Training Index

This page gives a complete index of everything I currently deliver within John varlow | Training and Consultancy courses. The index will get bigger as we deliver more training courses.

It is the intention that each of the items in the index will have a description of what is covered within the training but this is a work in progress so please bear with us as this is developed.

A

      • Absolute risk

What is absolute risk, how do you calculate it, and why do we use it?

      • Absolute risk reduction

What is absolute risk reduction? How does it relate to absolute risk? How is is calculated, and why do we use it?

      • Abstracts

What is an abstract? What should go into an abstract? When should an abstract be written?

      • Abstract presentations

What is an abstract for a presentation? How do you write one? What are the rules?

      • Accessibility (data quality)

A key part of data quality. How do you ensure data is accessible? What are the problems?

      • Accuracy (data quality)

A key part of data quality. How do you ensure your data is accurate? What are the problems?

      • Additive ETS models

Error, trend and seasonality models following an additive approach for errors. What are they and why are they chosen?

      • Agile projects

A project management technique often used in software development. What is an agile project? What are the benefits and when should they be used?

      • Akaike’s Information Criterion (AIC)

Time series data is used a lot in the NHS and Social Care. How do we decide which of our ARIMA models fits the data best? How does the AIC help with this? What is the difference between AIC and BIC?

      • Alpha (chance)

The level of chance set within studies, to decide sample size and statistical certainty. What is alpha and how do we set it appropriately?

      • Alternate / alternative hypotheses

What is a hypothesis? What is special about the alternate hypothesis and how should it relate to your analytical / research question? Alternative to what?

      • Analysis (SPSS)

How do you perform analysis within SPSS? What are the analysis menus? How do you interpret your results? (also delivered in PSPP if SPSS not available)

      • Analysis strategy / approach (research proposal)

How do you determine how you are going to analyse your data before you have collected it? How should the analysis strategy be written

      • Analytical design

How is data collected and grouped? What is the difference between an independent measure and a repeated measure design?

      • Analytical questions

Why is asking the right question important? How do you get people to ask the right question? What should go into a good question? How does the question relate to how you will do your analysis?

      • Approaches to outcome measurement

What is outcome measurement? What are the different approaches? How do individual outcomes differ to research or population level outcomes?

      • A priori power

Statistical power is important within research. It is related to sample size, play of chance and clinical differences. What is power, and when should an a-priori power be calculated?

      • ARIMA models

What is an ARIMA model? What is meant by an ARIMA(p,d,q)? What is meant by an ARIMA(p,d,q),n,(P,D,Q)? When shouldn't an ARIMA model be used?

      • ARIMA model selection

How do you pick the right ARIMA model? How do you ensure your data is stationary? How do you use ACF and PACF?

      • ARIMA outputs

How do you interpret the results / output of your ARIMA analysis. How do you determine whether you have a good model? How do you predict future values?

      • Association of variables

Sometimes variables seem associated. How can you test this and what does it mean?

      • Assurance of indicators

What makes a quality indicator? What tools are available to have your indicator assured independently?

      • Audit definition

How do you define audit? Why do people define it incorrectly? How does audit relate to research, and what are the key differences?

      • Audit lifecycle

Audits go through a lifecycle. What are the key components of the audit lifecycle?

      • Auto-correlation

      • Auto-correlation function (ACF)

The ACF is used within ARIMA models to determine what model to use. How do we do this?

      • Autoregressive models

ARIMA models are ways of looking at time series data. What is the autoregressive element in an ARIMA model. How does it differ from normal regression?

      • Average

We use the word 'average' all the time and we often use it to refer to the arithmetic mean. What other averages do we use? Which is the best average given the type of data that we have?

      • Average method

There are a number of simple time forecasting methods. One of these is the average method? What is the average method and how is it calculated?

B

      • Bar charts

What is a bar chart? How do they differ from histograms? What sort of data should we use in a bar chart?

      • Background (research proposal)

What should be included in the background section of a research proposal? How does this relate to the introduction? Where does the literature review fit?

      • Baseline category

When creating dummy variables for regression models you always need to specify a baseline category. What is a baseline category? How do you pick which category is the baseline, and how do you interpret your results?

      • BATS overview

A high level overview of the BATS method of time forecasting, currently only available within the statistical software R

      • Bayesian Information Criterion (BIC)

Time series data is used a lot in the NHS and Social Care. How do we decide which of our ARIMA models fits the data best? How does the AIC help with this? What is the difference between AIC and BIC?

      • Bell shaped curve

What is the bell shaped curve? Why is it important? What other names do we have for it? How does it relate to other distributions?

      • Bernoulli distribution

High level overview of the distribution, its link to the binomial distribution and how it relates to other distributions

      • Beta (power)

Statistical power is necessary in all research and analysis. What is power and how does this relate to B. How do we set B and how does it relate to sample size, statistical and clinical significance? How does the concept of power relate to sensitivity, specificity, type I and Type II errors?

      • Bias

All human research has bias. What is bias? What are the different types and how can we minimise it?

      • Binomial distribution

High level overview of the distribution, its link to the Bernoulli distribution and how it relates to other distributions. What is its role in probability?

      • Binomial logistic regression

Regression methods are a type of multi-variate analysis. Why do we use regression methods? What is logistic regression and how does it relate to linear regression? How do we interpret results as both odds ratios and probabilities?

      • Bi-variate analysis

What is bi-variate analysis? How does it differ from uni-variate or multi-variate analysis? What sort of bi-variate techniques are there?

