Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Statistics in orthodontics by Indian dental aca... 324 views
- Statistics in orthodontics by Indian dental aca... 45 views
- Nutrition part1 /certified fixed or... by Indian dental aca... 784 views
- Nutrition /certified fixed orthodon... by Indian dental aca... 605 views
- Bio statistics1 by Indian dental aca... 907 views
- Statistical tests/prosthodontic cou... by Indian dental aca... 590 views

2,020 views

Published on

The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.

Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,

Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call

0091-9248678078

No Downloads

Total views

2,020

On SlideShare

0

From Embeds

0

Number of Embeds

2

Shares

0

Downloads

13

Comments

0

Likes

7

No embeds

No notes for slide

- 1. Statistical tests and their relevance to Dental Research INDIAN DENTAL ACADEMY Leader in continuing dental education www.indiandentalacademy.com www.indiandentalacademy.com
- 2. Contents Introduction Descriptive statistics Tests of significance Parametric test Non Parametric test Application of tests in dental research Conclusion Bibliography www.indiande ntalacademy.c om
- 3. Introduction Normal BP – 120/80 mm Hg Europeans are taller than Asians Average male adult weighs 70kgs Drug A is better than drug B - Endless Cannot be arrived by just Raw data Numbers tell tales – Speak the language of STATISTICS – Adds meaning to data – helps to interpret data Thus lending “significance” to the study www.indiande ntalacademy.c om
- 4. Descriptive statistics STATISTICS – Is the science of compiling, classifying & tabulating numerical data and expressing the results in a mathematical or graphical form. OR Statistics is the study of methods & procedures for collecting, classifying, summarizing & analyzing data & for making scientific inferences from such data. www.indiande - Prof P.V.Sukhatme ntalacademy.c om
- 5. BIOSTATISTICS –Is the branch of statistics applied to biological or medical sciences (biometry). OR - Is that branch of statistics concerned with mathematical facts and data relating to biological events. www.indiande ntalacademy.c om
- 6. VARIABLE – A general term for any feature of the unit which is observed or measured. FREQUENCY DISTRIBUTION – Distribution showing the number of observations or frequencies at different values or within certain range of values of the variable. www.indiande ntalacademy.c om
- 7. Mean Dividing the total of all observations by the number of observations x1 + x 2 + x 3 + .... + xn ∑ x x= = n n Eg. calculate the mean of DMFT scores 2.3, 2.0,2.7,3.0,2.0. 2.3 + 2.0 + 2.7 + 3.0 + 2.0 12 x= = = 2 .4 5 5 www.indiande ntalacademy.c om
- 8. Geometric mean (GM) – nth root of the product ∑log x n GM = ( x1)( x 2 )( x3 )...( xn ) = n When the variation between the lowest and the highest value is very high, geometric mean is advised & preferred Harmonic mean (HM) – is the reciprocal of the arithmetic mean of the reciprocal of the observations 1 n HM = = 1 1 1 ∑ ∑ n xi xi www.indiande ntalacademy.c om
- 9. Median – is the middle value, which divides the observed values into two equal parts, when the values are arranged in ascending or descending order n + 1 2 Eg. calculate the median of DMFT scores 2.3, 2.0,2.7,3.0,2.0. arrange in asc order, 5 +1 = 6 rd value www.indiande 2.0,2.0.2.3,2.7,3.0 = =3 = ie 2.3 ntalacademy.c 2 om
- 10. Mode – is the value of the variable which occurs most frequently Mode = (3median – 2mean) Eg. calculate the mode of DMFT scores 2.3, 2.0,2.7,3.0,2.0. Mode = 2.0 Mode = 3 × 2.3 − 2 × 2.4 = 2.1 www.indiande ntalacademy.c om
