Enviar búsqueda
Cargar
Hm306 week 6
•
Descargar como PPTX, PDF
•
0 recomendaciones
•
5 vistas
B
BHUOnlineDepartment
Seguir
Hm306 week 6
Leer menos
Leer más
Educación
Denunciar
Compartir
Denunciar
Compartir
1 de 27
Descargar ahora
Recomendados
Sampling methods and sample size
Sampling methods and sample size
mdanaee
sampling methods
sampling methods
DanieBekele1
Sampling techniques
Sampling techniques
Narasimha B.C
SAMPLING AND SAMPLING ERRORS
SAMPLING AND SAMPLING ERRORS
rambhu21
Sampling and Sampling Distribution
Sampling and Sampling Distribution
Umesh Pandey
Survey Surveillance Screening
Survey Surveillance Screening
MalihaQuader1
Sampling Chapter No 10
Sampling Chapter No 10
Abdul Basit
Introduction to sampling
Introduction to sampling
Situo Liu
Recomendados
Sampling methods and sample size
Sampling methods and sample size
mdanaee
sampling methods
sampling methods
DanieBekele1
Sampling techniques
Sampling techniques
Narasimha B.C
SAMPLING AND SAMPLING ERRORS
SAMPLING AND SAMPLING ERRORS
rambhu21
Sampling and Sampling Distribution
Sampling and Sampling Distribution
Umesh Pandey
Survey Surveillance Screening
Survey Surveillance Screening
MalihaQuader1
Sampling Chapter No 10
Sampling Chapter No 10
Abdul Basit
Introduction to sampling
Introduction to sampling
Situo Liu
Sampling and sample size determination
Sampling and sample size determination
Remas Mohamed
Sampling
Sampling
Habib Gul
Brm sampling techniques
Brm sampling techniques
Sanchit Aggarwal
Sampling methods 16
Sampling methods 16
Raj Selvam
Sampling methods
Sampling methods
Sagar Gadekar
Sampling techniques in Research
Sampling techniques in Research
Pavithra L N
Sampling distribution concepts
Sampling distribution concepts
umar sheikh
CABT SHS Statistics & Probability - Sampling Distribution of Means
CABT SHS Statistics & Probability - Sampling Distribution of Means
Gilbert Joseph Abueg
Systematic sampling in probability sampling
Systematic sampling in probability sampling
Sachin H
Errors in research
Errors in research
AasthaBhatia18
Sampling design
Sampling design
Balaji P
Sampling and Inference_Political_Science
Sampling and Inference_Political_Science
Omar (TUBBS 128) Ventura VII
Research Method for Business chapter 10
Research Method for Business chapter 10
Mazhar Poohlah
Systematic ranom sampling for slide share
Systematic ranom sampling for slide share
IVenkatReddyGaaru
Sampling....
Sampling....
Nirbhay Singh
Sampling Design and Sampling Distribution
Sampling Design and Sampling Distribution
Vikas Sonwane
Research Method EMBA chapter 10
Research Method EMBA chapter 10
Mazhar Poohlah
Sample size determination
Sample size determination
Sathish Rajamani
2. sampling techniques
2. sampling techniques
Debasish Padhy
Introduction to Biostatistics and types of sampling methods
Introduction to Biostatistics and types of sampling methods
Dr. Sunita Ojha
Business research sampling
Business research sampling
Nishant Pahad
unit 10 Sampling presentation L- short.ppt
unit 10 Sampling presentation L- short.ppt
MitikuTeka1
Más contenido relacionado
La actualidad más candente
Sampling and sample size determination
Sampling and sample size determination
Remas Mohamed
Sampling
Sampling
Habib Gul
Brm sampling techniques
Brm sampling techniques
Sanchit Aggarwal
Sampling methods 16
Sampling methods 16
Raj Selvam
Sampling methods
Sampling methods
Sagar Gadekar
Sampling techniques in Research
Sampling techniques in Research
Pavithra L N
Sampling distribution concepts
Sampling distribution concepts
umar sheikh
CABT SHS Statistics & Probability - Sampling Distribution of Means
CABT SHS Statistics & Probability - Sampling Distribution of Means
Gilbert Joseph Abueg
Systematic sampling in probability sampling
Systematic sampling in probability sampling
Sachin H
Errors in research
Errors in research
AasthaBhatia18
Sampling design
Sampling design
Balaji P
Sampling and Inference_Political_Science
Sampling and Inference_Political_Science
Omar (TUBBS 128) Ventura VII
Research Method for Business chapter 10
Research Method for Business chapter 10
Mazhar Poohlah
Systematic ranom sampling for slide share
Systematic ranom sampling for slide share
IVenkatReddyGaaru
Sampling....
