8.processing

Processing &

Analysis of data D.A. Asir John Samuel, MPT (Neuro Paed),

Lecturer, Alva’s college of Physiotherapy,

Moodbidri

Dr.Asir John Samuel (PT), Lecturer, ACP

Processing operations

• Editing

• Coding

• Classification

• Tabulation


Editing

• Process of examining the collected raw data

• Editing is done to assure that data are

accurate, consistent with other facts gathered,

uniformly entered, as complete as possible

• Field editing

• Central editing Dr.Asir John Samuel (PT), Lecturer, ACP

Field editing

• Review of reporting forms by the investigator

for completing, translating or rewriting

• Individual writing styles

• On the very next day or on the next day

• Not correct errors of omission by simply

guessing Dr.Asir John Samuel (PT), Lecturer, ACP

Central editing

• Take place when all forms or schedules have

been completed and returned to fitness

• Correct errors such as an entry in wrong place,

wrong month, and the like

• Respondent can be contacted for clarification

• No bias


Coding

• Process of assigning numerals or other

symbols to answers

• Should be appropriate to research problem

under consideration

• Necessary for effective analysis

• Extraction of data


Classification

• Large volume of raw data is reduced into

homogeneous group

• Arranging data in groups or classes on basis of

common characteristics

• Classification according to attributes

• Classification according to class-intervals


Tabulation

• Arranging in concise and logical order

• Summarising raw data and displaying in

compact form

• Orderly arrangement of data in columns and

rows


Tabulation is essential because of

• Conserves space and reduces explanatory and

descriptive statement to a minimum

• Facilitates process of comparison

• Facilitates summation of items and detection

of errors and omissions

• Basis for various statistical computations Dr.Asir John Samuel (PT), Lecturer, ACP

Problems in processing

• Problem concerning “Don’t Know” responses

• Use of percentages


Problem concerning “Don’t Know” responses

• When DK group is small, it is of little significance

• In big group, it becomes mater of concern

• Actually may not know the answer or

• Researcher may fail in obtaining appropriate

information (failure of questioning process)

• Keep as a separate category in tabulation Dr.Asir John Samuel (PT), Lecturer, ACP

Use of percentages

• 2/more percentages must not be averaged

unless each is weighted by group size

• Too large percentages should be avoided

because difficult to understand and confuse

• Hide base value

• Real differences may not be correctly read

• Can never exceed 100 percent and for decrease


Statistics in Medical Research

• Documentation of medical history of disease,

their progression, variability b/w patient,

association with age, gender, etc.

• Efficacy of various types of therapy

• Definition of normal range

• Epidemiological studies


Statistics in Medical Research

• Study the effect of environment, socio-

economic and seasonal factors

• Provide assessment of state health in

common, met and unmet needs

• Success/failure of specific health programme

• Promote health legislation

• Evaluate total health programme of action Dr.Asir John Samuel (PT), Lecturer, ACP

Statistics in Medical Research - Limitation

• Does not deal with individual fact

• Conclusion are not exact

• Can be misused

• Common men cannot handle properly


Normal distribution

• Represented by a family of infinite curves

defined uniquely by 2 parameter the mean

and the SD of the population

• The curve are always symmetrically bell

shaped. The width of the curve is defined by

population, SD


Normal distribution

• Mean, median and mode coincide

• It extends from - ∞ to + ∞

• Symmetrically about the mean

• Approx 68% of distribution is within 1SD of

mean (68.27%)

- 95% - 2SD (1.96 SD)

- 99% - 3SD (2.58 SD) Dr.Asir John Samuel (PT), Lecturer, ACP

Normal distribution

• The total area under the curve is 1

• The value of measure of skewness is zero. It is

not skewed

• The curve is asymptotic. It approaches but

never touches baseline at extremes

• The curve extends on the both sides -3σ

distance on left to +3σ distance on the right


Normal distribution - Uses

• Construct confidence interval

• Many statistical techniques makes an

underlying assumption of normality

• Distribution of sample means is normal

• Normality is important in statistical inference


Skewness

• Measure of lack of symmetry in a distribution

• Positive skewed

- Right tail is longer

- Mass of distribution is concentrated on left

side

- Distribution is said to be right skewed


Negative skewed

• Left tail is longer

• Mass of distribution concentration on right

side

• Distribution is said to be left skewed

• Value of skewness is 0 for normal distribution


Kurtosis

• Measure of degree of peakness in distribution

• For normal distribution, value of kurtosis is 3

• Leptokurtic – High peakness

• Mesokurtic – normal

• Platykurtic – Low peakness


Descriptive statistics

• Measures of location

- Central tendency

- Mean, median and mode

• Measures of variation

- Dispersion

- Range, quartile, IQR, variance and SD Dr.Asir John Samuel (PT), Lecturer, ACP

Mean

• Sum of all observation divided by total no. of

observation


Mean - merits

• Well understood by most people

• Computation of mean is easy

• More stable

• All items in a series are taken into account

• Used in further statistical calculation

• Good basis for comparison Dr.Asir John Samuel (PT), Lecturer, ACP

Mean - Demerits

• Affected by extreme values

• Cannot be computed by mere observation

• Not suitable for skewed distribution

• May not be an actual item

• Not in qualitative data


Median

• Middle most observation when data is

arranged in ascending/descending order of

magnitude

• Divides number into 2 halves such that no.of

items below it is same as no.of items above


Median

Odd = n+1/2

Even = n/2 + (n+1)/2

2


Median - Merits

• Widely used measures of CD

• Not influenced by extreme values

• Can be determined if extremes are not known

• Not a typical representation of series

• Useful for skewed distribution


Median - Demerits

• When no. of items are small, median may not

be representative

• It is effected by frequency of neighboring

items

• Not a typical representation of series


Mode

• Most frequently occurring observation in data

• If all values are different then no mode


Mode - Merits

• Can be computed by mere observation

• Simple

• Precise

• Less time consuming

• Less strain


Mode - Demerits

• Not an amenable to further algebraic

treatment

• Not rigidly defined

• Affected by no. of frequency of items


Measures of Dispersion (variation)

• Range

• Interquartile range

• Variance

• Standard Deviation


Range

• Difference between largest and smallest value

Range = Largest no. – Smallest no.


Quartile

• Value that divide data into 4 equal parts when

data is arranged in ascending order

Q1 = (n+1/4)th ordered observation

Q1 = [2(n+1)/4]th ordered observation

Q3 = [3(n+1)/4]th ordered observation


Interquartile range

• Provides range which covers middlemost 50%

of observation

• Good measures of dispersion if there are

extreme values

IQR = Q3 – Q1


Variance

• Sum of squares of difference of each

observation from mean, divided by n-1

Variance = 𝜀 𝑥−𝑥 2

𝑛−1


Variance - Merits

• Easy to calculate

• Indicate the variability clearly

• Most informative


Variance - Demerits

• Units of expression of variance is not the same


Standard Deviation (SD)

• Square root of variance

SD = √𝜀 𝑥−𝑥 2

𝑛−1


Standard Deviation - Merits

• Most widely used

• Used in calculating standard error


Standard Deviation -Demerits

• Lengthy process

• Gives weightage to only extreme valves


8.processing

Education

asir john samuel pt

normal distribution

normal distribution

right dr

rightside distribution

leftside distribution

bias dr

normal normality