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Intro To Health Statistics - Coggle Diagram

- - - - Usually informed by: Lack of existing evidence - Contradicting existing evidence - Low quality existing evidence
      - Informs: Research Methodologies - Hypotheses
    - - FINER: Feasible - Interesting - Novel - Ethical - Relevant
        
        Feasible: how anxious are students in public schools in hte UK on average (you cant measure the whole UK, pick a specific group in a given context you can collect a reliable average from
        
        Interesting: are you investigating something which constibutes in a significant way to the wider bosy of work?
        
        Novel: Do not just recycle well established topics
        
        Ethical: is your research worth the effect is has on particpants ], always get ethical approval
        
        Relevant: will this work be useful within a larger sociocultural context?
      - PICOT: Population - Intervention - Comparator - Outcome - Time Frame
        
        Population: Target population - Broad/Narrow - Exclusions - Access
        
        Intervention: Treatment / Test / Procedure - Standard Care / Novel - Alternative: Exposure
        
        Comparator: Is there one? - Ethics
        
        Outcome: Primary - Secondary - Safety - Qualitative / Quantitative - Objective / Subjective
        
        Time: When is the outcome mostrelevant? - Prevalence - Feasibility considerations
        
        Examples:
        
        “Does adding surgical resection improve 5-yearsurvival in children with neuroblastoma comparedwith chemotherapy alone?”
        “Are patients on antidepressants better able toperform activities of daily living?”
      - SMART: Specific - Measurable - Achievable - Relevant - Timely
        
        Specific: amke sure you're measuring one particular aspect of the topic in depth to get useful findings
        
        Measurable: can you feasibly measure this variable in an objective way given the design and measurement tools at hand?
        
        Achievable: Make sure you are researching something which can be altered, is observable and falsifiable
        
        Relevant: will this work be useful in the larger context
        
        Timely: check when you are researching a topic is appropriate, longitudinal work must be very well planned and consisent
    - - Continuous:
        
        Height (cm) / Weight (kg) - Time to healing (days) - Age (years) - Income (£) - Validated questionnaire scale (points)
      - Categorical:
        
        Gender (Male, Female) - Marital Status (Married, Single, Divorced) - Education (Primary, Secondary) - Disease Severity (Mild, Moderate, Severe) - Group (Intervention, Control)
  - - - The practice of evidence-based medicine means integratingindividual clinical expertise with the best available externalclinical evidence from systematic research
      - The first and earliest principle of evidence-based medicineindicated that a hierarchy of evidence exists
      - In other words, not all evidence is the same
    - - Case studies, anecdote, bench studies and personal opinion
      - Observational studies (cohort and case control)
        
        Clinical research can be broadly classified into experimental andobservational studies
        
        What are the differences between observational studies andexperimental studies?
        
        Observational studiesThis is where the exposure of a subject occurs naturally and is'observed' or recorded by the investigator
        
        Experimental studiesIn an experimental study (intervention study) the exposure is'assigned' to the patient by the investigator
      - Other controlled clinical trials
      - RCTs
        
        The primary study at the top of the pyramid is the RCT
        
        RCTs are considered the ‘gold standard’ single study design
        
        Most rigorous way of exploring cause-effect relationships○ Between the treatment (intervention) and outcome and for assessingcost effectiveness
        
        If done properly, RCTs are powerful tools but not always possible - Ethical and practical concerns
        
        Some study designs do not involve formal randomisation (quasi-experimental)
        
        Often used to inform public health practice and where randomisationis difficult to achieve
        
        However, they cannot rule out the possibility that the association wascaused by a third factor linked to both the intervention and outcome
      - Systematic reviews and meta-analyses of RCTs
        
        Systematic Revies Continued:
        
        Use explicit, systematic methods to collate and synthesise findings of studies that address a clearly formulated question
        
        They are useful for confirming current practices, guiding decision-making and informing future research
        