Sampling....
Nirbhay Singh
Sampling Design and Sampling Distribution
Sampling Design and Sampling Distribution
Vikas Sonwane
Research Method EMBA chapter 10
Research Method EMBA chapter 10
Mazhar Poohlah
Sample size determination
Sample size determination
Sathish Rajamani
2. sampling techniques
2. sampling techniques
Debasish Padhy
Introduction to Biostatistics and types of sampling methods
Introduction to Biostatistics and types of sampling methods
Dr. Sunita Ojha
La actualidad más candente
(20)
Sampling and sample size determination
Sampling and sample size determination
Sampling
Sampling
Brm sampling techniques
Brm sampling techniques
Sampling methods 16
Sampling methods 16
Sampling methods
Sampling methods
Sampling techniques in Research
Sampling techniques in Research
Sampling distribution concepts
Sampling distribution concepts
CABT SHS Statistics & Probability - Sampling Distribution of Means
CABT SHS Statistics & Probability - Sampling Distribution of Means
Systematic sampling in probability sampling
Systematic sampling in probability sampling
Errors in research
Errors in research
Sampling design
Sampling design
Sampling and Inference_Political_Science
Sampling and Inference_Political_Science
Research Method for Business chapter 10
Research Method for Business chapter 10
Systematic ranom sampling for slide share
Systematic ranom sampling for slide share
Sampling....
Sampling....
Sampling Design and Sampling Distribution
Sampling Design and Sampling Distribution
Research Method EMBA chapter 10
Research Method EMBA chapter 10
Sample size determination
Sample size determination
2. sampling techniques
2. sampling techniques
Introduction to Biostatistics and types of sampling methods
Introduction to Biostatistics and types of sampling methods
Similar a Hm306 week 6
Business research sampling
Business research sampling
Nishant Pahad
unit 10 Sampling presentation L- short.ppt
unit 10 Sampling presentation L- short.ppt
MitikuTeka1
Statr sessions 11 to 12
Statr sessions 11 to 12
Ruru Chowdhury
Sampling Theory
Sampling Theory
RVS Institute of Health Sciences, Sulur, Coimbatore
2RM2 PPT.pptx
2RM2 PPT.pptx
Ramesh Safare
Sampling
Sampling
Zaeem Jifri
Sampling techniques.pptx
Sampling techniques.pptx
JehanzebFurqan
Chapter_2_Sampling.pptx
Chapter_2_Sampling.pptx
SubodhPaudel6
chapter8-sampling-IoxO.pptx
chapter8-sampling-IoxO.pptx
BenjJamiesonDuag2
chapter8-sampling-IoxO.pptx
chapter8-sampling-IoxO.pptx
AliSher68
Methods.pdf
Methods.pdf
jiregnaetichadako
Probability & Non-Probability.pptx
Probability & Non-Probability.pptx
fakharmasood2
Sampling.pdf
Sampling.pdf
Cyra Rivera
AAU. Chapter.5 Sampling Methods.pptx
AAU. Chapter.5 Sampling Methods.pptx
hailemeskelteshome
Sampling.pptx
Sampling.pptx
YashikaGupta97
Maxfield_8e_PPT_Ch08.pptx
Maxfield_8e_PPT_Ch08.pptx
MarcCollazo1
Research method ch06 sampling
Research method ch06 sampling
naranbatn
Unit 9a. Sampling Techniques.pptx
Unit 9a. Sampling Techniques.pptx
shakirRahman10
How to do sampling?
How to do sampling?