- 11. Variance Is the appropriate measure of dispersion for interval or ratio level data Computes how far each score is from the mean This is done by ( x − x ) Each score will have a deviation from the mean, so to find the average deviation => we have to add all the deviations and divide it by number of scores (just like calculating mean) www.indiande ntalacademy.c om
- 12. ∑( x − x) i.e. N but....∑ ( x − x ) = 0 So to eliminate this zero, square the deviations which eliminates the (-) sign i.e. ( x − x)2 ∑ N = S2 - is the average of the squared deviations www.indiande ntalacademy.c om
- 13. Standard deviation(Root Mean Square deviation) Is defined as the square root of the arithmetic mean of the squared deviations of the individual values from their arithmetic mean SD = (σ ) = ∑( x − x ) N −1 SD = ( s ) = ∑( x − x ) N 2 2 For small samples For large samples www.indiande ntalacademy.c om
- 14. For frequency distribution SD = (σ ) = SD = ( s ) = ∑ f ( x −x) N −1 ∑ f ( x −x) N 2 2 For small samples For large samples www.indiande ntalacademy.c om
- 15. Uses of SD 1. Summarizes the deviations of a large distribution from mean in one figure used as unit of freedom 2. Indicates whether the variation from the mean is by chance or real 3. Helps finding standard error- which determines whether the difference b/n means of two samples is by chance or real 4. Helps finding the suitable size of the sample for valid conclusions www.indiande ntalacademy.c om
- 16. Standard error Standard deviation of mean values Used to compare means with one another Standard deviation SD SE = = sample size n www.indiande ntalacademy.c om
- 17. Coefficient of variation is a measure used to measure relative variability I.e, Variation of same character in two or more different series . (eg – pulse rate in young & old person) Variation of two different characters in one & same series . (eg – height & weight in same individual) Standard Deviation CV = ×100 Mean www.indiande ntalacademy.c om
- 18. Normal curve and distribution The histogram of the same frequency distribution of heights, with large number of observations & small class intervals – gives a frequency curve which is symmetrical in nature Normal curve or Gaussian curve www.indiande ntalacademy.c om
- 19. Normal curve x www.indiande ntalacademy.c om
- 20. Characteristics of normal curve Bell shaped Symmetrical Mean, Mode & Median – coincide Has two inflections – the central part is convex, while at the point of inflection the curve changes from convexity to concavity www.indiande ntalacademy.c om
- 21. On preparing frequency distribution with small class intervals of the data collected, we can observe 1. Some observations are above the mean & others are below the mean 2. If arranged in order, maximum number of frequencies are seen in the middle around the mean & fewer at the extremes decreasing smoothly 3. Normally half the observations lie above & half below the mean & all are symmetrically distributed on each side of mean A distribution of this nature or shape is www.indiande called Normal or Gaussian distribution ntalacademy.c om
- 22. Arithmetically mean ± 1SD limits , include 68.27% observations mean ± 2SD limits, include 95.45% observations mean ± 1.96 SD limits, include 95% observations mean ± 3SDlimits, includes 99.73% observations mean ± 2.58SDlimits, includes 99% observations www.indiande ntalacademy.c om
- 23. Normal curve and distribution 68.26% 95.46% x − 1SD x − 2 SD x x + 1SD x + 2 SD 99.72% x − 3SD x + 3SD www.indiande ntalacademy.c om
