valid, supported by strong evidence, regularly reviewed and updated, and developed through collaboration between care providers and other organizations. Guidelines used in primary care can cover components like prevention, diagnosis, treatment, and education.
The document provides information about the College of Nursing at B.P. Koirala Institute of Health Sciences in Dharan, Nepal. It details the college's vision, mission, goals and academic programs. The college offers Certificate, Bachelor's, and Master's level nursing programs. It has five departments and is staffed by 19 postgraduate faculty members and 11 Bachelor level teachers. The college aims to graduate competent and socially responsible nurses to improve health in Nepal.
Lecture 10. Measurement of study variables (2).pptxPadmaBhatia1
This document discusses various methods for summarizing and describing quantitative data, including measures of central tendency (mean, median, mode) and dispersion (range, interquartile range, standard deviation). It provides examples and definitions of each measure. The mean and standard deviation are presented as the most commonly used measures for quantitative data without extreme outliers, while the median and interquartile range are preferable for data that does contain outliers. The document emphasizes choosing the appropriate central and dispersion values depending on whether the data is quantitative or qualitative.
This document provides an overview of quantitative data analysis methods for nursing research. It discusses inferential statistical tests that can be used for different data types and group structures, including chi-square tests, Mann-Whitney U tests, Kruskal-Wallis tests, t-tests, ANOVA, and correlation analyses. Examples are given of how to interpret the output of these tests and assess their assumptions. Resources for further learning about medical statistics and research methods are also listed.
This document discusses a study that examined how a clinical rotation in labor and delivery affected student nurses' knowledge. The study utilized a pre-test post-test design to determine if a clinical experience in labor and delivery increased student nurses' knowledge of labor and delivery nursing care. The purpose was to see if hands-on experience in labor and delivery positively impacted what the student nurses knew about providing care during labor and delivery.
Interference is a key concept in physics that describes how waves interact when they overlap. The document discusses an in-service course for postgraduate teachers to learn about the physics concept of interference, which is when two waves meet and either cancel each other out or combine to make a bigger wave.
This document provides an overview of systematic reviews and the PRISMA statement. It discusses the importance of systematic reviews in evidence-based practice and their advantages over traditional narrative reviews. The PRISMA statement aims to improve reporting standards for systematic reviews through a 27-item checklist and four-phase flow diagram to help authors and readers.
Clinical practice guidelines are tools that help healthcare professionals and patients make informed decisions about disease prevention and management. Serious quality problems in medicine over the past decade have led to increased development of guidelines for various medical conditions. Guidelines can improve quality by using systematic evidence-based science to promote optimal patient care. Key attributes are that guidelines are valid, supported by strong evidence, regularly reviewed and updated, and developed through collaboration between care providers and other organizations. Guidelines used in primary care can cover components like prevention, diagnosis, treatment, and education.
The document provides information about the College of Nursing at B.P. Koirala Institute of Health Sciences in Dharan, Nepal. It details the college's vision, mission, goals and academic programs. The college offers Certificate, Bachelor's, and Master's level nursing programs. It has five departments and is staffed by 19 postgraduate faculty members and 11 Bachelor level teachers. The college aims to graduate competent and socially responsible nurses to improve health in Nepal.
Lecture 10. Measurement of study variables (2).pptxPadmaBhatia1
This document discusses various methods for summarizing and describing quantitative data, including measures of central tendency (mean, median, mode) and dispersion (range, interquartile range, standard deviation). It provides examples and definitions of each measure. The mean and standard deviation are presented as the most commonly used measures for quantitative data without extreme outliers, while the median and interquartile range are preferable for data that does contain outliers. The document emphasizes choosing the appropriate central and dispersion values depending on whether the data is quantitative or qualitative.
This document discusses research questions and research design in nursing. It provides examples of sources of research questions from clinical experience, professional issues, theoretical frameworks, and policy. Both quantitative and qualitative research designs are covered. Quantitative designs include descriptive, correlational, and causal methods like experiments and surveys. Qualitative designs include ethnography, phenomenology, grounded theory and discourse analysis. The importance of research aims, objectives and questions is emphasized.