        Meta-analyses, while often part of systematic reviews, are not interchangeable with them
        
        They use statistical analysis to combine data from the studies found in the systematic review
        
        Studies must be homogenous (similar enough) that the data from them can be pooled together
        
        Since SR are focused on a clearly formulated question, their conclusions only answer that question and cannot be generalised and importantly they are only as reliable as the studies they include
      - Continued: RCTs are ranked above observational studies, while expert opinion and anecdotal experience are ranked at the bottom
        
        Systematic review and meta-analysis (secondary research) is placed above RCTs
        
        This is because systematicreviews combine data from multiple RCTs
        
        Remember the higher up the hierarchy that a study comes, the morelikely it is that the study can minimise the impact of bias on the resultsof the study
        
        You can think of this in a slightly different way - the higher up the top of the hierarchy the more certain we are of the results
        
        The hierarchy of evidence is a core principle of Evidence Based Health Care because it ranks study types based on the strength and precision of their research methods
        
        There is some criticism of HoE as a well-designed study lower down the pyramid should be ranked higher than a poorly designed RCT
        
        It is important to design good studies
        
        You need to understand about different types of study designs so thatyou can decide:
        
        Which type of study design is best able to answer your question
        You know the level and strength of evidence from a particular studydesign
        
        One of the problems is that not all research questions can be answered through a RCT either because of ethical issues or for practical issues
        
        Let’s not forget that even when evidence is available from RCTs, it might also be that evidence from other study designs is also relevant
- - - - Sometimes questions may lead by implying that an answer is foolish.– Do you have an unreasonable fear of heights?
      - A leading question might start with a piece of apparently factual information:– Most people think that medical statisticians are grossly underpaid. Do you agree?
      - A group of women who had just had a cervical smear were asked:– Do you understand the importance of having a smear test?– Unsurprisingly, 118/120 respondents said “yes”
    - - Hedges (1978) reports several examples of theeffects of varying the wording of questions.
      - He asked two groups of people one of the following: – Do you feel you take enough care of your health, or not? – Do you feel you take enough care of your health, or do you think you could take more care of your health?
      - 82% “took enough care” in response the firstquestion and 68% for the second.
      - Another set of questions were asked: – Do you think a person of your age can do anything to prevent ill-healthin the future or not? – Do you think a person of your age can do anything to prevent ill-healthin the future, or is it largely a matter of chance?
      - Recorded by age (overall percentage decreased with increased age), but second questions were, on average, always answered significantly lower
      - The second question is ambiguous, as it is quite possible to think that health is largely a matter of chance but that there is still something one can do about it.– Only if it is totally a matter of chance is there nothing one can do
      - Types of Ambiguity
        
        overlapping categories:
        
        The following comes from a questionnaire abouthealth checks in general practice:– When was your check-up? (Tick one answer only): Less than one month ago, 1 to 6 months ago, 6 to 12 months ago
        
        Respondents who had a check-up 6 months agowould find it difficult to answer this question.
        
        Another type of ambiguity occurs when we want to ask about numbers presented as ranges.
        
        multiple questions in one
        
        Sometimes people try to ask lots of things in onequestion - They confuse two (or more) questions in one.
        
        For example, would you prefer your smear to betaken by: – A female doctor – A male doctor – A nurse– I don’t mind
        
        The preference for a female and the preferencefor a doctor are mixed together.
    - - Sometimes the respondents may interpret the question in adifferent way depending on who answers the question.
      - For example, when parents and school children were askedthe same question:
        – Do you (does your child) usually cough first thing in themorning? - Schoolchildren 3.7%; Parents 2.4%
        – Do you (does your child) usually cough at other times in the dayor night? - Schoolchildren 24.8%; Parents 4.5%
      - The symptoms all showed relationships to the child’ssmoking and other potentially causal variables, and also toone another.
    - - For example, a sample of secondary school childrenwere asked two questions in the same questionnaireand whether they agreed with the statement:
        
        – Smoking causes lung cancer 85%
        
        – Smoking is not harmful 41%
      - The negative statement smoking is not harmful mayhave confused the children or they may not see canceras harmful
        
        A second sample of children were asked:– Smoking causes lung cancer 90%– Smoking is bad for your health 91%
      - A third sample of schoolchildren were asked: – What is meant by the term ‘lung cancer’?
        
