algonquin college - jan ladas1 community dental health algonquin college

Algonquin College - Jan Ladas 1

Community DentalCommunity Dental HealthHealth

Algonquin CollegeAlgonquin College


Statistical SignificanceStatistical SignificanceParametric Statistical Testing: Statistical techniques that are based on

certain valid assumptions (normal distribution with equal variance) about the parameters of the population from which a large, randomized study sample was drawn.e.g.: - “t” test and “f” test

- their value = parametric statistic - data are interval or ratio scaled.


Statistical SignificanceStatistical SignificanceNon-Parametric: Less restrictive / distribution free Inferential techniques used when

distribution is skewed or bimodal or when little is known about the observations

Measurement scales are ordinal or nominal

Study sample is small / variables are discretee.g.: Chi Square – McNemar’s test for significance of changes. “Before and After”


Statistical SignificanceStatistical Significance Two Groups Research DesignsStudent’s “t” test for statistical significance: Compares the means of 2 groups to

determine if difference between them is real or a result of sampling fluctuation under conditions of ho (no difference)

Best if applied to results where 2 groups received different experimental treatments

Used when population standard deviation is not known


Statistical SignificanceStatistical Significance

E.G.: Test comparison to see if flavoured toothpaste affects brushing time(S1) 1 group given unflavoured paste(S2) 1 group given bubblegum flavourCompare mean brushing time for significant differenceHo : Mean of S1 = Mean of S2



Student “t” test formula: Uses mean, variance and s.d. to

calculate “t” values Uses tables with degrees of freedom

and compare with P value Relates to normal curve and distribution



Multiple Groups Research Design: Analyses of Variance Test for Statistical

Significance = ANOVA – f value The test to determine whether

differences in multiple group scores have occurred by chance (sample fluctuation) or by applied experimental manipulation


Statistical SignificanceStatistical SignificanceANOVA compares the variability in 2 ways:1. Between Group Variance (BGV): Referred to as treatment effect Reflects the magnitude of the

difference among the group means Number of d.f. for BGV is calculated by

the formula K – 1 where K stands for the number of groups


Statistical SignificanceStatistical Significance2. Within Group Variance (WGV): Referred to as residual effect Pooled variance of all the groups in the

design added together This variance represents the

uncontrolled, unexplained variance due to the chance effect


Statistical SignificanceStatistical Significance1. Formula for d.f. for WGV is N (Number of

subjects) – K (number of experimental groups)

2. f ratio calculated = BGV / WGV3. With d.f. calculated and f value, tables are

consulted to determine significance levelIf f value indicates significant differences, tests to determine areas of differences are used. Multiple t tests are not appropriate.


Statistical SignificanceStatistical SignificanceChi-square test: Compares observed and expected frequencies not

means Best used with discrete variables where subjects

can be assigned to mutually exclusive groupsE.G.: Males, females, smoking, non-smoking

Based on the normal curve and degree of freedom Chi-square value calculated and compared with

pre-set critical value before accepting / rejecting Ho.


CorrelationCorrelation Refers to the linear relationship

between two variables Statistical measure for determining the

strength of the linear relationship Based on the number of variables,

nature of variables (discrete or continuous) and the scale of measurement. (nominal, ordinal, interval and ratio)


Correlation AnalysesCorrelation Analyses

Used to determine the relationship between variables each of which can be measured for each individual in the samplee.g.: - height and weight

- profit and loss


Correlation AnalysesCorrelation AnalysesPlus 1 Positive Correlation:Value of one variable increases as value of

second variable increasesMinus 1 Negative Correlation:Value of one variable increases as value of

second variable decreases + or – sign indicates the direction of the

correlation # (number) indicates the strengthn.b.: the closer the value to +1.00 or –1.00, the

stronger the relationship. 0 = no relationship.


Correlation AnalysesCorrelation Analyses

The analysis yields a measure called: Correlation Coefficient known as Pearson’s r.

Measures the direction and strength of the relationship of the two variables to produce the numerical correlation ranging from –1.0 to +1.0


EpidemiologyEpidemiology The study of the amount, causes,

distribution and controls of diseases and health conditions among given populations.

It attempts to determine which associated factors are important for prevention and controle.g.: Colorado Brown Stain Study


Characteristics of Characteristics of EpidemiologyEpidemiology

1. Groups are studied, not individuals. “Well and ill”2. Disease is multifactorial3. A disease state depends on exposure to a specific agent,

strength of the agent, susceptibility of the host and environmental conditions

4. Factors:Host – age, race, ethnic background, physiologic state, gender, cultureAgent – chemical, microbial, physical or mechanical irritants, parasitic, viral or bacterialEnvironment – climate or physical environment, food sources, socioeconomic conditions


TheThe Uses of the Science Uses of the Science of Epidemiologyof Epidemiology

1.Description of normal biologic processes

Examples: stages of growth, blood groups, and times and order of tooth eruption.

2. Understanding the natural history of diseases. Observations of disease progression and outcome in populations have enabled investigators to distinguish those diseases that are fatal or disabling from those that will resolve uneventfully.



3.Distribution of disease in the population.

By age, gender, race, geographic region, and socioeconomic status. Demonstrates trends in disease prevalence and distribution. (Study of patterns among groups).

4.Studying non-disease entities.Suicide, injury.



5.Identifying the determinants of disease.

Specific study designs can identify the risk factors and risk indicators associated with a disease and can lead to intervention strategies for prevention and control.

6.Testing hypotheses for disease prevention and control. Dental example: the various uses of fluorides to reduce caries incidence.



7.Planning and evaluating health care services.

Data can be used to assist planning decisions on services and types of personnel required. Validates the effectiveness of treatment techniques and quality of treatment provided.


Epidemiologic Epidemiologic Studies/ResearchStudies/Research

Descriptive, Experimental, AnalyticEvery study/research, no matter how modest,

needs a Protocol. A written plan containing the purpose and

detailed operation of the study Helps it’s design Helps researchers to anticipate potential

problems Helps in writing final report because the

protocol forms the basis of the reportn.b.: essential for research with humans


Research ProcessResearch Process1. Choosing the research question = hypothesis2. Developing the protocol3. Pre-testing the protocol – pilot study = small

version of a proposed study4. Conducting the study5. Analyzing the findings – review data

(biostatistics)6. Disseminating the findings – distributing the

results to target population so new knowledge can be utilized to benefit others


The Essential Features of a The Essential Features of a Protocol for Research with Protocol for Research with

HumansHumans

1.Precise definition of the research problem, the reasons for undertaking the research, and the review of pertinent literature.

2.Objectives of the study, or hypotheses to be tested and refuted.

3.Population to be studied, including its selection, source, size, method of sampling, and method of allocation to groups (if a clinical trial).



HumansHumans

4. Data to be collected, describing each item needed to accomplish the objectives or to test the hypotheses.

5. Procedures to be carried out, how data will be obtained and by whom.

6. Data collection methods, with examples of all data collection forms or computer methods of data collection, and a list of all necessary supplies, equipment and instruments. Budget justifications.



HumansHumans

7.Plans for data processing analysis and statistical distributions to be examined.

8.Time schedule for planning, obtaining informed consent, data collection and analysis and report writing.

9.An assessment of any ethical issues involved and obtaining consent.

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