Gautam Jayasurya
Sampling and sampling distribution
Sampling and sampling distribution
Ali Raza
Similar a Hm306 week 6
(20)
Business research sampling
Business research sampling
unit 10 Sampling presentation L- short.ppt
unit 10 Sampling presentation L- short.ppt
Statr sessions 11 to 12
Statr sessions 11 to 12
Sampling Theory
Sampling Theory
2RM2 PPT.pptx
2RM2 PPT.pptx
Sampling
Sampling
Sampling techniques.pptx
Sampling techniques.pptx
Chapter_2_Sampling.pptx
Chapter_2_Sampling.pptx
chapter8-sampling-IoxO.pptx
chapter8-sampling-IoxO.pptx
chapter8-sampling-IoxO.pptx
chapter8-sampling-IoxO.pptx
Methods.pdf
Methods.pdf
Probability & Non-Probability.pptx
Probability & Non-Probability.pptx
Sampling.pdf
Sampling.pdf
AAU. Chapter.5 Sampling Methods.pptx
AAU. Chapter.5 Sampling Methods.pptx
Sampling.pptx
Sampling.pptx
Maxfield_8e_PPT_Ch08.pptx
Maxfield_8e_PPT_Ch08.pptx
Research method ch06 sampling
Research method ch06 sampling
Unit 9a. Sampling Techniques.pptx
Unit 9a. Sampling Techniques.pptx
How to do sampling?
How to do sampling?
Sampling and sampling distribution
Sampling and sampling distribution
Más de BHUOnlineDepartment
Bi 117 week 1 ppt the bible as literature
Bi 117 week 1 ppt the bible as literature
BHUOnlineDepartment
ESL 0845L-OL Week 9 a usa government branches
ESL 0845L-OL Week 9 a usa government branches
BHUOnlineDepartment
ESL 0845L-OL Week 8 b the coca cola case
ESL 0845L-OL Week 8 b the coca cola case
BHUOnlineDepartment
ESL 0845L-OL Week 8 a organizational communication
ESL 0845L-OL Week 8 a organizational communication
BHUOnlineDepartment
ESL 0845L-OL Week 7 a jobs
ESL 0845L-OL Week 7 a jobs
BHUOnlineDepartment
ESL 0845L-OL Week 6 a health
ESL 0845L-OL Week 6 a health
BHUOnlineDepartment
ESL 0845L-OL Week 5 b modern manners
ESL 0845L-OL Week 5 b modern manners
BHUOnlineDepartment
ESL 0845L-OL Week 5 a community
ESL 0845L-OL Week 5 a community
BHUOnlineDepartment
ESL 0845L-OL Week 4 a products - sales presentation
ESL 0845L-OL Week 4 a products - sales presentation
BHUOnlineDepartment
ESL 0845L-OL Week 3 b symbols
ESL 0845L-OL Week 3 b symbols
BHUOnlineDepartment
ESL 0845L-OL Week 3 a consumption
ESL 0845L-OL Week 3 a consumption
BHUOnlineDepartment
ESL 0845L-OL Week 2 b generally speaking
ESL 0845L-OL Week 2 b generally speaking
BHUOnlineDepartment
ESL 0845L-OL Week 2 a money
ESL 0845L-OL Week 2 a money
BHUOnlineDepartment
ESL 0845L-OL Week 1 b success
ESL 0845L-OL Week 1 b success
BHUOnlineDepartment
ESL 0845L-OL Week 1 b relationships
ESL 0845L-OL Week 1 b relationships
BHUOnlineDepartment
ESL 0845L-OL Week 1 a introductions
ESL 0845L-OL Week 1 a introductions
BHUOnlineDepartment
ESL 0845L-OL Week 1 a family life
ESL 0845L-OL Week 1 a family life
BHUOnlineDepartment
ESL 0823L week 8 general interest in products
ESL 0823L week 8 general interest in products
BHUOnlineDepartment
ESL 0823L week 7 a job-interview-oneonone-activities-pronunciation-exercises-...
ESL 0823L week 7 a job-interview-oneonone-activities-pronunciation-exercises-...