This document provides an overview of biostatistics and data analysis. It defines biostatistics as the application of statistics in health sciences and biology. The fundamental tools of the scientific method like hypothesis formulation, experimental design, data gathering and analysis are discussed. Descriptive statistics, which summarize and describe data through numerical, graphical and mathematical presentations are covered. Common descriptive statistics like mean, median, mode, standard deviation and distribution curves are defined. Inferential statistics which allow generalization from samples to populations through hypothesis testing and significance levels are also introduced.
This document provides an overview of key concepts in nursing statistics. It begins by outlining the course objectives, which are to develop statistical literacy, analyze nursing literature, and critically evaluate different statistical methods. It then defines different statistical terms and concepts, including the different types of data, measures of central tendency, variability, and correlation. Examples are provided to illustrate these statistical techniques. The document serves to introduce nursing students to important foundational knowledge in statistics for analyzing nursing research.
This document discusses different study designs used in biomedical research. It begins by describing descriptive study designs including case reports, case series, cross-sectional studies, and ecological studies. It then covers analytical study designs like cohort and case-control studies. Finally, it discusses experimental study designs, focusing on randomized controlled trials. Key points include how these different designs compare in terms of strengths and limitations, and which designs provide the strongest evidence to answer particular research questions.
This document discusses key concepts in biostatistics used in biomedical research. It covers topics like types of variables, measures of central tendency and dispersion, distributions of data, statistical tests for different situations, hypotheses testing and errors, measures of association, diagnostic tests, and regression analysis. Understanding biostatistics is important for evidence-based medicine and improving patient lives through rigorous research. Sample size, confidence intervals, and avoiding bias and confounding are important considerations in study design and interpretation.
This document provides an overview of research methodology in otology. It discusses what constitutes ideal research, factors that affect confidence intervals, different types of study designs including descriptive, analytical, and interventional designs. Descriptive designs include cross-sectional and longitudinal studies. Analytical designs include prospective and retrospective studies. The document also discusses variables, sample size calculation, data analysis techniques, and important considerations for research such as feasibility and timelines.
This document provides an overview of key concepts for collecting and managing data in research studies. It discusses sampling methods, types of variables, data collection techniques including using existing records, observation, interviews and questionnaires. It also covers ensuring quality of data through accuracy, reliability, data handling, data processing including coding, data entry and verification. The goal is to choose appropriate methods to obtain high quality representative data for analysis and drawing valid conclusions.
The document provides information about biostatistics and statistical methodology. It begins with definitions of statistics and biostatistics. It then discusses topics like sampling, types of sampling techniques, measures of central tendency, measures of dispersion, and tests of significance. Specifically, it covers [1] the differences between probability and non-probability sampling, [2] common measures of central tendency like mean, median and mode, [3] measures of dispersion like range, mean deviation and standard deviation, and [4] tests of significance like the standard error test and chi-square test.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
1) The document discusses descriptive statistics and methods for summarizing categorical and numerical data through tables, graphs, and numerical measures.
2) Descriptive statistics are used to describe and characterize data through methods like frequency tables, measures of central tendency, and measures of variability.
3) Various graphs like bar charts, pie charts, histograms and frequency polygons are demonstrated to visually depict distributions of categorical and numerical variables.
This document provides an introduction to biostatistics. It defines key biostatistics terms like data, variables, scales of measurement, and methods of data presentation. Descriptive and inferential statistics are introduced. Common measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance) are defined for different data types. Common methods for presenting data visually, like histograms, bar graphs and box plots, are also described. The normal distribution is introduced as an important assumption for many statistical tests. Examples are provided to illustrate concepts like using z-scores to determine what proportion of values fall above or below a given cutoff from the mean.
Lecture 3 Measures of Central Tendency and Dispersion.pptxshakirRahman10
Objectives:
Define measures of central tendency (mean, median, and mode)
Define measures of dispersion (variance and standard deviation).
Compute the measures of central tendency and Dispersion.
Learn the application of mean and standard deviation using Empirical rule and Tchebyshev’s theorem.
Measures of Central Tendency:
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
Why is it needed?
To summarize the data.
It provides with a typical value that gives the picture of the entire data set
Mean:
It is the arithmetic average of a set of numbers, It is the most common measure of central tendency.