        • Understand 13%
        
        Do not know / don’t understand 32%
        
        They all knew that lung cancer was caused bysmoking however.
    - - Sometimes we want to ask people how oftenthey do things. For example, within a hospital a questionnaireasked:– How often have you visited the audio-visualservice?
        
        Frequently - Often - Rarely - Never
        
        If you were completing this question, thenwould you think frequently was more or lessthan often?
  - - - Should be avoided if at all possible– They make the questionnaire difficult forrespondents to complete
    - - There is a large amount of information to gather – The study is difficult to explain – There is likely to be a problem of literacy among therespondents
      - It is good to explore these things in a pilot study
- - - - Design
        
        176 women with low-risk pregnancies
        
        • Attending 2 public hospital-based antenatal clinics in Sydney, Australia
        
        • Intervention: 2-day antenatal education programme plus standard care
        
        • Control: standard care alone
        
        • Primary outcome: epidural use
      - Objective: • In this study they wanted to find out if the antenatal education programme reduced epidural use compared to the standard care group
        
        21/88 (23.9%) women in the intervention group received an epidural
        
        • 57/83 (68.7%) women in the control group received an epidural
        
        • So, what should we do?
    - - Output: Also called a contingency table or cross classification
      - • Called 5 by 2 table or 52 table; In general, r  c table
      - Chi-squared test of association
        
        • We find for each cell the frequency which we would expect if
        the null hypothesis were true
        
        • Null hypothesis: no association between the two variables
        
        • Alternative hypothesis: an association of some type
        
        • We use the row and column totals to do that
        
        • Proportion who are premature = 99/1443 = 6.86%
        
        • Proportion of full-term deliveries = 1344/1443 = 93.14%
        
        • Proportions should be the same for each category of housing if null hypothesis is true, i.e., no association between variables
        
        Expected Frequencies
        
        We can calculate expected frequencies based on proportions
        
        • Out of 899 owner occupiers, expect 899 x 99/1443 = 61.7 to be
        
        premature deliveries if the null hypothesis were true
        
        Hypothesis testing
        
        In general, the expected frequency if null hypothesis is true =
        
        row total × column total / grand total
        
        Comparing observed and expected frequencies
        
        If null hypothesis true, and samples are large enough, this is an observation from a chi-squared distribution, often written χ2
        
        • For a contingency table, the degrees of freedom are given by:
        (number of rows – 1) × (number of columns – 1)
        
        p value = significance of association
    - - The chi-squared statistic is not an index of the strength of the association
      - • If we double the frequencies, this will double chi-squared, but the strength of the association is unchanged
      - • The test statistic follows chi-squared distribution provided the expected values are large enough
      - • It is a large sample test so the smaller the expected values become, the more dubious will be the test
      - The conventional criterion: the chi-squared test is valid if
        
        • at least 80% of the expected frequencies exceed 5
        
        • AND all the expected frequencies exceed 1
    - - • Set up the null hypothesis and its alternative
      - • Do a test/check any assumptions of the test
      - • Find the value of the test statistic
      - • Refer the test statistic to a known distribution which it would follow if the null hypothesis were true
      - • Find the probability of a value of the test statistic arising which is as or more extreme than that observed, if the null hypothesis were true
      - • Conclude that the data are consistent or inconsistent with the null hypothesis
    - - 8 expected frequencies ≥ 5 => 8/10 = 80% and all above 1 - Valid assumptions for Chi-squared test
      - Post-hoc Tests (if assumptions are not met)
        
        • The conventional criterion: the chi-squared test is valid if
        
        • at least 80% of the expected frequencies exceed 5
        
        • AND all the expected frequencies exceed 1
        
        • We could combine or delete rows and columns to give bigger expected values.
        