      • Blinding

Blinding is essential in RCTs, and controls for the placebo effect. What is blinding, and how do we achieve it? What is the difference between single and double blinding? What are some of the pitfalls?

      • Blobbogram

Otherwise known as a Forrest plot. What is it and how is it used in systematic reviews and meta-analysis? What are the implications for non-significant results?

      • Block Randomisation

There are many different types of randomisation. What is block randomisation? How do you define blocks? When is it used?

      • Body language

Body language is key when presenting to / training your audience. What are the dos and donts of body language? How can you change what you do?

      • Bonferonni test

One of a number of post-hoc tests. Bonferroni is used following analysis of variance (ANOVA). Why are these types of tests needed? How do they relate to t-tests?

      • Box and whisker plots

What is a box and whisker plot? What sort of data should be put in this kind of plot? What are the strengths of this plot? Why aren't they used more than they are?

      • Box Cox transformations

Data often has to be transformed, particularly when doing regression or time forecasting. What is the Box Cox transform and how does it relate to power and log transforms?

      • Breusch Godfrey test

Autocorrelation often occurs n data series, particularly time series. What is the Breusch Godfrey test and how does it relate to autocorrelation?

C

      • Capability

All processes have variation, both natural and special. Given this, how capable is your process in meeting a particular target and how do you measure this?

      • Case-control design

What is case-control design? When is it used and what are the strengths and weaknesses of such an approach?

      • Case series design

What is case series? When is it used and what are the strengths and weaknesses of such an approach?

      • Case study design

Case study design is one of the 3 types of research design. What is it~? How is it carried out, and what are the strengths and weaknesses of this design approach?

      • Categories

Any type of data can be put into categories. What are independent categories and related categories? What is the difference between ordered and un-ordered categories?

      • Categorical data

There are two types of categorical data, these are nominal and ordinal data. What is special about categorical data? How should it be treated and how does it relate to the tests that can be performed on the data?

      • Causation

Many pieces of research and analysis try to prove that there is causation i.e. one factor causes another. What are the problems with this approach? What are the research designs that support causation? What are the implications of getting it wrong?

      • Central limit theorem

This theorem is one of the fundamentals of inferential statistics and underpins the concepts of standard error and confidence intervals. How does it relate to the distribution of your data? How does it build on the concepts of normal distributions and standard deviation?

      • Centroid

What is the centroid? How does it relate to the line of best fir within linear regression? How is it related to summary statistics and mean values?

      • Certainty

We often treat our results (of research or analysis) as facts. But are we really certain about anything? What is the role of certainty in statistics? How certain can we really be?

      • Chance effect

No matter how good our research or analysis, sometimes strange things happen for no reason. How do we account for chance effects within our analysis? What is the link between chance effects and significance?

      • Chance (alpha)

Any analysis has to take into account the play of chance. Chance is instrumental in determining sample size. How does setting alpha reflect on confidence intervals and statistical certainty?

      • Charts - overview

There are a number of ways to present our data. These mostly use a selection of charts. How do you know what chart to use? How does the choice of chart relate to the type of data that you have?

      • Chi Square test

Based on the chi-square distribution. What is the chi-square test? How many independent groups can it be used on? What is the role of Fisher's test? How do you calculate by hand?

      • Classifications

Much of the data that we deal with (particularly in the NHS and Social Care) is classification data. What is classification data? How does it differ from terminology? What examples do we have? How do we analyse this type of data?

      • Clinical significance

We often talk bout significance, but what do we mean? What is clinical significance? How does it differ from statistical significance? How does it impact on sample size calculations and power?

      • Clinical Terms version 3

There are a number of different terminologies currently within systems. CTV3 is one such terminology. How is it used and what are the future plans?

      • Cluster Sampling

There are a number of ways of identifying your sample. What is cluster sampling? What are the strengths and weaknesses? How does it compare to other sampling methods?

      • Cochrane's Q test

Cochrane's Q is a non-parametric test for related groups. How is it calculated? What distribution is it based on? How many groups can it be used on? How does it relate to the McNemar test?

      • Cohort analytic studies

What is a cohort analytic study? When is it used and what are the strengths and weaknesses of such an approach?

      • Cohort design

What is cohort design? When is it used and what are the strengths and weaknesses of such an approach?

      • Combination charts

We often have a lot of data to present, and combination charts, such as a clustered bar are commonly used. What is the problem with these types of charts? When should they be used? Are there alternatives?

      • Comparison Group

The word comparison is used to explain groups of subjects within research studies and forms part of the PICO approach. What is the relationship between a control group and a comparison group?

      • Completeness (data quality)

A key part of data quality. How do you ensure data is complete? What are the problems?

      • Complex data manipulation (SPSS)

How do you perform complex data manipulation within SPSS? What are the manipulation menus? What can go wrong? (also delivered in PSPP if SPSS not available)

      • Compound measures

Measures are usually simple or compound. What is a compound measure and why are they used? What are the problems with compound indicators?

      • Confidence limits

P-values have been used to express statistical probability for many years. However, it is now good practice to use confidence intervals. What are confidence intervals? How do you calculate them? What advantage do they have over p-values?

      • Confounding variables

There are some variables that cause problems when looking at associations and causal relationships. What are confounding variables? How do they relate to other variables? What can you do to remove their effect?

      • Conservation of resources (ethics)

Research should always consider and mitigate for ethical issues. How does the conservation of resources relate to ethics and how can you mitigate for this?

      • Constant

Many statistical models contain a constant. What is a constant and how do you interpre them within regression and forecasting models?