- 24. Height in cm 142.5 145.0 147.5 150.0 152.5 155.0 157.5 160.0(M) 162.5 165.0 167.5 170.0 172.5 175.0-177.5 frequency of each group 3 8 15 45 90 155 194 195 136 93 42 16 6 2 Mean – 160.0 Mean ±1SD 680 68% frequency with in height limits of Mean ±2SD 950 95% SD – 5cm Mean ±3SD 995 99% www.indiande ntalacademy.c om
- 25. Relative or standard normal deviate or variate – (Z) Is the deviation from the mean in a normal distribution or curve observation - mean x − x Z= = SD SD -in standard normal curve the mean is taken as zero & SD as unity of one www.indiande ntalacademy.c om
- 26. Skewness Skewness – as the static to measure the asymmetry coefficient of skewness is 0 Positively (right) skewed Negatively (left) skewed Bimodal www.indiande ntalacademy.c om
- 27. kurtosis Kurtosis – is a measure of height of the distribution curve Coefficient of kurtosis is 3 Leptokurtic(high) Platykurtic (flat) Mesokurtic (normal) www.indiande ntalacademy.c om
- 28. Tests of significance Population is any finite collection of elements I.e – individuals, items, observations etc,. Sample – is a part or subset of the population Parameter – is a constant describing a population Statistic – is a quantity describing a sample, namely a function www.indiande ntalacademy.c of observations om
- 29. Statistic (Greek) Parameter (Latin) Mean x µ Standard Deviation s σ Variance s2 σ Correlation coefficient r ρ Number of subjects n N 2 www.indiande ntalacademy.c om
- 30. Hypothesis testing Hypothesis H is an assumption about the status of a phenomenon or is a statement about the parameters or form of population Null hypothesis or hypothesis of no difference – States no difference between statistic of a sample & parameter of population or b/n statistics of two samples This nullifies the claim that the experiment result is different from or better than the one observed already Denoted by H0 www.indiande ntalacademy.c om
- 31. Alternate hypothesis – Any hypothesis alternate to null hypothesis, which is to be tested Denoted by H1 Note : the alternate hypothesis is accepted when null hypothesis is rejected www.indiande ntalacademy.c om
- 32. Type I & type II errors H 0 Accept H0 is true H1 is true H1 Accept No error Type I error Type II error No error Type I error = α Type II error = β When primary concern of the test is to see whether the null hypothesis can be rejected such test is called Test of significance www.indiande ntalacademy.c om
- 33. The probability of committing type I error is called “P” value Thus p-value is the chance that the presence of difference is concluded when actually there is none Type I error – important- fixed in advance at a low level – such upper limit of tolerance of the chance of type I error is called Level of Significance (α) Thus α is the maximum tolerable probability www.indiande of type I error ntalacademy.c om
- 34. Difference b/n level of significance & Pvalue LOS 1) Maximum tolerable chance of type I error 2) α is fixed in advance P-value 1) Actual probability of type I error 2) calculated on basis of data following procedures The P-value can be more than α or less than α depending on data When P-value is < than α results is statistically significant www.indiande ntalacademy.c om
- 35. The level of significance is usually fixed at 5% (0.05) or 1% (0.01) or 0.1% (0.001) or 0.5% (0.005) Maximum desirable is 5% level When P-value is b/n 0.05-0.01 = statistically significant < than 0.01= highly statistically significant Lower than 0.001 or 0.005 = very highly significant www.indiande ntalacademy.c om