Computed by summing all values in the data set and dividing the sum by the number of values in the data set Properties:
Applicable for interval and ratio data
Not applicable for nominal or ordinal data
Affected by each value in the data set, including extreme values.
Formula:
Mean is calculated by adding all values in the data set and dividing the sum by the number of values in the data set.
Median:
Mid-point or Middle value of the data when the measurements are arranged in ascending order.
A point that divides the data into two equal parts.
Computational Procedure:
Arrange the observations in an ascending order.
If there is an odd number of terms, the median is the middle value and If there is an even number of terms, the median is the average of the middle two terms.
Mode:
The mode is the observation that occurs most frequently in the data set.
There can be more than one mode for a data set OR there maybe no mode in a data set.
Is also applicable to the nominal data.
Comparison of Measures of Central Tendency in Positively Skewed Distributions:
Majority of the data values fall to the left of the mean and cluster at the lower end of the distribution: the tail is to the right Mean, median & mode are different When a distribution has a few extremely high scores, the mean will have a greater value than the median = positively skewed.
Majority of the data values fall to
the right of the mean and cluster at the upper end of the distribution= Negatively Skewed
This document provides information about medical statistics including what statistics are, how they are used in medicine, and some key statistical concepts. It discusses that statistics is the study of collecting, organizing, summarizing, presenting, and analyzing data. Medical statistics specifically deals with applying these statistical methods to medicine and health sciences areas like epidemiology, public health, and clinical research. It also overview some common statistical analyses like descriptive versus inferential statistics, populations and samples, variables and data types, and some statistical notations.
This document provides an introduction to biostatistics. It defines biostatistics as the branch of statistics applied to biological or medical sciences. The document outlines some key functions and applications of biostatistics in areas like pharmacology, medicine, and public health. It also describes some basic principles of biostatistics like data collection, presentation, summarization, analysis, and interpretation. Specific statistical concepts discussed include measures of central tendency, measures of dispersion, sampling methods, and tests of significance.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
The document provides an overview of basic statistics and research methodology, focusing on study designs. It discusses observational studies like cross-sectional, case-control and cohort studies as well as experimental studies like clinical trials. For each study design, it describes the key elements including temporal sequence, intervention, sampling methods, and how they differ from one another. It emphasizes the importance of selecting the appropriate study design based on the research question and highlights factors to consider like ability to determine causation, study of rare diseases, costs and time involved.
This document provides an overview of basic research principles and the research process. It discusses what constitutes research, its functions and sources of knowledge. It also outlines the typical stages of research including problem identification, hypothesis formulation, data collection and analysis. Additionally, it covers research methodology, variables, sampling techniques and ethics in research.
This document provides an overview of evidence-based medicine (EBM). It defines EBM as integrating the best available research evidence with clinical expertise and patient values. It notes that the amount of medical evidence is increasing exponentially, making it difficult for physicians to keep up-to-date. The document outlines the 5 steps of EBM practice and emphasizes the importance of critically appraising evidence for validity, importance, and applicability to patients. It also discusses assessing the levels, strength, and quality of evidence to determine the strength of recommendations for clinical practice guidelines.
This document discusses research questions and research design in nursing. It provides examples of sources of research questions from clinical experience, professional issues, theoretical frameworks, and policy. Both quantitative and qualitative research designs are covered. Quantitative designs include descriptive, correlational, and causal methods like experiments and surveys. Qualitative designs include ethnography, phenomenology, grounded theory and discourse analysis. The importance of research aims, objectives and questions is emphasized.
This document provides an overview of biostatistics and data analysis. It defines biostatistics as the application of statistics in health sciences and biology. The fundamental tools of the scientific method like hypothesis formulation, experimental design, data gathering and analysis are discussed. Descriptive statistics, which summarize and describe data through numerical, graphical and mathematical presentations are covered. Common descriptive statistics like mean, median, mode, standard deviation and distribution curves are defined. Inferential statistics which allow generalization from samples to populations through hypothesis testing and significance levels are also introduced.