      • Continuous data

All data is either categorical or continuous. What is different between categorical and continuous data? How should we treat categorical data? What do we mean by interval and ratio data? What is the relationship between continuous and ordinal data, and when should you treat continuous data as if it is ordinal?

      • Control (project)

Control is one of the most important aspects of project management. What is control? Why is t so important and how do you ensure it takes place?

      • Control subjects

The word control is used to explain groups of subjects within research studies. What is control and why are some groups inappropriately named. What is the relationship between a control group and a comparison group?

      • Control limits

Control limits are used with control charts. What are they, and how do they differ from confidence intervals?

      • Convenience Sampling

There are a number of ways of identifying your sample. What is convenience sampling? What are the strengths and weaknesses? How does it compare to other sampling methods?

      • Correlations

How do you see how variables are associated? What are correlations and how do you interpret them? Which correlation techniques are associated with different types of data?

      • Correlation coefficients

What is correlation? What tests should you use? How do you interpret specific coefficients. How do you calculate them?

      • Cox's proportional hazards

What is a hazard and hazard ratio? How does this differ from risks and risk ratios? What role does time play in hazards? How are Kaplan Meier survival curves related to proportional hazards?

      • Critical appraisal overview

What is the difference between appraising and reading an article? What do we mean by information overload? How do we keep on top of the literature? Where did critical appraisal originate? What tools are there?

      • Critical appraisal process

What is the process to follow when critically appraising a paper? What is the most important part of a paper. How do you decide on the relevant tool? What screening questions should be asked before spending time on a paper?

      • Critical appraisal skills programme (CASP)

What is CASP? Where was it started? What tools have they made available for critical appraisal?

      • Cross-sectional design

Many studies are designed as being cross sectional. What does this mean? How are cross sectional studies established and what conclusions can be drawn from them? What are their strengths and weaknesses?

      • Cyclical variation

All time series data have a number of different properties. One of these is cyclicity. What is cyclicity and how does it vary from seasonality?

D

      • Damping methods

Forecast data that follows a linear trend often over estimates future forecast values. One way to account for this is damping, or dampening. What is damping? How is it calculated and applied to ETS models?

      • Data model and dictionary

All changes of data standards within the NHS in England are included within the NHS Data Model and Dictionary. What is the dictionary? How is it updated, and how are system suppliers and the NHS service notified of changes? What is the implication of not following the dictionary?

      • Data protection (ethics)

Research should always consider and mitigate for ethical issues. How does data protection relate to ethics and how can you mitigate for this? How has data protection changed with the DPA 2018 and GDPR?

      • Data quality - overview

Data quality is essential if our analysis is going to be meaningful. What are the elements of data quality? How do you ensure your data is as high quality as possible?

      • Data quality : accessibility

A key part of data quality. How do you ensure data is accessible? What are the problems?

      • Data quality : accuracy

A key part of data quality. How do you ensure data is accurate? What are the problems?

      • Data quality : completeness

A key part of data quality. How do you ensure data is complete? What are the problems?

      • Data quality : relevance

A key part of data quality. How do you ensure data is relevant? What are the problems?

      • Data quality: timeliness

A key part of data quality. How do you ensure data is accessible? What are the problems?

      • Data standards

How is data coded within systems? How do we ensure that data is collected in the same way and that systems can talk and connect to one another? What is interoperabilty? What is the data dictionary and how do data standards get in there?

      • Data transformation

Data often has to be transformed, particularly when doing regression or time forecasting. What are the different types of transforms?

      • Definition of audit

How do you define audit? Why do people define it incorrectly? How does audit relate to research, and what are the key differences?

      • Definition of descriptive statistics

There are two categories of statistics, descriptive and inferential. What are descriptive statistics@ When do you use them and what do they tell you? What are the limitations of descriptive statistics?

      • Definition of evaluation

How do you define evaluation? How does evaluation relate to research and audit?

      • Definition of an indicator

What do we mean by an indicator. How is it different to a metric? What do we mean by the underlying construct of the indicator?

      • Definition of inferential statistics

There are two categories of statistics, descriptive and inferential. What are inferential statistics? When do you use them and what do they tell you? What are the limitations of inferential statistics?

      • Definition of a metric

Metrics and indicators are used extensively within the NHS and Social Care. What is the difference between a metric and an indicator? How do we use metrics and how are they misused?

      • Definition of a number

Numbers are everywhere. We use numbers to count things. But what are numbers? Can we always treat them in the same way?

      • Definition of research

How do you define research? Why do people define it incorrectly? How does audit relate to research, and what are the key differences?

      • Definition of service development

What is service development? How is it different to evaluation and why does it need distinguishing from research or audit?

      • Definition of time series data

Time series data is used across the NHS and Social Care. What is it, and how can we analyse and forecast from it?

      • Degrees of freedom

Degrees of freedom are fundamental to all statistical tests. What do we mean by independent observations and how does this relate to degrees of freedom? What statistics do we commonly use that incorporate degrees of freedom?

      • Delphi Method

Often used in judgemental forecasting. What is the delphi method and what are its limitations?

      • Denominator

Numerators and denominators are the primary ingredients of rates. What are they, and what is the difference between the two? Do numbers in your numerator always have to be in your denominator?

      • Dependent variables

What are dependent variables? How do they differ from independent variables? How do we use dependent variables? Are their any other variables we need to consider?

      • Descriptive statistics - definition

There are two categories of statistics, descriptive and inferential. What are descriptive statistics@ When do you use them and what do they tell you? What are the limitations of descriptive statistics?

      • Design : case-control

What is case-control design? When is it used and what are the strengths and weaknesses of such an approach?