- 36. Sampling Distribution Zone of Rejection H0 1/2 α Zone of Rejection H0 1/2 α Zone of Acceptance H0 x − 1.96 SD x x + 1.96SD Confidence interval Confidence limits – 95% www.indiande ntalacademy.c om
- 37. Tests of significance Are mathematical methods by which the probability (P) or relative frequency of an observed difference, occurring by chance is found Steps & procedure of test of significance – 1. State null hypothesis H 0 2. State alternate hypothesis H1 3. Selection of the appropriate test to be utilized & calculation of test criterion based on type of test www.indiande ntalacademy.c om
- 38. 4. Fixation of level of significance 5. Select the table & compare the calculated value with the critical value of the table 6. If calculated value is > table value, H 0 is rejected 7. If calculated value is < table value, H 0is accepted 8. Draw conclusions www.indiande ntalacademy.c om
- 39. TESTS IN TEST OF SIGNIFICANCE Parametric (normal distribution & Normal curve ) Quantitative data Student ‘t’ test 1) Paired 2) Unpaired Z test (for large samples) One way ANOVA Two way ANOVA Non-parametric (not follow normal distribution) Qualitative data 1) Z – prop test Qualitative (quantitative converted to qualitative ) 1. Mann Whitney U test 2. Wilcoxon rank test 3. Kruskal wallis test 4. Spearman test www.indiande ntalacademy.c om
- 40. Parametric Uses Non-parametric Paired t test Test of diff b/n Paired observation Wilcoxon signed rank test Two sample t test Comparison of two groups Wilcoxon rank sum test Mann Whitney U test Kendall’s s test One way Anova Comparison of several groups Kruskal wallis test Two way Anova Comparison of groups Friedmann test values on two variables Spearman’s rank Correlation Measure of association Correlation coefficient B/n two variable Kendall’s rank correlation www.indiande ntalacademy.c Normal test (Z test ) Chi square test om
- 41. Student ‘t’ test Small samples do not follow normal distribution as the large ones do => will not give correct results Prof W.S.Gossett – Student‘t’ test – pen name – student It is the ratio of observed difference b/n two mean of small samples to the SE of difference in the same www.indiande ntalacademy.c om
- 42. Unpaired ‘t’ test Types Paired ‘t’ test Actually, t-value Z-value of large samples, but the probability (P) of this is determined by reference ‘t’ table Degree of freedom (df)- is the quantity in the denominator which is one less than independent number of observations in a sample For unpaired ‘t’ test = n1 + n2 − 2 For paired ‘t’ test = n-1 www.indiande ntalacademy.c om
- 43. Criteria for applying ‘t’ test – Random samples Quantitative data Variable follow normal distribution Sample size less than 30 Application of ‘t’ test – 1. Two means of small independent sample 2. Sample mean and population mean 3. Two proportions of small independent samples www.indiande ntalacademy.c om
- 44. Unpaired ‘t’ test I) Difference b/n means of two independent samples Data – Group 1 Group 2 Sample size Mean SD n1 x1 n2 x2 SD1 SD2 1) Null hypothesis H 0 ⇒ ( x1 − x2 ) = 0 2) Alternate hypothesis H1 ⇒ ( x1 − x2 ) ≠ 0 www.indiande ntalacademy.c om
- 45. 3) Test criterion t= x1 − x2 SE ( x1 − x2 ) here SE of ( x1 − x2 ) is calculated by 1 1 SE of ( x1 − x2 ) = SD + n n 2 1 where SD = SE ( x1 − x2 ) = ( n1 −1) SD12 + ( n2 −1) SD22 n1 + n2 − 2 ( n1 − 1) SD12 + ( n2 − 1) SD22 1 n1 + n2 − 2 1 n + n 2 1 www.indiande ntalacademy.c om
- 46. 4) Calculate degree of freedom df = ( n1 − 1) + ( n2 − 1) = n1 + n2 − 1 5) Compare the calculated value & the table value 6) Draw conclusions www.indiande ntalacademy.c om