        • Obviously, this cannot be done for 2 x 2 tables.
        
        • If the table does not meet the criterion even after reduction, then we can use a different test.
      - Fisher's Exact Test
        
        Calculate the probability of every possible table with the given row and column totals.
        
        • We then sum the probabilities for all tables as or less probable than the observed.
        
        • Only used to be used for small samples in 2 by 2 tables, because of computing problems but can be used with any sample size.
        
        • When the table has more than two rows or columns or large frequencies, this can be a very large number of tables.
    - - • We would have got the same value of chi-squared whatever the order of the rows
      - • The test ignores the natural ordering
      - • We can look for a trend from one end of the table to the other
      - • SPSS does the Mantel-Haenszel linear-by-linear association chi-squared test, whether you want it or not
      - We assign numerical values to categories.
        
        E.g.: Considerable improvement =1,
        
        Moderate or slight improvement =2,
        
        No material change = 3,
        
        Moderate or slight deterioration = 4,
        
        Considerable deterioration =5,
        
        Death =6.
        
        • We then say, given these numerical scales, is there a relationship
        
        X2 (MH) = (n-1) x r2 (r = correlation coefficient)
        
        Statistic follows Χ2 distribution with 1 degree of freedom
      - Observations of association
        
        • Should be valid even when the standard chi-squared test is not, provided we have at least 30 observations
        
        • Can be significant even when the standard chi-squared test is not
        
        • It gives a more powerful test against a more restricted null hypothesis
- - - - Nominal: categories are not odered (physical features, conditions etc)
      - Ordinal: Categories have a natural order (e.g. stages of cancer)
    - - Integer Values: whole numbers (e.g. number of children)
      - Values take any numbers within a range: (e.g. body weight)
    - - qualitites or characteristics which can be attributed to a given sample or condition and are the primary "currency" of research
  - - - Cumulative frequency: Number of individuals with values
        less than or equal to a category
      - Relative cumulative frequency: Proportion of individuals
        with values less than or equal to a category
  - - - Frequency distributions of continuous quantitative variables are most commonly depicted by a histogram
      - • Intervals are on a horizontal axis with vertical bars representing the count (or proportion) of individuals for that interval
      - • No spaces between bars unless there are no observations for an interval
    - - As most of the values occur only once, counting the number of occurrences does not help
      - • Instead: divide the scale into class intervals, e.g. from 3.0 to 3.5, from 3.5 to 4.0 etc.
      - • Class intervals must not overlap, convention to put the higher boundary point of an interval into the next one, i.e. boundary value of 3.5 is part of the 3.5 – 4.0 interval, not 3.0 – 3.5
    - - Averages
        
        Mean
        
        • Mean = sum of observations / number of observations
        
        • Can be affected by individual outliers
        
        Arithmetic average
        
        The Median
        
        The central value of the distribution, where half the data are below this value and half the data above it
        
        • Middle observation for odd samples and average of two central observations for even samples
        
        • Less affected by outliers than the mean
        
        Mode
        
        The most common observation
        
        • Quick and easy to compute
        
        • Unaffected by extreme scores
        
        • Distributions can have more than one mode (e.g. bimodal)
      - Interpreting Histograms
        
        For symmetric data, Mean = Median = Mode (symmetrical histogram)
        
        Central tendency for positively skewed data: mode > median > mean (histogram peak left)
        
        Central tendency for negatively skewed data: mode > median > mean (histogram peak right)
      - Variance and Standard Deviation
        
        Variance: Average of squared differences from the mean
        
        Standard Deviation: Average difference from the mean (square root of the variance)
      - Percentiles and Quartiles
        
        The pth percentile is the value of the observation such that p% are less than or equal to it, different ways to calculate this
        