      • Design : case series

What is case series? When is it used and what are the strengths and weaknesses of such an approach?

      • Design : case study

Case study design is one of the 3 types of research design. What is it~? How is it carried out, and what are the strengths and weaknesses of this design approach?

      • Design : cohort

What is cohort design? When is it used and what are the strengths and weaknesses of such an approach?

      • Design : cross-sectional

Many studies are designed as being cross sectional. What does this mean? How are cross sectional studies established and what conclusions can be drawn from them? What are their strengths and weaknesses?

      • Design : experimental

One of the three main study designs. What is experimental design? Why is it seen to be the best design? What are its limitations?

      • Design : survey

One of the three main study designs. What is survey design? What are the strengths and weaknesses of such a design?

      • Determinism

Part of the scientific method. What is determinism? Why is it important in research and analysis? What other ways of thinking are there?

      • Deviation

Deviation is a concept used in a number of statistics, the most common being standard deviation. What is deviation and how do we measure it? How does deviation link to degrees of freedom?

      • Dichotomous data

What is dichotomous data? What type of data is dichotomous data? What can we do with dichotomous dependent variables? Can we treat dichotomous data as continuous data?

      • Differencing

Time series data needs to be stationary before an ARIMA model can be built. How do we make our data stationary? What is differencing and how does it make our data stationary?

      • Different methods of dissemination

Dissemination is a key part of the research process. What methods of dissemination are there? Are some methods better than others? Does the audience play a part in deciding which method you use?

      • Direct standardisation

Calculated rates and ratios are often subject to different population structures. How can we adjust our data so that rates are comparable between populations. Can we use national population structures to adjust our data. Can this always be done? What should you standardise for? What is the difference between direct and indirect standardisation?

      • Discrete data

There are many types of data. What is discrete data? How does it differ from integer continuous data? How should it be treated, and what statistical tests can be used?

      • Disseminating research findings

What do we mean by dissemination and why should we disseminate our research findings?

      • Dissemination strategy

What is a dissemination strategy and why should you have one when you are doing research?

      • Distributions

All data is distributed in particular ways. What are the particular types of distribution and how do they relate to each other? Why do we concentrate on the normal distribution?

      • Distribution : Bernoulli

High level overview of the distribution, its link to the binomial distribution and how it relates to other distributions

      • Distribution : Binomial

High level overview of the distribution, its link to the Bernoulli distribution and how it relates to other distributions. What is its role in probability?

      • Distribution : Chi Square

High level overview of the Chi Square distribution and how it relates to other distributions

      • Distribution : Exponential

High level overview of the exponential-distribution and how it relates to other distributions

      • Distribution : F

High level overview of the F-distribution and how it relates to other distributions

      • Distribution : Gaussian

High level overview of the Gaussian distribution. Is it different to the normal distribution and how does it relate to other distributions

      • Distribution : Leptokurtic

What is a leptokurtic distribution and how does it relate to kurtosis?

      • Distribution : Mesokurtic

What is a mesokurtic distribution and how does it relate to the normal distribution

      • Distribution : Normal

High level overview of the normal distribution, its other names, how it differs from the Standard Z and how it relates to other distributions

      • Distribution : Platykurtic

What is a platykurtic distribution and how does it relate to skewness?

      • Distribution : Poisson

High level overview of the poisson distribution and how it relates to other distributions

      • Distribution : Student T

High level overview of the student's t-distribution, its link to the normal distribution and how it relates to other distributions

      • Distribution : Standard Z

High level overview of the distribution, its link to the normal distribution and how it relates to other distributions

      • Distribution : Uniform

High level overview of the distribution and how it relates to other distributions

      • Doubling up principle in ratio data

Ratio data is a special type of continuous data. What is the doubling up principle and how does it relate to ratio data?

      • Drift Method

There are a number of simple time forecasting methods. One of these is the drift method? What is the drift method and how is it calculated?

      • Dummy variables

We create dummy variables for regression models when using categorical data. How do you create dummy variables? How many dummy variables are there? What is a baseline category? How do you pick which category is the baseline, and how do you interpret your results?

      • Durbin Watson test

Autocorrelation often occurs n data series, particularly time series. What is the Durbin Watson test and how does it relate to autocorrelation?

E

      • Ecologic validity

There are many types of validity when considering results. What is ecologic validity? Why is it important and why is it often overlooked?

      • Elements of time series data

Time Series data is used a lot within the NHS and Social Care. What are the elements of time series data and how do they differ from each other?

      • Empiricism

Part of the scientific method. What is empiricism? Why is it important in research and analysis? What other ways of thinking are there?

      • Epidemiology overview

What is epidemiology? When is it used, and what are the major methods within it? How does epidemiology relate to research and analysis?

      • Equal Variance Assumed

What does it mean for samples to have equal variance? What do you do if this is not the case? How does this relate to the Z-test and the T-test for two independent groups. What is Levene's statistic?

      • Error : Sampling

Much of inferential statistics is concerned with error. What is sampling error and how can it be used to our advantage?

      • Error : Standard

Much of inferential statistics is concerned with error. What is sampling error and how can it be used to our advantage?

      • Ethical approval

We talk a lot about ethics within research. Research studies need ethical approval before studies can start. What is ethical approval? What is considered and how long does it take?

      • Ethics overview

What are ethics in research? What are the ethical issues that you need to consider? How do you mitigate for these?

      • Ethics : conservation of resources

Research should always consider and mitigate for ethical issues. How does the conservation of resources relate to ethics and how can you mitigate for this?