- 47. Example – difference b/n caries experience of high & low socioeconomic group Sl no Details High socio economic group Low socio economic group I Sample size n1 = 15 n2 = 10 II DMFT x1 = 2.91 x2 = 2.26 III Standard deviation SD1 = 0.27 SD2 = 0.22 x1 − x2 SE ( x1 − x2 ) 0.65 = = 6.34, 0.1027 t0.001 = 3.76 ∴tc > t0.001 t= There is a significant difference df = 23 www.indiande ntalacademy.c om
- 48. T table www.indiande ntalacademy.c om
- 49. Other applications II) Difference b/n sample mean & population x−µ mean t= df = n − 1 SE = SD n III) Difference b/n two sample proportions t= p1 − p2 1 1 PQ + n n 2 1 n1 p1 + n2 p2 where P = n1 + n2 Q = 1− P df = n1 + n2 − 2 www.indiande ntalacademy.c om
- 50. Paired ‘t’ test Is applied to paired data of observations from one sample only when each individual gives a paired of observations Here the pair of observations are correlated and not independent, so for application of ‘t’ test following procedure is used1. 2. 3. Find the difference for each pair y1 − y2 = x Calculate the mean of the difference (x) ie x Calculate the SD of the differences & later SE SD SE = n www.indiande ntalacademy.c om
- 51. 4. Test criterion x −0 x t= = SE ( d ) SD( x ) n 5. Degree of freedom df = n − 1 6. Refer ‘t’ table & find the probability of calculated value 7. Draw conclusions www.indiande ntalacademy.c om
- 52. Example – to find out if there is any significant improvement in DAI scores before and after orthodontic treatment Sl no DAI before DAI after Difference Squares 1 30 24 6 36 2 26 23 3 9 3 27 24 3 9 4 35 25 10 100 5 25 23 2 4 20 158 Total www.indiande ntalacademy.c om
- 53. ∑ x = 20 = 4 Mean x = n 5 sum of squares, ∑ ( x − x ) = ( 6 − 4 ) + ( 3 − 4 ) + ( 3 − 4 ) + (10 − 4 ) + ( 2 − 4 ) 2 SD = ( x − x )2 ∑ 2 2 2 = 4 + 1 + 1 + 36 + 4 = 46 = 46 = 11.5 = 3.391 4 n −1 SD 3.391 ∴SE = = =1.5179 n 5 x 4 ∴c = t = = 2.6352 SE 1.5179 but t 0.5 = 2.78 ∴ c < t 0.5 t 2 Hence not significant df = n −1 = 4 www.indiande ntalacademy.c om 2
- 54. Z test (Normal test) Similar to ‘t’ test in all aspect except that the sample size should be > 30 In case of normal distribution, the tabulated value of Z at - 5% level = Z 0.05 = 1.960 1% level = Z 0.01 = 2.576 0.1% level = Z 0.001 = 3.290 www.indiande ntalacademy.c om
- 55. Z test can be used for – 1. Comparison of means of two samples – x1 − x2 Z= SE ( x1 − x2 ) where SE ( x1 − x2 ) = ( SE 1 2 + SE2 2 ) 2 SD12 SD2 = n + n 2 1 2. Comparison of sample mean & population mean Z = x −µ SD 2 n www.indiande ntalacademy.c om
- 56. 3. Difference b/n two sample proportions Z= p1 − p2 1 1 PQ + n n2 1 n1 p1 + n2 p2 where P = n1 + n2 Q = 1− P 4. Comparison of sample proportion (or percentage) with population proportion (or percentage) Z= p−P 1 PQ n Where p = sample proportion P = populn proportion www.indiande ntalacademy.c om
- 57. Analysis of variance (ANOVA) Useful for comparison of means of several groups Is an extension of student’s ‘t’ test for more than two groups R A Fisher in 1920’s Has four models 1. 2. 3. 4. One way classification (one way ANOVA ) Single factor repeated measures design Nested or hierarchical design Two way classification (two way ANOVA) www.indiande ntalacademy.c om
- 58. One way ANOVA Can be used to compare likeEffect of different treatment modalities Effect of different obturation techniques on the apical seal , etc,. www.indiande ntalacademy.c om
- 59. 1 Groups (or treatments) 2 Individual values i k x11 x12 x21 xi1 xk 1 x22 xi 2 x1n x2 n xin xk 2 xkn n n n n T2 Ti Tk S2 x2 Si Sk xi www.indiande k ntalacademy.c om Calculate No of observations Sum of x values Sum of squares Mean of values Τ1 = x11 + x12 + ... + x1n S1 = ( x11 ) + ( x12 ) + .. + ( x1n ) 2 x1 = 2 T1 n 2 x