        Quartiles divide the data into four equal parts (25% each)
        
        Expressed on a histogram - percentiles shown on box plot
        
        box max/min shows Q3 and Q1 respectfully
        
        Outer lines show max and minimum values
        
        in-box line shows median
        
        Outliers are data points that are more than 1.5 times (extreme: more than 3 times) the height of the box (IQR) away from the edges of the box (Q1 or Q3)
- - - - • It is not the probability that the null hypothesis is true
      - • The null hypothesis is either true or it is not; it is not random and has no probability
    - - • Suppose we take a probability of 0.01 or less as constituting reasonable evidence against the null hypothesis
      - • If the null hypothesis is true, we shall make a wrong decision one in a hundred times
      - • The conventional approach is to say that differences are significant if the probability is less than 0.05
      - • If we decide that the difference is significant, the probability is sometimes referred to as the significance level
  - - - • For example, we may look at the effect of a drug, given for some other purpose, on blood pressure and find it significantly raises blood pressure by an average of 1 mm Hg
      - • A rise in blood pressure of 1 mm Hg is not clinically significant, so, although it may be there, it does not matter
    - - • ‘Not significant’ does not imply that there is no effect
      - • It means that we have failed to demonstrate the existence of one
  - - - • Each point in a scatter diagram representing one subject
    - - Correlation enables us to measure the closeness to a linear relationship
      - The correlation coefficient is based on the products of differences
        from the mean of the two variables
        
        • For each observation we subtract the mean for that variable
        
        • Multiply the deviations from the mean for the two variables for a subject together
        
        • Add them
        
        • Sometimes this is called the sum of products about the mean
      - To see how correlation works, we can draw two lines on the scatter diagram:
        
        • A horizontal line through the mean strength and A vertical line through the mean height
        
        • Products in top right and bottom left quadrants Mean positive
        
        Products in top left and bottom right quadrants negative
        
        If the sum of products is positive, then correlation positive
        
        • Sum of products negative (i.e. most of points in the –ve quadrants)
    - - Correlation coefficient, r
      - • Min value: -1 and max value: +1
      - • Also known as:
        
        • Pearson’s correlation coefficient
        
        • Product moment correlation coefficient
      - Calculated by dividing sum of products by square root of sum of squares
        
        • Square root of sum of squares:
        
        • For each observation we subtract the mean for that variable
        
        • Square each difference
        
        • Add them up
        
        • Multiply the two summed values together
        
        • Take the square root of this value
        
        Perfect correlation, r=+1 (+ve) or r=-1 (-ve) - Points on scatter graph will be in a striaght line
        
        No linear relationship, r=0 - scatter graph will have no straight shape
        
        It is possible for r to be equal to 0 when there is a relationship which is not linear - Line will be curved (bell curve, -ve or +ve)
    - - Not meeting assumptions for a given significance test means the p value is very unreliable and will require alternative test methods
      - Normality - histogram output
      - Linear - scatter graph output
      - If Assumptions are not met
        
        Spearman's Ro Test
        
        • We have highlighted that one of the assumptions for the Pearson’s correlation coefficient is Normality of one of the variables
        
        • If this assumption is not met, then we can use an alternative correlation coefficient
        
        • First, we rank the observations then calculate the product moment correlation of the ranks
        
        • Rather than the observations themselves
        
        • The resulting statistic has a distribution which does not depend on the distribution of the original variables
        
        • Usually denoted as ρ or rs
        
        The ranks for the two variables are found and then apply the formula for the product moment correlation to these ranks:
- - - - Design
        
        66 patients received total hip arthroplasty
        
        60 patients received resurfacing arthroplasty•
        
        Primary endpoint: Hip functioning at 12 months after surgery(Harris Hip Score)
      - Results
        
        Total hip arthroplasty
        
        Before surgery: Mean=50.1 (SD=13.5)
        
        After 12 months: Mean=82.3 (SD=21.4)
        
        Resurfacing arthroplasty
        
        Before surgery: Mean=48.6 (SD=14.2)
        
        After 12 months: Mean=88.4 (SD=15.8)
      - Conducting Stats Tests - Behind the Scenes
        
        Quantify the observed relationship between two variables intoa test statistic (observed value) from a known statisticaldistribution
        
        Calculate the probability (p-value) of the observed value to bea plausible observation from the sampling distribution that thetest statistic would form if H0 were true
        
        Declare the relationship statistically significant if the observedvalue is greater than the ‘critical value’ of that distribution atwhich the p-value becomes less than 0.05 (i.e. the evidenceagainst H0 becomes stronger)
      - Analysis Write Up
        
        Give descriptive summary, including thedirection and magnitude of any observedrelationship between variables
        
        Quote relevant test output and comment onstatistical significance
        
        Answer the research question!
        
        Comment on test assumptions of the testthat was performed
    - - Mean Harris Hip Score at 12 months
        
        Total hip arthroplasty: 82.3
        
        Resurfacing arthroplasty: 88.4
        
        Could be illustrated by adjacent box plots
      - Hypotheses about hip functioning in the population
        
        H0: Hip functioning is the same for the two treatments
        
        HA: Hip functioning is different between the two treatments
      - Group Difference
        
        Observed Mean Difference = 6.04 score points
        
        H0: Mean Difference = 0
        
        How can we know the likelihood (p-value) of 6.04 if H0 is true?
  - - - Normal distribution with mean of 0 and SD of 1
      - 1z = 1 Standard Deviation (of a single sample distribution)
      - 1z = 1 Standard Error (of the distribution of many samples)
    - - The standard error of the mean (SEM or SE) is an estimator of thepopulation Standard Deviation
      - Measure of uncertainty around the mean
      - Affected by the size and variability of the sample (s.e. = SD / sqrt(n) fora single group estimate)
      - Importance
        
        Area under the curve calculations for sampling population
        
        Confidence Intervals
    - - Sampling distribution of mean group differences if thedifference was truly zero (H0) - Scores shown on bell-curve line graph (x-axis centre = 0)
      - 0 +/- z-score = the value requires for a p value of .05 - determins statistical significant difference between two mean values
      - Transformations to hip score - z-score x standard error
      - Observed score is within plausible range of H0 - If observed difference z-score does not fall within significance paremeters (2.5% at low and high extremes) then the difference is not significant
  - - - For 118 degrees of freedom (63 + 57 – 2), critical t = 1.98 for 5% significance level
      - Confidence Intervals (CIs)
        
        A confidence interval for a statistic (e.g. mean) is a range of values that is likely to contain the value of that statistic for the population
        
        Calculated as the mean ± critical statistic (e.g. critical t) * standard error of the mean
        
        Standard: 95% Confidence Interval
        
        Confidence limits are values that state the boundaries of the confidence interval
        
        If the interval excludes zero (i.e. upper and lower limit are both negative or upper and lower limit are both positive), the mean difference can be assumedto be statistically significant
        
        Application
        
        Can be applied to group differences or group means
        
        Often graphically displayed as ‘whiskers’ (NOT a box plot)
        
        Overlapping confidence intervals suggest non-significant test results
  - - - Independence Not Met:
        
        Scenarios
        
        Measuring a variable before and after an intervention or eventor time period
        
        Matched pairs (e.g. parent-child, or individuals matched on avariable)
        
        Implications
        
        Observations are correlated, i.e. no longer independent
        
        No between-subject differences within each comparison,therefore smaller standard error of group differences
        
        t-distribution still applies, but degrees of freedom for only onegroup• Larger observed t-values (because smaller standard error)
        
        Same t-distribution comparison as assumptions met method
    - - Independence: “As data come from a survey of different individuals, wecan assume that observations are independent.”
      - Normality: “Based on a histogram of the variable / comparison of mean andmedian, the variable is approximately normal / positively skewed /negatively skewed.”
      - Equality of Variances: “Based on the similar / different standarddeviations of the variable in the two groups and a non-significant /significant Levene’s test, the variances can / can’t be assumed to be equal
    - - Pairs of observations are independent - Check study design, we know there were 128 independentparticipants
      - Paired differences are normally distributed - Calculate difference between paired observations and plot forexample on a histogram
- - - - Variability within groups - Deviations between each observation and group (treatment) mean
      - Variability between groups - Deviations between each group (treatment) mean and the overall mean
    - - Sum of squared deviations
      - Total Variation (SST) = SSB (Between SS) + SSW (Within SS)
    - - The ratio between SSB and SSW is a representation of the size of any treatment effect
        
        large ratio -> suggests group differences
        
        small ratio / ratio close to 1 -> group means are the same as total mean, i.e. no difference
      - Interpretation of relative size of this ratio depends on number ofparticipants and number of groups (represented by degrees offreedom)
        
        df(between groups) = Number of groups – 1
        
        df(within groups) = Total sample size – number of groups
      - Statistic of interest
        
        Statistic = ( SSB / dfbetween ) / ( SSW / dfwithin )
        
        Follows the F distribution for df between and df within
      - The F-Distribution: Output of df test
        
        Family of distributions shown on line chart - the shape depends on two parameters
        
        DF1 and DF2 set parameters
        
        the critical F value on the chart shows where the ANOVA output value where the p-value is.05
- - - - Note the sample size for each variable observed
      - Check large differences in frequencies between categories
      - Output: cluter bar chart
    - - Assumptions are likely to be violated:
        
        Expected frequencies for uncommon categories will be very low
        
        In this example: 40% of expected cell counts < 5 and minimumexpected cell count = 0.14
      - Tests may not be meaningful:
        
        There is not enough information in our example to draw anyinference about mixed ethnicities, but the statistical test will tryand incorporate this
        
        This may distort any real relationships in the remaining data
      - Solutions: (always note any tests or alterations performed to alter raw data)
        
        Drop/remove categories (with very little sample size)
        
        Example: this would meet the Chi Squared assumption
    - - Sometimes it may be desirable to treat data collected in categories asa continuous variable
      - Conditions:
        
        The categories MUST follow an order (e.g. lowest to highest)
        
        Categories must be approximately equally spaced (conceptually)
        
        There should be a minimum of 4 or more categories
      - Possible Reasons
        
        Easier analysis / interpretation
        
        Compensate for categories with few participants
      - If data are collected as Likert scales, this is usually done with theintention to analyse data continuously (but you do not have to)
  - - - Generally, we would avoid turning continuous data into categories, aswe almost always lose information
      - However, this is useful if
        
        There are known categories of interest (e.g. BMI groups, cut-offs forclinical diagnosis)
        
        The data are severely skewed
      - Always justify your choice of categories, this should be independent ofthe statistical significance of any tests
    - - Many tests are reasonably forgiving when it comes to non-normalcontinuous data (e.g. t-test / ANOVA)
      - Sometimes, there are alternative ‘non-parametric’ tests that do no relyon normality (e.g. Spearman’s correlation)
      - We could abandon the continuous nature of a variable entirely andgroup it into categories (e.g. old vs young)
      - Or, we could transform a variable!
      - This does not mean we manipulate the data to achieve a desired testresult, but rather we create a version of a variable that will allow for avalid test to be carried out
      - Interpretation not always straightforward however!
  - - - Sometimes we want to derive a new variable of interest from multipleexisting variables
      - E.g. for calculating known metrics, such as standard height, weight or BMI
      - What variable(s) could you derive from these questions rather thananalysing each activity separately?
        
        Qualitative Data:
        
        You need to seperate qualitative categories into quantitative variables (e.g. exercise (yes/no) or cooking (yes/no)
        
        Be aware of bias within quantified qualitative variables