This document provides an overview of key concepts in nursing statistics. It begins by outlining the course objectives, which are to develop statistical literacy, analyze nursing literature, and critically evaluate different statistical methods. It then defines different statistical terms and concepts, including the different types of data, measures of central tendency, variability, and correlation. Examples are provided to illustrate these statistical techniques. The document serves to introduce nursing students to important foundational knowledge in statistics for analyzing nursing research.
This document discusses different study designs used in biomedical research. It begins by describing descriptive study designs including case reports, case series, cross-sectional studies, and ecological studies. It then covers analytical study designs like cohort and case-control studies. Finally, it discusses experimental study designs, focusing on randomized controlled trials. Key points include how these different designs compare in terms of strengths and limitations, and which designs provide the strongest evidence to answer particular research questions.
This document discusses key concepts in biostatistics used in biomedical research. It covers topics like types of variables, measures of central tendency and dispersion, distributions of data, statistical tests for different situations, hypotheses testing and errors, measures of association, diagnostic tests, and regression analysis. Understanding biostatistics is important for evidence-based medicine and improving patient lives through rigorous research. Sample size, confidence intervals, and avoiding bias and confounding are important considerations in study design and interpretation.
This document provides an overview of research methodology in otology. It discusses what constitutes ideal research, factors that affect confidence intervals, different types of study designs including descriptive, analytical, and interventional designs. Descriptive designs include cross-sectional and longitudinal studies. Analytical designs include prospective and retrospective studies. The document also discusses variables, sample size calculation, data analysis techniques, and important considerations for research such as feasibility and timelines.
This document provides an overview of key concepts for collecting and managing data in research studies. It discusses sampling methods, types of variables, data collection techniques including using existing records, observation, interviews and questionnaires. It also covers ensuring quality of data through accuracy, reliability, data handling, data processing including coding, data entry and verification. The goal is to choose appropriate methods to obtain high quality representative data for analysis and drawing valid conclusions.
The document provides information about biostatistics and statistical methodology. It begins with definitions of statistics and biostatistics. It then discusses topics like sampling, types of sampling techniques, measures of central tendency, measures of dispersion, and tests of significance. Specifically, it covers [1] the differences between probability and non-probability sampling, [2] common measures of central tendency like mean, median and mode, [3] measures of dispersion like range, mean deviation and standard deviation, and [4] tests of significance like the standard error test and chi-square test.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
1) The document discusses descriptive statistics and methods for summarizing categorical and numerical data through tables, graphs, and numerical measures.
2) Descriptive statistics are used to describe and characterize data through methods like frequency tables, measures of central tendency, and measures of variability.
3) Various graphs like bar charts, pie charts, histograms and frequency polygons are demonstrated to visually depict distributions of categorical and numerical variables.
This document provides an introduction to biostatistics. It defines key biostatistics terms like data, variables, scales of measurement, and methods of data presentation. Descriptive and inferential statistics are introduced. Common measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance) are defined for different data types. Common methods for presenting data visually, like histograms, bar graphs and box plots, are also described. The normal distribution is introduced as an important assumption for many statistical tests. Examples are provided to illustrate concepts like using z-scores to determine what proportion of values fall above or below a given cutoff from the mean.
Lecture 3 Measures of Central Tendency and Dispersion.pptxshakirRahman10
Objectives:
Define measures of central tendency (mean, median, and mode)
Define measures of dispersion (variance and standard deviation).
Compute the measures of central tendency and Dispersion.
Learn the application of mean and standard deviation using Empirical rule and Tchebyshev’s theorem.
Measures of Central Tendency:
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
Why is it needed?
To summarize the data.
It provides with a typical value that gives the picture of the entire data set
Mean:
It is the arithmetic average of a set of numbers, It is the most common measure of central tendency.
Computed by summing all values in the data set and dividing the sum by the number of values in the data set Properties:
Applicable for interval and ratio data
Not applicable for nominal or ordinal data
Affected by each value in the data set, including extreme values.
Formula:
Mean is calculated by adding all values in the data set and dividing the sum by the number of values in the data set.
Median:
Mid-point or Middle value of the data when the measurements are arranged in ascending order.
A point that divides the data into two equal parts.
Computational Procedure:
Arrange the observations in an ascending order.
If there is an odd number of terms, the median is the middle value and If there is an even number of terms, the median is the average of the middle two terms.
Mode:
The mode is the observation that occurs most frequently in the data set.
There can be more than one mode for a data set OR there maybe no mode in a data set.
Is also applicable to the nominal data.
Comparison of Measures of Central Tendency in Positively Skewed Distributions:
Majority of the data values fall to the left of the mean and cluster at the lower end of the distribution: the tail is to the right Mean, median & mode are different When a distribution has a few extremely high scores, the mean will have a greater value than the median = positively skewed.
Majority of the data values fall to
the right of the mean and cluster at the upper end of the distribution= Negatively Skewed
This document provides information about medical statistics including what statistics are, how they are used in medicine, and some key statistical concepts. It discusses that statistics is the study of collecting, organizing, summarizing, presenting, and analyzing data. Medical statistics specifically deals with applying these statistical methods to medicine and health sciences areas like epidemiology, public health, and clinical research. It also overview some common statistical analyses like descriptive versus inferential statistics, populations and samples, variables and data types, and some statistical notations.
This document provides an introduction to biostatistics. It defines biostatistics as the branch of statistics applied to biological or medical sciences. The document outlines some key functions and applications of biostatistics in areas like pharmacology, medicine, and public health. It also describes some basic principles of biostatistics like data collection, presentation, summarization, analysis, and interpretation. Specific statistical concepts discussed include measures of central tendency, measures of dispersion, sampling methods, and tests of significance.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
The document provides an overview of basic statistics and research methodology, focusing on study designs. It discusses observational studies like cross-sectional, case-control and cohort studies as well as experimental studies like clinical trials. For each study design, it describes the key elements including temporal sequence, intervention, sampling methods, and how they differ from one another. It emphasizes the importance of selecting the appropriate study design based on the research question and highlights factors to consider like ability to determine causation, study of rare diseases, costs and time involved.
This document provides an overview of basic research principles and the research process. It discusses what constitutes research, its functions and sources of knowledge. It also outlines the typical stages of research including problem identification, hypothesis formulation, data collection and analysis. Additionally, it covers research methodology, variables, sampling techniques and ethics in research.
This document provides an overview of evidence-based medicine (EBM). It defines EBM as integrating the best available research evidence with clinical expertise and patient values. It notes that the amount of medical evidence is increasing exponentially, making it difficult for physicians to keep up-to-date. The document outlines the 5 steps of EBM practice and emphasizes the importance of critically appraising evidence for validity, importance, and applicability to patients. It also discusses assessing the levels, strength, and quality of evidence to determine the strength of recommendations for clinical practice guidelines.
1. MSc/Dip/Cert Advancing Nursing Practice
MSc by Research (Nursing)
RESEARCH METHODS IN NURSING AND
NURSING STUDIES
NURSING STUDIES
HEALTHCARE (B)
QUANTITATIVE DATA ANALYSIS 1
Dr. Sheila Rodgers
Nursing Studies
University of Edinburgh
2. MSc/Dip NURSING Research Methods
This lecture aims to:
NURSING STUDIES
NURSING STUDIES
•Introduce the different types of data
•Look at ways of summarising and presenting
descriptive data
•Explain measures of dispersion
•Introduce the principles of inferential
statistics
3. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
Types of data
NOMINAL discrete, no true value (yes/no)
ORDINAL categories in an order (staff grade)
INTERVAL count or category of equal
spaces (age group, no. children)
RATIO equal interval, fixed zero, continuous
(age VAS)
4. MSc/Dip NURSING Research Methods
PRESENTING DATA
NURSING STUDIES
NURSING STUDIES
Tables – frequency, contingency
Charts –
Pie or bar charts for nominal data
Histograms for numerical or ratio data
Line graphs and box plots for numerical data
but not frequencies
Scatter plots for numerical and ordinal data
9. • Box plots
Clinical peripherality scores
by NHS Board area (Swan et
al., 2004)
The box plot shows median
values and interquartile range of
scores for each NHS board
area. Higher values represent
greater clinical peripherality. A
summary plot of clinical
peripherality scores for non-
urban practices in each NHS
Board area is shown in this
figure. NHS Boards serving the
more remote and rural areas of
Scotland show greater median
values and a wider scatter of
clinical peripherality values for
their practices.
A detailed analysis can be
found in the Rural Action Team
Report.
http://www.show.scot.nhs.uk/sehd/nationalframework
11. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
MEASURES OF CENTRAL TENDENCY
Mean
Median
Mode
12. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
MEASURES OF VARIATION OR DISPERSION
Range, inter/semi quartile
Standard deviation – average distance of
each measurement from the mean
Variance – SD before the square root is taken
13. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
NORMAL DISTRIBUTION
Bell shape curve reaching to infinity
Uni-modal / Bi-modal
Positive or negative skews
14. MSc/Dip NURSING Research Methods
NORMAL DISTRIBUTION
NURSING STUDIES
NURSING STUDIES
15. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
16. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
17. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
18. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
20. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
SAMPLING ERROR
SE = SD divided by the square root of n
CONFIDENCE INTERVALS
95% chance of having the true population mean
within a certain range:
Mean + (1.96 x SE) AND Mean - (1.96 x SE)
21. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
22. NURSING STUDIES MSc/Dip NURSING Research Methods
NURSING STUDIES
Editor's Notes
Data has been described in different categories according to it properties. Nominal – discrete categories by name, no true value, male female Ordinal – categories in some sort of order, no information about how big the spaces are between each category, maybe a likert scale Interval counts or categories with equal spaces between them, no kids, age groups, no dressing packs used Ratio equal intervals may be continuous has a fixed zero, temp, age vas cost of dressing packs It is important to know what type of data you are dealing with in order to work out if it has been presented and analysed correctly. There are rules as to how different types of data should be presented and analysed. When reading and reviewing papers you shuodl look to see if these rules have been followed.
How we present that data is really important as we want readers to clearly understand what comes out of the data. Whilst it is possible to give tables with large amounts of data, it can often be difficult to make sense of lots of rows and columns of data. Looking at a picture of how that data can be represented or summarised allows us to take in much more information, to make sense of it and to spot trends in the data. Certain types of data are best presented in certain ways.
Nice that is 3D and coloured But there is no key MCI = Mild Cognitive Impairment, no actual numbers are given nor the full total number of people in the study. There could be 36 people in this study or 3,000.
This chart summarises a lot of information in one place about different types of day care provision in Scotland. Colours are used well to separate out different types of data and yet show the relation between categories. There are spaces between sets of bars from different client groups to show that the data is not continuous. A common error when presenting bar charts is to have the bars touching each other implying that the data follows sequentially from on e bar to the next. This is only true in histograms when plotting out data that is at least at interval level.
In this histogram, two different populations are presented, international and UK nurses. They are quite rightly shown as two different colours. They clearly show the trend for a marked increase in the number of international nurses registering in the UK with a slight decline in 2002/3. There is a space between the bars because the two different categories of nurses are being kept separate.
In this line graph, both colours and shape markings on the lines are used to try and differentiate between the different sets of data. However the colours are not that distinguishable and the markings very hard to read. It is easy however to spot the trend in this data and also see the rate of change over time.
This box plot looks at the different regions of Scotland in terms of how remote and rural their health services are. A scale was created to indicate this and ranged from 0 to 100. The higher the score, the more widely dispersed is their health care provision. This type of graph is a good way of presenting numerical data which might have quite a range of values within it. For example, in Lothian (Edinburgh is the heart of this region), the majority of the services are quite centralised with the average score for Lothian’s services being 20 – the middle of the box. However, the ends of the lines represent the top and bottom 25% of the whole data set which shows here that over 25% of Lothian’s services have a high score of rurallity and dispersion at over 60.
Scatter plots can be useful to look at the relationships between two sets of data that are both numerical and of at least ordinal value. It is much easy to interpret how scores on one variable might change when scores on the other variable change. In this example we can see how when arterial blood pH is high that capillary blood pH also tends to be high. This study was trying to assess if capillary blood sampling could be used for pH measurement rather than taking arterial blood. What do you think ?
Interquartile range to show middle 50% of values around the median SD is the square root of variance so that the average variation is shown in the correct units Variance – sum (X-mean) 2 n-1
Infection control paper Ethics do we have the right to use patient record data – ethics committees may give to people to use