BHUOnlineDepartment
ESL 0823L week 6 parts of-the-body-matter-7160
ESL 0823L week 6 parts of-the-body-matter-7160
BHUOnlineDepartment
Más de BHUOnlineDepartment
(20)
Bi 117 week 1 ppt the bible as literature
Bi 117 week 1 ppt the bible as literature
ESL 0845L-OL Week 9 a usa government branches
ESL 0845L-OL Week 9 a usa government branches
ESL 0845L-OL Week 8 b the coca cola case
ESL 0845L-OL Week 8 b the coca cola case
ESL 0845L-OL Week 8 a organizational communication
ESL 0845L-OL Week 8 a organizational communication
ESL 0845L-OL Week 7 a jobs
ESL 0845L-OL Week 7 a jobs
ESL 0845L-OL Week 6 a health
ESL 0845L-OL Week 6 a health
ESL 0845L-OL Week 5 b modern manners
ESL 0845L-OL Week 5 b modern manners
ESL 0845L-OL Week 5 a community
ESL 0845L-OL Week 5 a community
ESL 0845L-OL Week 4 a products - sales presentation
ESL 0845L-OL Week 4 a products - sales presentation
ESL 0845L-OL Week 3 b symbols
ESL 0845L-OL Week 3 b symbols
ESL 0845L-OL Week 3 a consumption
ESL 0845L-OL Week 3 a consumption
ESL 0845L-OL Week 2 b generally speaking
ESL 0845L-OL Week 2 b generally speaking
ESL 0845L-OL Week 2 a money
ESL 0845L-OL Week 2 a money
ESL 0845L-OL Week 1 b success
ESL 0845L-OL Week 1 b success
ESL 0845L-OL Week 1 b relationships
ESL 0845L-OL Week 1 b relationships
ESL 0845L-OL Week 1 a introductions
ESL 0845L-OL Week 1 a introductions
ESL 0845L-OL Week 1 a family life
ESL 0845L-OL Week 1 a family life
ESL 0823L week 8 general interest in products
ESL 0823L week 8 general interest in products
ESL 0823L week 7 a job-interview-oneonone-activities-pronunciation-exercises-...
ESL 0823L week 7 a job-interview-oneonone-activities-pronunciation-exercises-...
ESL 0823L week 6 parts of-the-body-matter-7160
ESL 0823L week 6 parts of-the-body-matter-7160
Último
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
YousafMalik24
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
navabharathschool99
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
Conquiztadors- the Quiz Society of Sri Venkateswara College
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptx
nelietumpap1
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
Nguyen Thanh Tu Collection
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERP
Celine George
ACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdf
SpandanaRallapalli
Karra SKD Conference Presentation Revised.pptx
Karra SKD Conference Presentation Revised.pptx
AshokKarra1
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
Humphrey A Beña
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Carlos105
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
Celine George
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
Conquiztadors- the Quiz Society of Sri Venkateswara College
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17
Celine George
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
Sarwono Sutikno, Dr.Eng.,CISA,CISSP,CISM,CSX-F
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
JhezDiaz1
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptx
MaryGraceBautista27
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
phamnguyenenglishnb
ENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choom
nelietumpap1
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
Anupkumar Sharma
Último
(20)
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptx
Q4 English4 Week3 PPT Melcnmg-based.pptx
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERP
ACC 2024 Chronicles. Cardiology. Exam.pdf
ACC 2024 Chronicles. Cardiology. Exam.pdf
Karra SKD Conference Presentation Revised.pptx
Karra SKD Conference Presentation Revised.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptx
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
ENGLISH6-Q4-W3.pptxqurter our high choom
ENGLISH6-Q4-W3.pptxqurter our high choom
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
Hm306 week 6
1.
© 2016© 2016 A
Practical Approach to Analyzing Healthcare Data Chapter 7 – Sample Selections
2.
© 2016 Types of
Studies - Descriptive • Descriptive studies – performed to generate hypotheses for more formal studies – Cross-sectional study – describes the characteristics of a population at a specific point in time • Often used for prevalence studies – Applied descriptive studies • Data mining • Exploratory data analysis
3.
© 2016 Types of
Studies - Analytic • Analytic studies – more formal studies designed to test a specific hypotheses – Case-control study – involves both a case group (subjects with the attribute under investigation) and a control group (those without the attribute) • Members of the case and control groups are often matched based on demographics • Typically a retrospective study • May not be used to determine cause and effect; can calculate odds ratio • Weakness – dependent of subject’s ability to recall events – Cohort studies – involves case and control group, but groups are identified before the study is performed • Prospective study • May not be used to determine cause and effect; can calculate relative risk • May take a long time to complete • Not useful if the attribute studied is rare
4.