- 60. ANOVA table Sl Source Degree Sum of squares no of of variation freedom I Between Groups II With in groups III Total k − 1 ∑i ( xi − x ) n − k ∑ ∑ (x i ij j n −1 ∑ ∑ (x i j ij 2 − xi ) Mean sum of squares F ratio or variance ratio 2 T2 2 = ∑i xi − 2 ∑i ( xi − x ) N SB = k −1 2 2 SB ( k − 1, N − k ) 2 SW T 2 xij2 − ∑ i i T ∑i∑ j 2 = ∑i ∑ j xij −∑i i 2 ni SW = ni N−k 2 T 2 2 − x ) = ∑i ∑ j xij − N 2 ST2 = ∑∑ i T2 x − 2 j ij N −1 N www.indiande ntalacademy.c om
- 61. ANOVA www.indiande ntalacademy.c om
- 62. Example- see whether there is a difference in number of patients seen in a given period by practitioners in three group practice Practice Individual values A C B 268 387 161 349 264 346 328 423 324 209 254 293 292 239 Calculate No of observations (n) 5 4 5 Sum of x values 1441 1328 1363 Sum of squares 426899 462910 393583 Mean of values 288.2 332.0 www.indiande 272.6 ntalacademy.c om
- 63. Between group sum of squares ( ∑ x ) + ( ∑ x ) + ( ∑ x ) − ( ∑ x + ∑ x +∑ x ) = 2 2 A 2 B nA C nB A B 2 C n A + nB + nC nC = 8215.71 Total sum of squares = ∑x + x + x 2 A 2 B 2 C (∑ x − A + xB + xC ) 2 n A + nB + nC = 63861.71 With in group sum of squares = total SS - between SS = 55646.0 www.indiande ntalacademy.c om
- 64. ANOVA table Source of Degree of variation freedom I Between Groups 3 −1 = 2 II With in groups 14 − 3 = 11 III Total 14 − 1 = 13 F = 0.81 Sum of squares Mean sum of squares F ratio or variance ratio 8215.71 8215.71 4107.86 = 4107.86 = 0.81 2 5088.73 55646 = 5088.73 11 55646.0 Sl no 63861.71 F0.05 = 3.98 df = ( 2,11) Because FC < FT , there is no significant difference in the number of patients attending 3 different practice www.indiande ntalacademy.c om
- 65. Further, any particular pair of treatments can be compared using SE of difference b/n two means Eg – xd & xc 1 1 SE ( xd − xc ) = MSE + n nc d & difference xd − xc may be tested by using ' t' test criterion xd − xc t= SE ( xd − xc ) www.indiande ntalacademy.c om
- 66. Two way ANOVA Is used to study the impact of two factors on variations in a specific variable Eg – Effect of age and sex on DMFT value www.indiande ntalacademy.c om
- 67. Sample values blocks Treatments i x11 x21 x31 ii .. x12 x22 n Sample size Total Mean value sample size Total xk 1 k T1 x1 x32 xk 2 k T2 x2 x1n x2 n x3n xkn k Tn xn n n n n nk = N T1 T2 T3 Tk x1 x3 xk x2 Mean value T x www.indiande ntalacademy.c om
- 68. 2 way ANOVA table Sl no Source I Blocks II Treatments III Residual or error IV Sum of squares SSblocks SStreatments Degree of freedom n −1 k −1 SS residual ( n − 1)( k − 1) Total SStotal Mean sum of squares (MSS) MS blocks SS blocks = n −1 Variance ratio F F1 = MSblocks MS residual SStreatments F = MS treatment MStreatments = 2 MS residual k −1 SS residual MS residual = ( n − 1)( k − 1) ( nk − 1) = N − 1 F1 = variance ratio of blocks with df of ( n − 1) Vs ( n − 1)( k − 1) www.indiande ntalacademy.c F2 = variance ratio of treatment' s with df of ( k − 1) Vs ( n − 1)( k − 1) om
- 69. Multiple comparison tests 1. Fisher’s procedure – student’s ‘t’ test 2. Least significant difference method (LSD) Just like student’s ‘t’ test To test significant difference b/n two groups or variable means 1. Scheffe’s significant difference procedure Is applicable when groups having heterogeneous variance or variations 1. Tukey’s method For comparison of the differences b/n all possible pairs of treatments or group means www.indiande ntalacademy.c om