      • Ethics : data protection

Research should always consider and mitigate for ethical issues. How does the data protection relate to ethics and how can you mitigate for this?

      • Ethics : informed consent

Research should always consider and mitigate for ethical issues. How does informed consent relate to ethics and how can you mitigate for this?

      • Ethics : minimising discomfort

Research should always consider and mitigate for ethical issues. How does minimising discomfort relate to ethics and how can you mitigate for this?

      • Ethics : protecting participants

Research should always consider and mitigate for ethical issues. How does the protection of participants relate to ethics and how can you mitigate for this?

      • Ethics : taboos in the community

Research should always consider and mitigate for ethical issues. How do the taboos of the community relate to ethics and how can you mitigate for this?

      • Ethnography

What is ethnography? How does it relate to the interpretevist paradigm?

      • ETS Models

What are ETS models? What does ETS stand for, and when are these models used? How do ETS models relate to other forms of time series forecasting models?

      • Evaluation - definition

How do you define evaluation? How does evaluation relate to research and audit?

      • Event rates

What are event rates? How are they calculated? How are they used and what are the problems with using them?

      • Experimental design

One of the three main study designs. What is experimental design? Why is it seen to be the best design? What are its limitations?

      • Exploratory analysis

What is exploratory analysis? When should you do it? What does it tell you?

      • Exponential distribution

High level overview of the exponential-distribution and how it relates to other distributions

      • Exponential smoothing

Why do we use smoothing methods? What is exponential smoothing? What methods are included in exponential smoothing? How does it relate to ETS models?

      • Exponential triple smoothing

Often (if incorrectly) refereed to as ETS, what is exponential triple smoothing? What are the properties of time series data that are addressed using triple smoothing?

      • Exponential trend

All data follows trends. What is an exponential trend? How does it relate to growth? Is it the same as a polynomial trend?

      • External reliability

There are a number of different types of reliability. What is external reliability? How does it differ from internal reliability? How does inter and intra observer reliability fit in with external reliability?

F

      • F distribution

High level overview of the F-distribution and how it relates to other distributions

      • Feasibility

Feasibility is key to a successful research project. What is feasibility and how can you ensure that your project is feasible?

      • Finance and resources

Finance / resource considerations are key in delivering a successful research project. How do you ensure that you know what resources and finance you will need? What is the connection between finance / resource and timescale of a project?

      • Fisher's test

Most nominal data is compared using tests based on the chi-square test. When should Fisher's test be used instead? What are the rules?

      • FORECAST function (MS Excel)

Microsoft Excel has a number of inbuilt statistical functions that keep being developed with each release. What is the FORECAST function? How did it start off in MS Excel and what can you do with the function in the latest version. How does the FORECAST function differ from the TREND function, and what is the link with linear regression and exponential smoothing?

      • Forecasting

A lot of data within the NHS and Social Care is time series data. How do you forecast what might happen in the future given historical data? What forecasting models can you use?

      • Forrest Plot

Otherwise known as a blobbogram (by CASP). What is it and how is it used in systematic reviews and meta-analysis? What are the implications for non-significant results?

      • Fourier series

Named after the French mathematician, what is a Fourier series? How is it used within regression models? How does it relate to seasonality?

      • Friedman test

What is the Friedman test? When do you use it? How does it relate to the McNemar test?

      • Funnel Plots

Made famous by Walter Shewhart, what are funnel plots? When are they used and what do they tell you?

G

      • Gaming and perverse incentives

We construct indicators and metrics on a regular basis, introduce new initiatives and set targets. What s the problem with doing this? What is gaming and perverse incentives? How can we avoid the issue?

      • Gantt Charts

What tools do we have in project management? How do we record what the project plan is and how we are meeting key milestones? What is a Gantt chart and why are they useful?

      • Gaussian distribution

High level overview of the Gaussian distribution. Is it different to the normal distribution and how does it relate to other distributions

      • Gradient

A linear regression line is made up of a constant and a gradient. How do we determine what the right gradient is? How do we calculate it?

      • Graphical data presentation

There are a number of ways to graphically present our data. These mostly use a selection of charts. How do you know what chart to use? How does the choice of chart relate to the type of data that you have?

      • Grounded Theory

Grounded theory is a key concept within qualitative research. What is grounded theory and how is it used?

      • GROWTH function (MS Excel)

The GROWTH function within MS Excel is a key statistical function looking at forecasting. How does the GROWTH function link to polynomial regression? How is it used appropriately?

H

      • Hand gestures in presentation

Hand gestures are used a lot in presentations, for effect and emphasis. How can you use hand gestures to best effect? What are some gestures that you should avoid using?

      • Hawthorne Effect

The Hawthorne Effect occurs in prospective studies. What is the Hawthorne effect and can you control for it?

      • Healthcare Resource Groups (HRG)

Healthcare Resource Groups (HRGs) are used within the NHS for costing purposes. What are HRGs? How are they defined and used?

      • Heteroskedasticity

Heteroskedasticity is a key part of regression methods. What is heteroskedasticity and how does it relate to residuals and autocorrelation?

      • Histograms

What is a histogram? How do they differ from bar charts? What sort of data should we use in a histogram?

      • Holt's linear method

What is exponential smoothing and how does Holt's linear method relate to Brown's Method and Holt Winter's method? When should each method be used and how do they relate to seasonality? Is there a different method when relationships are not linear?

      • Holt Winter's method

What is exponential smoothing and how does Holt Winter's method relate to Brown's Method and Holt's linear method? When should each method be used and how do they relate to seasonality? Is there a different method when relationships are not linear?

      • Homogeneity of variance

What do we mean by homogeneity of variance? Why is it important, and how is Levene's statistic used to interpet results where various isn't homogenous?

      • Homoskedasticity

Homoskedasticity essential in regression methods. What is it, and how does it relate to heteroskedasticity? How can you make your residuals homoskedastic?

      • Hypotheses : alternate

When should you write a hypothesis? How does this relate to your research / analytical question? What is an alternate hypothesis and what is it the alternative to?

      • Hypotheses : one-tailed

When should you write a hypothesis? How does this relate to your research / analytical question? What is a one-tailed hypothesis and how does this relate to one tailed statistical tests?

      • Hypotheses : null

When should you write a hypothesis? How does this relate to your research / analytical question? What is a null hypothesis? How does it relate to the alternate hypothesis? Can you prove a null hypothesis? Why formulate a hypothesis that hypothesises nothing?

      • Hypotheses : statistical

What do we mean by a statistical hypothesis? Is this different to a research hypothesis? What do we mean when we talk about significant differences in a statistical hypothesis?

      • Hypotheses : two tailed

What is the difference between a one-tailed and a two-tailed hypothesis? How does this affect the analysis that is performed. Are there any rules as to which type of hypothesis is picked?

I

      • ICD codes

What are ICD codes? How are they used in practice and why should we care? What is the difference between different versions? How are they analysed, and how do they relate to other coding structures? What do we mean by classifications and how does this differ from terminology? What do we mean by coding granularity?

      • Implementation

What do we mean when we say that a solution has been implemented? Is this the end of the process or is there more to do? What is a post implementation review and what do we do with it?

      • Incidence rates

What is the difference between incidents and incidence? How do we define incidence, and why do most people calculate it incorrectly? What do we mean by populations at risk? How does incidence relate to prevalence and why is it important to understand your outcome event? What should be quoted alongside incidence rates?

      • Incident cases

What is an incident case? Does everyone have the same risk? How do incident cases relate to incidence rates?

      • Independent Samples

What are independent samples? How do they relate to research / analytical designs? What sort of statistical tests can be used on independent samples? What needs to be taken into account?

      • Independent samples t-test

What is the independent samples t-test? When should it be used? What do we men by independent samples? Is this test always appropriate? What is the t-distribution and how does it relate to the normal distribution? Why is it sometimes called the Student t-test?

      • Independent variables

What is an independent variable? What sort of models are we looking at if we are talking about independent and dependent variables? How is a dependent variable different? How do these relate to confounders?

      • Indicator - definition

What do we mean by an indicator. How is it different to a metric? What do we mean by the underlying construct of the indicator?

      • Indicator construction

How do you construct an indicator? What are the factors that need to be taken into account? Are complex indicators better than simple indicators?

      • Indirect standardisation

Calculated rates and ratios are often subject to different population structures. How can we adjust our data so that rates are comparable between populations. Can we use national population structures to adjust our data. Can this always be done? What should you standardise for? What is the difference between direct and indirect standardisation?

      • Inference

What do we mean by inference? What are the problems associated with it? How can we use statistics to give some certainty around inference?

      • Inferential statistics - definition

There are two categories of statistics, descriptive and inferential. What are inferential statistics? When do you use them and what do they tell you? What are the limitations of inferential statistics?

      • Influencing variables

What are influencing variables in research or statistical models? Are they independent variables or dependent variables? How do they relate to confounders?

      • Information standards

What is an information standard? Why are they necessary? How do they relate to interoperability? Are they different to data standards?

      • Informed consent

What do we mean by informed consent. How is this different to explicit consent? When do you need informed consent? How does this relate to data protection rules?

      • Integration (Time Series)

Time series data needs to be stationary before an ARIMA model can be built. How do we make our data stationary? What is differencing and how does it make our data stationary? How does an integrated model relate to differencing?

      • Intellectual property rights

What are intellectual property rights. How does IPR differ from copyright and patents? What needs to be done to ensure IPR isn't violated? Does IPR have monetary value?

      • Intention to treat

Intention to treat is crucial in research studies, particularly randomised controlled trials. What do we mean by intention to treat and why is it violated so often. What is the implication of intention to treat being violated? What can we do about missing observations? What do we mean by last observation carried forward?

      • Inter-quartile range

How can we measure dispersion, or spread of data? Do we have similar measures for different types of data? If not, what is the inter-quartile range and what data should it be used with?

      • Interoperability

Our data is stored in numerous systems, whether health, education financial etc. Often data needs to be transferred from one system to another. What are the problems with this? Are there any risks and how do we mitigate for these?

      • Interpreting regression output

There are many different regression models, all of which have different output when using statistical software. How do we interpret this, and what are the similarities and differences of different models?

      • Interpretevist paradigm

There are two different paradigms within research. These are the positivist and the interpretevist paradigm. What are these, and what is the difference between them. How do we decide which paradigm to follow?

      • Interval data

All data is either categorical or continuous. What is different between categorical and continuous data? How should we treat categorical data? What do we mean by interval and ratio data? What is the relationship between interval and ordinal data?

      • Intervention

Many research studies are based around an intervention. What is an intervention and what do we need to consider before the effect of an intervention is assessed?

      • Intervention group

What is an intervention group within a Randomised Controlled Trial. How should an Intervention group be treated, and what is its relationship to the comparison group? How does randomisation, blinding and intention to treat ensure that an intervention group is analysed appropriately

      • Inter observer reliability

What is reliability and why is it important? What is the difference between inter-observer and intra-observer reliability? How does it relate to the concepts of internal and external reliability?

      • Internal reliability

What is reliability and why is it important? What is the difference between internal and external reliability? How does it relate to the concepts of inter-observer and intra-observer reliability?

      • Intra observer reliability

What is reliability and why is it important? What is the difference between inter-observer and intra-observer reliability? How does it relate to the concepts of internal and external reliability?

      • Introduction (research proposal)

What is the purpose of an introduction within a research proposal. What sort of things should be included in the introduction and how does it differ from the background?

J

      • Judgmental forecasting

A lot of data within the NHS and Social Care is time series data. How do you forecast what might happen in the future given historical data? What forecasting models can you use? What is judgemental forecasting, and how does it differ from other forecasting methods? How does it relate to the Delphi method?

K

      • Kaplan Meier

What are Kaplan Meier survival curves? When should they be used? How do they relate to Cox's proportional hazards? Is the Kaplan Meier curve able to be calculated manually?

      • Kendall’s tau correlation

What is correlation? What tests should you use? How do you interpret specific coefficients. How do you calculate them? When should you use Kendall's Tau Correlation? How is it related to Spearman's correlation coefficient?

      • Knowledge : Private

What is knowledge, and why is it important to determine the type of knowledge we have. How does this relate to the scientific method? Why shouldn't we use private knowledge to inform our practice?

      • Knowledge : Professional

What is knowledge, and why is it important to determine the type of knowledge we have. How does this relate to the scientific method? What is the difference between professional knowledge and scientific knowledge?

      • Knowledge : Public

What is knowledge, and why is it important to determine the type of knowledge we have. How does this relate to the scientific method? What is the difference between public knowledge and professional knowledge?

      • Knowledge : Scientific

What is knowledge, and why is it important to determine the type of knowledge we have. How does this relate to the scientific method? What is scientific knowledge and why is it the only knowledge that should inform practice?

      • Kruskal-Wallis test

What is the kruskal wallis test? When should it be used? What is the comparative test for two groups? What are the assumptions within the test?

      • Kurtosis

L

      • Lag

What do we mean by lag when we are talking about time series data? Why is it necessary to include lag in forecast models and which models best minimise the play of lag?

      • Last observation carried forward

What do we mean by last observation carried forward? How does this relate to the idea of intention to treat?

      • Least squares

Least squares is a method used in a number of statistical tests including regression. What is the method of least squares and why is it important?

      • Leptokurtic distribution


      • Letter of invitation

      • Levene's statistic

      • Line charts

      • Line of best fit

      • Line of no effect

      • Linear regression

      • Linear trends

      • Literature

      • Literature review

      • Log transformations

      • Logistic regression

      • Logit

      • Longitudinal studies

M

      • Mann Whitney U test

      • Maximum likelihood

      • McNemar test

      • Measures of central tendency

      • Mean

      • Mean square error (MSE)

      • Measures of dispersion

      • Median

      • Mesokurtic distribution

      • Meta-analysis

      • Methodology (research proposal)

      • Metric - definition

      • Minimising discomfort (ethics)

      • Mode

      • Moving averages

      • MSTS models

      • Multiple independent groups - tests

      • Multiple linear regression

      • Multiple related groups - tests

      • Multiplicative ETS models

      • Multinomial logistic regression

      • Multi-variate analysis

N

      • Naïve method

      • Natural cause variation

      • Nominal data

      • Non-linear trends

      • Non-parametric tests

      • Normal distribution

      • Normality test

      • Null hypotheses

      • Number - definition

      • Numbers needed to treat

O

      • Observational studies

      • Odds

      • Odds ratio

      • One-tailed hypotheses

      • One way and two way Anova test

      • OPCS codes

      • Oral presentations

      • Ordering data

      • Ordinal data

      • Out of control processes

      • Outcome measurement overview

      • Outliers

      • Overlapping confidence intervals

P

      • P values

      • Paired samples t-test

      • Parallel lines test

      • Parametric tests

      • Partial auto-correlation function (PACF)

      • Pearson's product moment correlation

      • Percentages and rates

      • Percentiles

      • Perverse incentives and gaming

      • Phenomenology

      • PICO approach

      • Pie Charts

      • Piloting

      • Placebo effect

      • Platykurtic distribution

      • Positivist paradigm

There are two different paradigms within research. These are the positivist and the interpretevist paradigm. What are these, and what is the difference between them. How do we decide which paradigm to follow?

      • Post-hoc power

Statistical power is important within research. It is related to sample size, play of chance and clinical differences. What is power, and when should a post-hoc power be calculated?

      • Post-hoc tests

      • Poster presentations

      • Power

Statistical power is important within research. It is related to sample size, play of chance and clinical differences. What is power, and when should an a-priori power be calculated. How does this relate to post-hoc power?

      • Power transformations

      • Presentation methods

      • Prediction intervals

      • Prevalence rates

      • Principles of Good indicators

      • Private knowledge

      • Probability

      • Procedure codes

      • Process capability

      • Process control and regression models

      • Process control charts

      • Professional knowledge

      • Project assumptions

      • Project board

      • Project closure

      • Project dependencies

      • Project feasibility

      • Project finance and resources

      • Project initiation

      • Project live running (control)

      • Project management overview

      • Project management tools

      • Project manager

      • Project planning

      • Project review

      • Project risks

      • Project sponsor

      • Project staff

      • Project team

      • Proportional hazards

      • Prospective studies

      • Protection of participants (ethics)

      • Public knowledge

Q

      • Quality indicators

      • Quartiles

      • Questionnaires

      • Questionnaire design principles

      • Question : Analysis

      • Question : Research

      • Qualitative Methods

      • Quantitative Methods

      • Quintiles

      • Quota Sampling

R

      • R-squared values

      • Random walk

      • Randomisation

      • Randomised controlled trials (RCT)

      • Range

      • Ranking indicators and metrics

      • Rate : Incidence

      • Rate : Prevalence

      • Ratio data

      • Ratios

      • READ codes

      • Reductionism

      • Regression equations

      • Regression methods and process control

      • Repeated measures ANOVA

      • Relative risk

      • Relative risk reduction

      • Relevance in data quality

      • Reliability

      • Reliability : External

      • Reliability : Inter observer

      • Reliability : Internal

      • Reliability : Intra observer

      • Replicable code (SPSS)

      • Retrospective Studies

      • Research Aims

      • Research and Audit difference

      • Research application procedures

      • Research definition

      • Research designs overview

      • Research design strengths and weaknesses

      • Research ethics

      • Research language overview

      • Research lifecycle

      • Research methodology

      • Research objectives

      • Research proposal

      • Research protocol

      • Research question

      • Research regulations overview

      • Residual variation

      • Resources and finance

      • Results : interpretation

      • Results : presentation

      • Risk

      • Role of voice in presentation

      • Rolling averages

      • Root mean square error (RMSE)

      • Run Charts

S

      • Sample size

      • Sensitivity

      • Shewhart funnel plot

      • Specificity

      • Sampling error

      • Sampling : cluster

      • Sampling : convenience

      • Sampling methods overview

      • Sampling :quota

      • Sampling : snowball

      • Sampling : stratified

      • Satisfaction surveys

      • Scatter plots

      • Scepticisim

      • Scheffe test

      • Scientific knowledge

      • Scientific method

      • Seasonal method

      • Seasonal variation

      • Secular trend

      • Semantic interoperability

      • Service development definition

      • Significance

      • Simple database setup (SPSS)

      • Simple database manipulation (SPSS)

      • Simple linear regression

      • Simple outcome measures

      • Simple randomisation

      • Skewness

      • Smoothing techniques

      • Snomed CT (R)

      • Snowball sampling

      • Solver (MS Excel)

      • Sources of bias

      • Spearman's rank correlation

      • Special cause variation

      • SPSS simple database setup

      • SPSS simple dataset manipulation

      • SPSS analysis

      • SPSS complex data manipulation

      • SPSS replicable code

      • Standard deviation

      • Standard error

      • Standard Z distribution

      • Standardised procedures

      • Standardisation : direct

      • Standardisation : indirect

      • Stationarity

      • Statistical hypotheses

      • Statistical power

Statistical power is important within research. It is related to sample size, play of chance and clinical differences. What is power, and when should an a-priori power be calculated? How does this relate to a post-hoc power?

      • Statistical process control

      • Statistical significance

      • Statistical tables

      • Statistical tests for 2 groups

      • Stem and Leaf plots

      • Stratified sampling

      • Stratification

      • Students T distribution

      • Sum of squares

      • Survey design

      • Survival curves

      • Systematic Review

T

      • Tables

      • Taboos of the community (ethics)

      • TBATS overview

A high level overview of the TBATS method of time forecasting, currently only available within the statistical software R

      • Terminology

Much of the data that we deal with (particularly in the NHS and Social Care) is classification data. Yet we ar moving toward recording information using terminology (namely Snomed CT). What is terminology data? How does it differ from classifications? What examples do we have? How do we analyse this type of data?

      • Test : One way or two way ANOVA

      • Test: Bonferonni

One of a number of post-hoc tests. Bonferroni is used following analysis of variance (ANOVA). Why are these types of tests needed? How do they relate to t-tests?

      • Test: Breusch Godfrey

Autocorrelation often occurs n data series, particularly time series. What is the Breusch Godfrey test and how does it relate to autocorrelation?

      • Test : Chi Square

Based on the chi-square distribution. What is the chi-square test? How many independent groups can it be used on? What is the role of Fisher's test? How do you calculate by hand?

      • Test : Cochrane's Q

Cochrane's Q is a non-parametric test for related groups. How is it calculated? What distribution is it based on? How many groups can it be used on? How does it relate to the McNemar test?

      • Test : Durbin Watson

      • Test: Fisher

      • Test : Friedman

      • Test : Independent samples t

      • Test : Kruskal Wallis

      • Test : Levene

      • Test : Mann Whitney U

      • Test : McNemar

      • Test : Paired samples t

      • Test : Parallel lines

      • Test : Repeated measures ANOVA

      • Test : Scheffe

      • Test : Tukey

      • Test : Wilcoxon signed ranks

      • Time series data - overview

      • Timeframes

      • Timeliness in data quality

      • Triangulation

      • TREND function (MS Excel)

      • True effect (of intervention)

      • True zero in ratio data

      • Tukey test

      • Two-tailed hypotheses

      • Type I errors

      • Type II errors

U

      • Understanding your audience

      • Uni-variate analysis

V

      • Validated measures

      • Validation sample

      • Validity

      • Variables

      • Variance

      • Variation

W

      • Waterfall projects

      • Weighting data

      • White noise

      • Wilcoxon signed ranks test

      • Winsorisation

XYZ

      • Z distribution

      • Z score

      • Z test for means

      • Z test for proportions

      • Z transform

0-9

      • 2 independent groups - tests

      • 2 related groups - tests

      • 3 or more independent groups - tests

      • 3 or more related groups - tests

      • 3D presentation

      • 95% certainty

      • 95% confidence