© 2016 Types of
Studies - Experimental • Allow the determination of a cause and effect relationship between variables • Randomized Control Trials (RCT) – Used to determine the effectiveness of new drugs/treatment protocols • Blinded studies – Single blind – subject does not know if they are assigned to the case or control group – Double blind – neither subject nor the researcher know if they are assigned to the case or control group – Triple blind- subject, researcher and analytics are all blinded as to the group assignment of the subject
5.
© 2016 Why select
a sample? • Often population is too large to collect data from every unit of analysis or subject • Statistical inference is used to make conclusions about a population based on a sample • Vocabulary: – Population or universe – all subjects that are under study and eligible to be sampled – Sample – selected subset of the population – Sampling frame – A listing of all of the subjects in the population – Variable of interest – Quantity to be estimated (denial rate, coding error rate, overpayment, underpayment, etc)
6.
© 2016 Statistically Valid
Sample • Large enough to provide information with sufficient precision to meet the goals of the analysis • Probability sample where each item has an equal chance of being selected • Must be reproducible
7.
© 2016 Defining the
Variable of Interest • What is the percent of lab orders that are not signed by a physician during 2012? – Universe – all lab orders during 2012 • What is the amount over/under paid due to incorrect E/M level assignment during January? – Universe – • E/M services billed during January • E/M services provided during January • Must refine question to determine if billed date or service date should be used for defining the universe • What is the coding accuracy rate for secondary diagnosis codes on inpatient accounts during the first quarter? – Universe – • All secondary diagnoses coded during first quarter • All inpatient accounts during first quarter • Must refine question to determine if diagnosis codes or charts are the unit of analysis
8.
© 2016 Simple Random
Sampling • It is the statistical equivalent of drawing sampling units from a hat. • Each sampling unit (claim, chart, etc.) must have the same probability of selection. • Note that some random number generators will allow the user to set a ‘seed’. If that feature is available, the analyst should always set a seed. This will ensure that the sample can be replicated. • A simple random sample is not appropriate if the frame cannot be listed or if it is important that the sample contain particular (rare) subsets of the population.
9.
© 2016 Random Number
Generators • All random number generators are based on mathematical functions that need a ‘seed’ or starting point • The use of a seed ensures that two independent samples drawn using the same software will result in the same series of random numbers and reproducible sample • Excel – RAND() function does not allow a seed – Random Number Generation in Data Analysis ToolPak does allow a seed
10.
© 2016 Simple Random
Sampling Steps • Method 1: – The members of the sampling frame should be assigned a random number between 0 and 1 – The frame may then be sorted by the random number – The first ‘n’ will be the simple random sample of size ‘n’ • Method 2: – Assign a sequence number from 1 to ‘n’ to each member of the sampling frame – Use a random number generator (e.g., ratstats) to select random numbers from 1 to ‘N’ (N is the population size)
11.
© 2016 Systematic Random
Sampling • A systematic random sample is a simple random sample that is selected using a particular technique. If the population includes ‘N’ members and we wish to draw as sample of size ‘n’, then a systemic random sample could be selected by choosing every N/nth member of the population as the sample. – The selection should start at random from a member between the 1st and N/nth member. • NOTE: If N/n is not a whole number, then round down to the next lower whole number to determine the sampling interval. • In order to ensure that a systematic random sample is truly random, the population should not be sorted in an order that might bias the sample.
12.
© 2016 Stratified Random
Sampling • Population is divided into unique subsets or strata • Strata should be mutually exclusive and exhaustive. In other words, each of the members of the population should be in one and only one stratum. • A simple random sample is then selected from each of the strata • The size of the sample in each strata may be equal or may be assigned proportionally according to the relative size of each strata • Stratified sampling is appropriate when the quantity to be estimated may vary among natural subgroups (strata) of the population • Typical strata in healthcare may be: – CPT® Code (E/M levels) – Physician – Specialty – Clinic
13.
© 2016 Stratified Random
Sampling Example • Example: An analyst wishes to select a stratified random sample of 90 from a population of 1,000 E/M visits. The distribution of E/M visits in the population is: – Level 1: 55 – Level 2: 183 – Level 3: 236 – Level 4: 309 – Level 5: 217
14.
© 2016 Stratified Random
Sampling Example • Example: An analyst wishes to select a stratified random sample of 90 from a population of 1,000 E/M visits. The distribution of E/M visits in the population is: Level Population Count (N) % of Population Sample Size (n) 1 55 2 183 3 236 4 309 5 217 Totals 1,000 100% 90
15.
© 2016 Stratified Random
Sampling Example • Example: An analyst wishes to select a stratified random sample of 90 from a population of 1,000 E/M visits. The distribution of E/M visits in the population is:
16.
© 2016 Cluster Sampling •
The population is divided into subsets much like the strata in stratified sampling • Clusters should be mutually exclusive and exhaustive • All members of each cluster are selected to be a part of the sample • Clusters are selected at random • Cluster sampling is appropriate when it is difficult to access all of the population
17.
© 2016 Cluster Sampling Example The
director of the emergency department would like to audit the accuracy of charge capture for the first quarter of 2010. Unfortunately, she is not able to obtain a full listing of the patients that pass through the ED for a sampling frame. Instead, a cluster sample will be drawn using date of service as the cluster. Select 10 dates via simple random sampling to produce a cluster sample.
18.
© 2016 Non-probability Sampling •
Random sample not required if: – Study is exploratory or a focused review – Example: If we wish to determine educational opportunities for improving documentation, we may sample accounts with few secondary diagnoses to determine if there is a pattern in the types of diagnosis codes most likely to be missed • Typically, this sample is driven by some exploratory data analysis or data mining to help ‘steer’ the sample to subjects most likely to have the issue of interest
19.
© 2016 Non-probability Sampling •
Convenience sampling – Example – sample first ‘n’ customers that enter the hospital cafeteria • Judgment sampling – Use exploratory data analysis based on experience or history – AKA focused review – Example – Know from history that the customer satisfaction in cafeteria is lowest at lunch time because of long lines. Select sample at that time to try to improve process. • Quota sampling – Subjects divided into groups – Judgment sample used within each group – Example – may select first 10 male and 10 female customers to cafeteria
20.
© 2016 RAT-STATS • Statistical
program provided by the Office of the Inspector General (OIG) • Free and downloadable from the OIG website – PC only (no MAC version) • Functionality – Determine sample size – Create random numbers for sample selection – Analyze sample data from simple, cluster and stratified sampling • Two types of studies: – Attribute – variable of interest is a rate or proportion – Variable – variable of interest is a interval or ratio quantity
21.
© 2016 RAT-STATS Demonstration •
Instructor: – Reproduce the demo on pages 125 to 131 with a local installation of RAT-STATS – Students should practice in the lab
22.
© 2016 Sample Size •
Sample size is dependent on: – Standard Deviation of the quantity to be estimated – Desired precision (width of confidence interval) – Sampling method – Size of the population (if it is relatively small) – Resources available to perform the study • Any analyst that quotes a sample size without asking for the above information is not making an informed choice regarding sample size • The standard deviation of the quantity to be estimated typically is derived from a pilot study or previous review – OIG current recommendation for a pilot study is 30
23.
© 2016 Sample Size Attribute
Study • Determined by: – Anticipated rate of occurrence (50% results in largest sample) – Confidence level – Desired precision range
24.
© 2016 Sample Size Attribute
Study • A larger sample size is required for: – A higher level of confidence – A anticipated rate of occurrence closer to 50% – A smaller (narrower) precision range
25.
© 2016 Sample Size Variable
Study • Determined by: – Probe sample mean and standard deviation – Confidence level – Desired precision range
26.
© 2016 Sample Size Variable
Study • A larger sample size is required for: – A higher level of confidence – A larger probe standard deviation – A smaller (narrower) precision range
27.
© 2016 Sample Size
and Precision • In both types of studies, attribute or variable, a higher level of precision requires a larger sample size • A higher level of precision is equivalent to requiring a narrower confidence interval for a set confidence level • Note that increasing ‘n’ in both the proportion and mean confidence interval formulas results in narrower intervals (all other variables held constant)
Descargar ahora