- 70. 5. Duncan’s multiple comparison test – For all comparisons of paired groups only 6. Dunnet’s comparison test procedure – For comparison of one control and several treatment groups www.indiande ntalacademy.c om
- 71. Non parametric tests Here the distribution do not require any specific pattern of distribution. They are applicable to almost all kinds of distribution Chi square test Mann Whitney U test Wilcoxon signed rank test Wilcoxon rank sum test Kendall’s S test Kruskal wallis test Spearman’s rank correlation www.indiande ntalacademy.c om
- 72. Chi square test By Karl Pearson & denoted as χ² Application 1. Alternate test to find the significance of difference in two or more than two proportions 2. As a test of association b/n two events in binomial or multinomial samples 3. As a test of goodness of fit www.indiande ntalacademy.c om
- 73. Requirement to apply chi square test Random samples Qualitative data Lowest observed frequency not less than 5 Contingency table Frequency table where sample classified according to two different attributes 2 rows ; 2 columns => 2 X 2 contingency table r rows : c columns => rXc contingency table χ =∑ 2 (O − E) E 2 O – observed frequency E – expected frequency www.indiande ntalacademy.c om
- 74. Steps 1. State null & alternate hypothesis 2. Make contingency table of the data ( r × c) 3. Determine expected frequency by r×c E= N ( total frequency) 4. Calculate chi-square of each by- χ 2 (O − E) = E 2 www.indiande ntalacademy.c om
- 75. 5. calculate degree of freedom df = ( c −1)( r −1) 6. Sum all the chi-square of each cell – this gives chi-square value of the data χ =∑ 2 (O − E) 2 E 7. Compare the calculated value with the table value at any LOS 8. Draw conclusions www.indiande ntalacademy.c om
- 76. Example from a dental health campaign School Oral hygiene Total G F- P Below avg 62 (85.9) 103 (93.0) 57 (45.2) 11 (8.9) 233 Avg 50 (43.9) 36 (47.5) 26 (23.1) 7 (4.6) 119 Above avg 80 (62.3) 69 (67.5) 18 (32.8) 2 (6.5) 169 Total E= F+ 192 208 101 20 521 r×c N ( total frequency) χ c2 = ∑ (O − E)2 E = 31.4 df = ( c −1)( r −1) = 2 × 3 = 6 Table χ t2 at P = 0.001 is 22.46 Hence significant difference www.indiande ntalacademy.c om
- 77. www.indiande ntalacademy.c om
- 78. Alternate formulae If we have contingency table a c a+c b d b+d a+b c+d a+b+c+d=N N ( ad − bc ) χ = with df = 1 ( a + b )( c + d )( a + c )( b + d ) 2 2 If one of the value is below 5 => Yate’s correction formula 2 N N ad − bc − 2 χ2 = ( a + b )( c + d )( a + c )( b + d ) with df = 1 www.indiande ntalacademy.c om
- 79. If the table is larger than 2X2, Yate’s correction cannot be applied – then the small frequency (<5) can be pooled or combined with next group or class in the table Chi square test only tells the presence or absence of association, but does not measure the strength of association www.indiande ntalacademy.c om
- 80. If degree of association as to be calculated then – ad − bc 1. Yule’s coefficient of association Q = ad + bc Y= 2. Yule’s coefficient of colligation 3. - ad − bc V= ( a + b )( a + c )( b + d )( c + d ) 1 − bc ad 1 + bc ad 4. Pearson’s coefficient of contingency C= χ2 N + χ2 www.indiande ntalacademy.c om
- 81. Wilcoxon signed rank test Is equivalent to paired ‘t’ test Steps Exclude any differences which are zero Put the remaining differences in ascending order, ignoring the signs Gives ranks from lowest to highest If any differences are equal, then average their ranks Count all the ranks of positive differences – T+ Count all the ranks of negative differences – Twww.indiande ntalacademy.c om
- 82. If there is no differences b/n variables then T+ & T_ will be similar, but if there is difference then one sum will be large and the other will be much smaller T= smaller of T+&T_ Compare the T value with the critical value for 5%, 2% & 1% significance level A result is significant if it is smaller than critical value www.indiande ntalacademy.c om
- 83. Example:Results of a placebo-controlled clinical trail to test the effectiveness of sleeping drug Patients 1 2 3 4 5 6 7 8 9 10 Sleep hrs Drug Placebo 6.1 5.2 7.0 7.9 8.2 3.9 7.6 4.7 6.5 5.3 8.4 5.4 6.9 4.2 6.7 6.1 7.4 3.8 5.8 6.3 Difference 0.9 -0.9 4.3 2.9 1.2 3.0 2.7 0.6 3.6 -0.5 Rank with signs + 3.5 -3.5 10 7 5 8 6 2 9 -1 50.5 -4.5 www.indiande ntalacademy.c om
- 84. Calculated T=-4.5 df =10, Table value at 5% (n=10)= 8 Cal T< table value, H0 is rejected We conclude that sleeping drug is more effective than the placebo www.indiande ntalacademy.c om
- 85. Mann Whitney U test Is used to determine whether two independent sample have been drawn from same sample It is a alternative to student ‘t’ test & requires at least ordinal or normal measurement n1 ( n1 + 1) U = n1n2 + − R1 or R2 2 Where, n1n2 are sample sizes R1 R2 are sum of ranks assigned to I & II group www.indiande ntalacademy.c om
- 86. Procedure All the observation in two samples are ranked numerically from smallest to largest without regarding the groups Then identify the observation for I and II samples Sum of ranks for I and II sample determined separately Take difference of two sum T =R1 - R2 www.indiande ntalacademy.c om
- 87. Comparison of birth weights of children born to 15 non smokers with those of children born to 14 heavy smokers NS 3.9 3.7 3.6 3.7 3.2 4.2 4.0 3.6 3.8 3.3 4.1 3.2 3.5 3.5 2.7 HS 3.1 2.8 2.9 3.2 3.8 3.5 3.2 2.7 3.6 3.7 3.6 2.3 2.3 3.6 Ranks assignments R1 26 23 16 21 8 29 27 17 24 12 28 10 15 13 R2 7 5 6 11 25 14 9 4 20 22 19 2 1 03 18 www.indiande ntalacademy.c om
- 88. Sum of R1= 272 and Sum of R2=163 Difference T=R1 – R2 is 109 The table value of T0.05 is 96 , so reject the H0 We conclude that weights of children born to the heavy smokers are significantly lower than those of the children born to the non-smokers (p<0.05) www.indiande ntalacademy.c om
- 89. Applications of statistical tests in Research Methods www.indiande ntalacademy.c om
- 90. One variable, two group sample problem www.indiande ntalacademy.c om
- 91. One variable, multiple group sample problem www.indiande ntalacademy.c om
- 92. Two variable problem Interested in relationship b/n two variables Use linear regression Yes Are both continuous variables No Is one variable continuous & other categorical No Yes Use analysis of variance Or Logistic regression Both variables categorical Use contingency table analysis www.indiande ntalacademy.c om
- 93. Multiple – variable problem Research interested in relationship B/n more than two variables Use multiple regression Or Multivariate analysis www.indiande ntalacademy.c om
- 94. Conclusion Statistics are excellent tools in research data analysis; how ever, if inappropriately used they may make the results of a well conducted research study un-interpretable or meaningless www.indiande ntalacademy.c om
- 95. Bibliography Biostatistics – Rao K Vishweswara, Ist edition. Methods in Biostatistics – Dr Mahajan B K, 5th edition. Essentials of Medical Statistics – Kirkwood Betty R, 1st edition. Health Research design and Methodology – Okolo Eucharia Nnadi. Simple Biostaistics – Indrayan,1st edition. Statistics in Dentistry www.indiande ntalacademy.c om – Bulman J S
- 96. www.indiande ntalacademy.c om

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment