PSI RESEARCH TOOLKIT. Dashboard Analysis Series Five: Analysis Methodology for Complex Survey Data B UILDING R ESEARCH C APACITY

Size: px
Start display at page:

Download "PSI RESEARCH TOOLKIT. Dashboard Analysis Series Five: Analysis Methodology for Complex Survey Data B UILDING R ESEARCH C APACITY"

Transcription

1 B UILDING R ESEARCH C APACITY Dashboard Analysis Series Five: Analysis Methodology for Complex Survey Data PSI s Core Values Bottom Line Health Impact * Private Sector Speed and Efficiency * Decentralization, Innovation, and Entrepreneurship * Long-term Commitment to the People We Serve

2 Research & Metrics Population Services International 1120 Nineteenth Street, NW, Suite 600 Washington, DC PSI Research & Metrics 2007 Population Services International, 2007 Contact Information Hongmei Yang, PhD 1, Kathryn O'Connell, PhD 2, and Varja Lipovsek, PhD 3 1. Researcher, PSI/Washington 2. Regional Researcher, PSI/Asia 3. Regional Researcher, PSI/Eastern Europe and PSI/South Asia For more information, please contact: Hongmei Yang, PhD Population Services International th Street, NW, Suite 600 Washington, DC Telephone hyang@psi.org

3 DASHBOARD ANALYSIS SERIES FIVE: ANALYSIS METHODOLOGY FOR COMPLEX SURVEY DATA LEARNING OBJECTIVES By the end of this chapter, the reader will be able to: 1. Identify study design related variables. 2. Understand when and why to control for study design. 3. Conduct dashboard analysis while controlling for the study design. BACKGROUND Most data are collected through multi-stage cluster sampling. For more information on cluster sampling, please refer to the toolkit chapter Sampling Strategies (Capo-Chichi and Chapman, 2007). Respondents are not independent of each other. This has implications when analyzing data and making valid inferences. For example, the purpose of the study is to identify the determinants of consistent condom use with clients among female sex workers in Hanoi, Vietnam. To find these high risk women for the study, the decision is made to recruit such women from entertainment establishments. The study design stipulates that ten such women need to be recruited in each establishment that has been randomly selected. Women from the same establishment are not independent of each other for many reasons, such as sharing opinions on condom use, following the same establishment policy regarding interaction with clients, coming from the same village, or having similar economic status. Therefore, they are more homogeneous compared to women from other establishments. Traditional analyses usually do not consider the sample design feature and ignore the homogeneity within clusters because they assume independent random sampling. Ignoring the homogeneity within clusters, however, results in an underestimate of the sample variance and invalid statistical inference. It is therefore necessary to consider the effect of the sampling design upon the results. Some software, such as STATA, SAS, and SPSS (version 13 and higher) provide special modules or commands for analyzing such data. However, when the software is not available, it is advisable to model the survey design variables (i.e., augment the model with survey design variables and in effect adjusting, or controlling, for the study design).

4 What Should Be Adjusted? Survey design variables identify the sample design. These study design variables include: 1. Strata (e.g., rural and urban areas) and 2. Primary sampling unit (PSU) or cluster--the first level of sampling. There may be only one study design variable (either strata or PSU) or both, depending on the sampling strategy of the survey. If multistage sampling was performed without stratifying the population, then adjust for the PSU. If stratified multistage sampling was performed, adjust for both strata and PSU. If one is unclear if it is either strata, PSU, or both, contact the regional researcher for assistance. It is critical to ensure that variables relevant to the study design are included in the questionnaires and collected during fieldwork. Every questionnaire must be coded appropriately for either strata, PSU, or both. When to Adjust for Study Design? The study design variables should be included in each of the dashboard analyses, whether they are significant or not: Monitoring analysis with more than one time round (UNIANOVA), Segmentation analysis (logistic regression and UNIANOVA), and Evaluation analysis to test for differences across exposure groups (UNIANOVA). The two major statistical procedures that deal with study design and addressed in examples in this chapter include logistic regression and UNIANOVA. For more detail on these and their assumptions, see toolkit chapters Dashboard Analysis Series Two: Monitoring Analysis, Dashboard Analysis Series Three: Segmentation Analysis, and Dashboard Analysis Series Four: Evaluation Analysis (Lipovsek, O Connell, & Yang, 2006). When including study design variables in logistic regression, these categorical variables will automatically be treated as categorical variables when you use the CONTRAST =INDICATOR function. Additional information is provided in Dashboard Analysis Series Three: Segmentation Analysis (Lipovsek et al., 2006). The sample size should be large enough so that for every variable in the model there are 10 cases of data. 1 For UNIANOVA, where there are many categories for the study design related variable, many dummy variables will have to be created for it. For example, if there are 50 provinces for the variable province, there will be 49 new dummies in the data set. In this case, running UNIANOVA may be problematic, and the results may not be reliable, unless there is a very large sample size. The rule of the ratio of case to variable used in regression analysis (i.e., number of cases is about 10 times the number of variables) can be borrowed here to decide whether the number of categories is too big (UNIANOVA and regression analysis share the same statistical principle). So, in this chapter, three examples are provided to illustrate how to identify and control for study design related variables in dashboard analysis. Different scenarios are considered when there may be few 1 Using "indicator/contrast" subcommand will specify the variable as a categorical variable so that it will not be treated as a continuous variable. A categorical variable will be counted as one variable no matter how many categories it has. 4

5 dummy variables related to the study design (or a lot) and different sample sizes. The first example deals with a situation where there is a large sample size (2,000 respondents) and only a few categories within the study design variables (i.e., limited number of strata and PSU), therefore a limited number of study design variables to be added to the model. The second example highlights a situation where there is a smaller sample size (some 637 respondents), and there are no strata. However, there is a large number of PSU, and therefore a large number of dummy variables. The third example addresses a case where there is a small sample size (735 respondents) and when there are both strata and a large number of PSU (and thus a large number of dummy variables). HOW-TO-STEPS AND CASE EXAMPLES 1. Adjusting for Survey Design Case Study One: Dealing with a small number of strata and PSU i. Sampling Strategy In Russia, economy and culture differ greatly in eastern and western parts of the country. An HIV TRaC study was conducted in the country with three provinces randomly selected using probability proportional to size (PPS) from the eastern region and three from the western region respectively. A total of six provinces were selected. A certain number of communes (these are smaller geographical areas within a province) were selected from each selected province. Then, certain number of households was selected from each of the sampled communes. Finally, one eligible participant was selected from each selected household. The final sample size was In this example, the study design related variables are: a. Strata: eastern or western region b. PSU: six provinces (three from each stratum) ii. Logistic Regression Logistic regression can be performed to determine which independent variables predict condom use at last sex with a casual partner. Both PSU ( province ) and strata ( region ) need to be included in the model to adjust for the study design effect. Key variables used in the analysis: cduse = condom use at last sex with a casual partner (DV) belief = belief construct selfeff = self efficacy about condom use with a casual partner cdeff = believes condoms are effective to prevent STDs risk = believes to be at risk of getting HIV avail = believes condom is available when needed age = age (continuous) marri = marital status (2 categories, 1: married, 2: single) province = id for selected provinces (6 categories, ranging from 1-6) region = id for east or west parts of the country (2 categories, 1 = west, 2 = east) 5

6 The logistic regression syntax when controlling for design variable looks like this: LOGISTIC REGRESSION VAR =cduse /METHOD =ENTER belief selfeff cdeff risk avail province region age marri /CONTRAST (province) =INDICATOR (1) /CONTRAST (region) =INDICATOR /CONTRAST (marri) =INDICATOR /PRINT=GOODFIT /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). Note: In the above syntax, the subcommand /CONTRAST is needed because province and region are categorical but not continuous variables. Province has six different values (from 1 to 6) representing six different provinces. After INDICATOR there is (1), which means the level with the lowest value will be used as the reference. 2 Since 1=province A is the level with the lowest value, province A is designated as the reference. Region has two values 1=west, 2=east. East is set as reference group because there is no (1) after INDCATOR, which means the level with highest value (which is 2 ) will be the reference. Similarly, for marital status, single is chosen as the reference since single has the highest value 2. When (1) is not specified after the INDICATOR, SPSS will automatically use the highest number in your variable as the reference. In the above example, there are two study design variables as well as the OAM factors and other demographic factors of interest. When looking at the output, some categories from the study design may be significant and others are not. Alternatively none may be significant, or all may be significant. Even if some, all or none of the categories are significant, one should still keep the study design variable in the analysis. As other non significant independent variables are removed, keep the study design variable in the analysis. Additionally, no variable s categories can be removed without removing the entire study design variable. Keep the study design variable throughout the entire logistic regression analysis since adjustments need to be made for the effects of the study design variable (even if some categories are not significant). The results for the study design variables can be read the same way as any other categorical variable, such as SES and ethnicity. However, unless it is explicitly desired by the program, the study design variables are usually not interpreted. They simply ensure that the survey design is taken into account during the analyses. An example where one might want to report this is when households have been stratified according to their proximity to malaria endemic areas, and one finds that behavior (e.g., use of a net) is better among those living in the forest (i.e., high malaria endemic areas) than those that are one kilometer from the forest (i.e., lower risk malaria endemic areas). 2 A reference group is simply the group that you will be comparing the other group with. In the example above, east will be compared with west. Or province A will be compared with province B, then province C, and so on. 6

7 iii. UNIANOVA UNIANOVA is then performed to produce adjusted proportions or means. Dummy variables need to be created for categorical variables. For a categorical variable with n categories, (n-1) dummies are needed. So in the above example, five dummies need to be created for province, one for region, and one for marital status. To keep the references the same as those in the logistic regression analysis, use east (denoted by a 2 ), single (denoted by a 2 ), and province A (denoted by a 1 ) as references for region, marital status, and province, respectively. The syntax for creating dummies is as below: IF province=2 prov2d=1. IF province=3 prov3d=1. IF province=4 prov4d=1. IF province=5 prov5d=1. IF province=6 prov6d=1. RECODE prov2d TO prov6d (1=1) (sysmis=0). RECODE region (1=1) (2=0) INTO westd. RECODE marri (1=1) (2=0) INTO marrid. UNIANOVA syntax for adjusted proportions or means while adjusting for design variables is as follows: UNIANOVA belief BY cduse WITH selfeff cdeff risk avail prov2d prov3d prov4d prov5d prov6d westd age marrid /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /EMMEANS=TABLES(cduse) WITH (selfeff=mean cdeff=mean risk=mean avail=mean prov2d=mean prov3d=mean prov4d=mean prov5d=mean prov6d=mean westd=mean age=mean marrid=mean) COMPARE ADJ(LSD) /CRITERIA=ALPHA(.05) /DESIGN=cduse selfeff cdeff risk avail prov2d prov3d prov4d prov5d prov6d westd age marrid. SPSS outputs for this example are not shown since there is nothing special from any typical logistic regression and UNIANOVA analysis. If there is more than one of round data, monitoring and evaluation analysis using UNIANOVA will need to be produced. Study design related variables need to be adjusted in these analyses. The method of adjusting is the same as that for segmentation analysis (i.e., keep the study design variables in the analysis even if they are not significant). 2. Adjusting for Survey Design Case Study Two: Dealing with a smaller sample size, no strata, but large number of PSU When the number of categories of study design related variable is big, special handling methods can be considered. The following provides an example where such special handling is needed. 7

8 i. Sampling Strategy In Cambodia, a HIV TRaC study was conducted among sexually active men with sweethearts (SAMS) in Phnom Penh. First, a census of hotspots was taken. Time-location clusters were created based on concentration of SAMS at different times of day at each hotspot. Seventy time-location clusters were selected using probability proportional to size (PPS). A certain number of SAMS was selected randomly at each sampled time-location cluster. A total of 637 SAMS were recruited for the study. Study design related variables: a. Strata: none b. PSU: time-location clusters (i.e., clust_no ) ii. Logistic Regression Logistic regression will be performed to determine which independent variables predict condom use at last sex with a sweetheart. In this case, the PSU ( clust_no ) needs to be included to adjust for sample design effect. Since clust_no is a categorical variable ranging from 1 70, the subcommand /CONTRAST is needed. The (1) after INDICATOR in the subcommand /CONTRAST indicates using the category with the lowest value as the reference. The results for the clust_no can be read the same way as any other categorical variables, such as SES and ethnicity. Key variables used in the analysis: q115_sh = condom use at last sex with a sweetheart (DV) beliefsh = belief construct q127 = discussed condom use with sweetheart q131 = believes condoms are appropriate to use with sweethearts q148 = believes to be at risk of getting HIV q113 = has bought condoms clust_no = id for time-location clusters (categorical variable, ranging from 1 to 70) The logistic regression syntax with controlling for design variable looks like this: LOGISTIC REGRESSION VAR =q115_sh /METHOD =ENTER beliefsh q127 q131 q148 q113 clust_no /CONTRAST (clust_no) =INDICATOR (1) /PRINT=GOODFIT /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). The following partial output is provided to show the new model with adjustment of study design (Table 1). 3 3 Because of space considerations, output for only three dummies of the clust_no are shown. In reality, there would be 69 dummies shown in the output. 8

9 TABLE 1: PARTIAL OUTPUT TABLE Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1(a) BELIEFSH Q Q Q Q CLUST_NO CLUST_NO (1) 420 CLUST_NO (2) CLUST_NO (3) a Variable(s) entered on step 1: BELIEFSH, Q127, Q131, Q148, Q113, CLUST_NO. When looking at the output, some categories from the study design are significant and others are not. Alternatively none may be significant, or all may be significant. Even if some, all, or none of the categories are significant, one should still keep the study design variable in the analysis. As other non significant independent variables are removed, the study design variables must be kept in the analysis. Additionally, no variable s categories can be removed without removing the entire study design variable. Keep the study design variable throughout the entire logistic regression analysis since adjustments need to be made for the effects of the study design variable (even if some categories are not significant). The results for the study design variables can be read the same way as any other categorical variable, such as SES and ethnicity. However, unless it is explicitly desired by the program, the study design variables are usually not interpreted; they simply ensure that the survey design is taken into account during the analyses. iii. UNIANOVA To get adjusted proportions or means while adjusting for design variables, create dummy variables for clust_no. For a categorical variable with n categories, (n- 1) dummies are needed. In the example, there are 70 categories in clust_no, which will produce 69 dummies. Compared to the sample size of 637, 69 is big enough to violate the rule of the ratio of case to variable used in regression analysis (i.e., a minimum of 10 cases for each independent variable). Therefore, using the traditional UNIANOVA syntax may be problematic. In this case, the following approach is suggested. 9

10 Syntax for adjusted proportion or mean with controlling for design variable: UNIANOVA q127 BY q115_sh clust_no WITH beliefsh q131 q148 q113 /RANDOM=clust_no /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /EMMEANS=TABLES(q115_sh) WITH (beliefsh=mean q131=mean q148=mean q113=mean) COMPARE ADJ(LSD) /CRITERIA=ALPHA(.05) /DESIGN=q115_sh clust_no beliefsh q131 q148 q113. Compared to the syntax in Case Study One, three differences should be noted: a. Variable clust_no is placed after BY (instead of after WITH). A variable placed after BY will be treated as categorical variable by default. So, there is no need to create dummies for it. It allows one to analyze the data and still adjust for study design variable when the sample is small. b. The subcommand /RANDOM= is needed because clust_no is a random factor, not a fixed factor (see Annex for difference between random and fixed factor). c. In subcommand DESIGN, cluster_no is used which means cluster TABLE 2: PARTIAL OUTPUT FROM THE ABOVE UNIANOVA Estimated Marginal Means Sweetheart Estimates Dependent Variable: Q127 Have you discussed using condom with your sweetheart? 95% Confidence Interval Sweetheart Mean Std. Error Lower Bound Upper Bound 0 No.507 a Yes.857 a a. Covariates appearing in the model are evaluated at the following values: BELIEFSH Belief = , Q131 Are condom appropriate to use with sweetheart? =.96, Q148 I am at risk of getting HIV/AIDs = 2.85, Q113 Have you ever bought condom for use? =

11 In this case, those SAMS who used a condom at last sex with their sweetheart were more likely to have discussed using a condom with sweethearts than those who had not used a condom (86% vs. 51%). 3. Adjusting for Survey Design Case Study Three: Dealing with a small sample size, strata, and large number of PSU When the number of categories of study design related variable is big, special handling methods can be considered. The following provides an example where such special handling is needed, and when there are both strata and PSUs being measured. i. Sampling Strategy In Myanmar, a HIV TRaC study was conducted among female sex workers (FSW) in four cities. The four cities were purposively selected and a census of hotspots was conducted and randomly selected in each city, using probability proportional to size (PPS). The number of selected hotspots was as: City 1 (106 hotspots), City 2 (41 hotspots), City 3 (15 hotspots), and City 4 (25 hotspots). In other words, the hotspots are nested within the cities. A fixed number of FSW (n=4) were interviewed in each hotspot (although one hotspot only had 2 women). A total of 735 FSW were recruited for the study. Study design related variables: a. Strata: the four cities (i.e., location ) b. PSU: the selected 190 hotspots (i.e., cluster ) ii. Logistic Regression Logistic regression will be performed to determine what independent variables predict consistent condom use with all clients. In this case, the PSU ( cluster ) and strata ( location ) need to be included in the model to adjust for sample design effect. Key variables used in the analysis: consisd = consistent condom use with all clients Q103D = has attended 5 standard & above level of education Q228D = has experienced police harassment in the past month Q307D = would want to have a confidential HIV test Q511D = has heard of Aphaw gel lubricant Self_eff = self-efficacy construct location = variable identifying 4 cities cluster = id for hotspots (categorical variable) The logistic regression model with controlling for design variables looks like this: LOGISTIC REGRESSION VAR=consisD /METHOD=ENTER q103d Q228D Q307D Q511D Self_eff location cluster /CONTRAST (location)=indicator (1) /CONTRAST (cluster)=indicator (1) /PRINT=GOODFIT 11

12 /CRITERIA PIN(.05) POUT(.10) ITERATE(20) CUT(.5). Note: subcommands /CONTRAST are used because location and cluster are categorical variables. The (1) after INDICATOR in the subcommand /CONTRAST indicates using the category with the lowest value as the reference. The results for them can be read the same way as any other categorical variable, such as SES and ethnicity. However, unless it is explicitly desired by the program, the study design variables are usually not interpreted; they simply ensure that the survey design is taken into account during the analyses. TABLE 3: PARTIAL OUTPUT FOR LOGISTIC REGRESSION MODEL WHEN CONTROLLING FOR STUDY DESIGN VARIABLES 4 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1(a) Q103D Q228D Q307D Q511D SELF_EFF LOCATION LOCATION (1) LOCATION (2) LOCATION (3) 737 CLUSTER CLUSTER( 1) CLUSTER( 2) CLUSTER( 3) a Variable(s) entered on step 1: Q103D, Q228D, Q307D, Q511D, SELF_EFF, LOCATION, CLUSTER. When looking at the output, some categories from your study design are significant and others are not. Alternatively none may be significant, or all may be significant. Even if some, all, or none of the categories are significant, still keep the study design variable in the analysis. As other non significant independent variables are removed, the study design variables must be kept in the analysis. Additionally, no variable s categories can be removed without removing the entire study design variable. Keep the study design variable throughout the entire logistic regression analysis since adjustments need to be made for the effects of the study design variable (even if some categories are not significant). 4 Because of space considerations, output for only three categories of the cluster variable are shown. The full output would show 189 clusters. 12

13 iii. UNIANOVA Since there are so many hotspots (190) creating 189 (n-1) dummies will violate the rule of the ratio of case to variable used in regression and UNIANOVA analysis (i.e., a minimum of 10 cases for each independent variable). Therefore, using the traditional UNIANOVA syntax may be problematic. In this case, the study has a nested design (with hotspots nested in the cities). The syntax below reflects this. Syntax for Adjusted Proportions or Means with adjusting for study design variable: UNIANOVA q103d BY consisd location cluster WITH Q228D Q307D Q511D Self_eff /RANDOM=cluster /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /EMMEANS=TABLES(consisD) WITH(Q228D=MEAN Q307D=MEAN Q511D=MEAN Self_eff=MEAN ) COMPARE ADJ(LSD) /CRITERIA=ALPHA(.05) /DESIGN=consisD location cluster(location) Q228D Q307D Q511D SELF_EFF Three important notes: a. Variables location and cluster are placed after BY (instead of after WITH). Variables placed after BY will be treated as categorical variables by default. So, there is no need for creating dummies for them. b. A subcommand /RANDOM= is needed for cluster since cluster is a random factor. No RANDOM is needed for the strata, since this is a fixed factor (See annex for difference between random and fixed factor). c. In subcommand DESIGN, location represents the strata variable, and cluster(location) is used which means cluster is nested within location (to put it more generally, PSUs are selected within each stratum). There is no specific requirement in terms of the order of variables in the subcommand DESIGN. 13

14 TABLE 4: OUTPUT FOR UNIANOVA WITH CONTROLLING FOR STUDY DESIGN RELATED VARIABLES Estimated Marginal Sweetheartt Estimates Dependent Variable: Q103D Consistent condom 95% Confidence Interval use with all client Mean Std. Error Lower Bound Upper Bound Di.00 hnot t consistent.659 a,b consistent.770 a,b a. Covariates appearing in the model are evaluated at the following values: Q228D =.1784, Q307D =.8243, Q511D =.6971, SELF_EFF Self efficacy = b. Based on modified population marginal mean. In this case, those FSW who consistently use a condom with clients are more likely to be people who obtained greater than 5 standard level of education (77% vs. 66%). LESSONS LEARNED There are currently no lessons learned to document. 14

15 QUALITY IMPROVEMENT CHECKLIST CHECKLIST 1: CONTROLING FOR STUDY DESIGN EFFECT Read and understand the sampling strategy of the study. Identify study design related variables: Do you have a variable indicating stratum in the data set? Do you have a variable indicating primary sampling unit in the data set? What is your sample size? In the logistic regression analysis: Specify categorical variables using /CONTRAST. In the UNIANOVA analysis: Create (n-1) dummies for a categorical variable with n categories. Identify whether the PSU variable is a fixed or a random factor. Use /RANDOM for a random factor. Check your sample size. Put strata variable and PSU variable in the right place in the syntax (i.e., either after WITH or after BY). APPENDIX Terms used in the chapter are standardized below. Categorical Variables Variables with values which are classified into unordered groups. For example, if there are five response choices under the variable province in a data set, province is a categorical variable since each response choice represents a province (i.e., group) and the order of the provinces is meaningless (i.e., unordered). If there are only two groups, the variable is termed specifically as dichotomous variable, such as gender (with female and male categories). Dummy Variables Variables that are sometimes needed to modify the form of categorical variables in some analysis (e.g., UNIANOVA). For example, marital status may be a categorical variable with 4 response choices: 1=currently married; 2=single; 3=divorced; and 4=others. To control its effect on the dependent variable in an ANOVA analysis, we need to create dummy variables, otherwise it will be treated as a continuous variable, which is meaningless since 1, 2, 3, and 4 only mean categories. The role of dummy variables is to identify each level of the original variables separately. A dummy variable has 1 or 0 value for each observation. The number of dummies needed for a categorical variable is the number of categories minus one. In 15

16 the marital status example, 3 dummies need to be created. If currently married is going to used as the reference group, the dummies will be: single equals to 1 if a respondent is single; single equals to 0 for all others; divorce equals to 1 if a respondent divorced and 0 otherwise; and other equals to 1 if a respondent falls in this category and 0 otherwise. By doing so, respondents who are currently married get 0 for all three newly created dummies. Variables Measures in the SPSS data file for the constructs of behavior, risk/need, behavioral determinants, population characteristics, and exposure. Variables can be measured with a single or multiitems, which are grouped together to form a composite. Binary Logistic Regression A modeling technique which allows examination of relationships between multiple explanatory variables (e.g., categorical or continuous) and one dichotomous outcome. The reported statistic is the odds ratio. Analysis of Variance A statistical technique for examining how much of the variation in the dependent (outcome) variable is attributable to the independent (explanatory) variable. In SPSS, both commands UNIANOVA and ANOVA can be used to test whether differences between means are significant. The dependent variable should be continuous and independent variable should be categorical (i.e., with two or more non-ordered categories). Fixed versus Random Factors Fixed Factor Refers to a variable for which all the possible values of interest are included. For example if one sampled every time-location cluster from the sampling frame, the clust_no would be a fixed factor. Random Factor A factor is said to be random when the levels of the factor represent a random sample of all possible values from a population and inferences will be made on the entire population. In the example, the 70 time-location clusters are representing a random sample of all possible time-location clusters from a sampling frame. Therefore, the variable clust_no is a random factor. 16

PSI RESEARCH & METRICS TOOLKIT

PSI RESEARCH & METRICS TOOLKIT Building Research Capacity Studies: TRaC, Condom Sales and the Disability-Adjusted-Life-Year (DALY) Calculator PSI s Core Values Bottom Line Health Impact Private Sector Speed and Efficiency Decentralization,

More information

Dashboard Analysis for TRaC Studies Series One: Pre-Analysis Data Preparation

Dashboard Analysis for TRaC Studies Series One: Pre-Analysis Data Preparation B UILDING R ESEARCH C APACITY Dashboard Analysis for TRaC Studies Series One: Pre-Analysis Data Preparation PSI s Core Values Bottom Line Health Impact * Private Sector Speed and Efficiency * Decentralization,

More information

SOCIAL MARKETING RESEARCH. The PSI Dashboard

SOCIAL MARKETING RESEARCH. The PSI Dashboard SOCIAL MARKETING RESEARCH Improving Reproductive Health Women of Reproductive Age In rural areas of Priority Sites of Tajikistan and Kyrgyzstan Trough Interpersonal Communications Second Round Tracking

More information

CHAPTER 3: METHODOLOGY

CHAPTER 3: METHODOLOGY CHAPTER 3: METHODOLOGY 3.1 Introduction This study is a secondary data analysis of the 1998 South African Demographic and Health Survey (SADHS) data set of women and households. According to the SADHS

More information

Lesotho (2006): HIV/AIDS TRaC Study among the General Population (15-35 years) First Round. The PSI Dashboard

Lesotho (2006): HIV/AIDS TRaC Study among the General Population (15-35 years) First Round. The PSI Dashboard SOCIAL MARKETING RESEARCH SERIES Lesotho (2006): HIV/AIDS TRaC Study among the General Population (15-35 years) First Round The PSI Dashboard Maseru, Lesotho October 2006 PSI s Core Values Bottom Line

More information

Daniel Boduszek University of Huddersfield

Daniel Boduszek University of Huddersfield Daniel Boduszek University of Huddersfield d.boduszek@hud.ac.uk Introduction to Logistic Regression SPSS procedure of LR Interpretation of SPSS output Presenting results from LR Logistic regression is

More information

PSI R ESEARCH& METRICS T OOLKIT. Writing Scale Items and Response Options B UILDING R ESEARCH C APACITY. Scales Series: Chapter 3

PSI R ESEARCH& METRICS T OOLKIT. Writing Scale Items and Response Options B UILDING R ESEARCH C APACITY. Scales Series: Chapter 3 PSI R ESEARCH& METRICS T OOLKIT B UILDING R ESEARCH C APACITY Scales Series: Chapter 3 Writing Scale Items and Response Options PSI s Core Values Bottom Line Health Impact * Private Sector Speed and Efficiency

More information

Analysis of TB prevalence surveys

Analysis of TB prevalence surveys Workshop and training course on TB prevalence surveys with a focus on field operations Analysis of TB prevalence surveys Day 8 Thursday, 4 August 2011 Phnom Penh Babis Sismanidis with acknowledgements

More information

LESOTHO (2009): MAP STUDY EVALUATING CONDOM COVERAGE, QUALITY OF COVERAGE, AND MARKET PENETRATION

LESOTHO (2009): MAP STUDY EVALUATING CONDOM COVERAGE, QUALITY OF COVERAGE, AND MARKET PENETRATION TRAC SUMMARY REPORT PSI DASHBOARD LESOTHO (2009): MAP STUDY EVALUATING CONDOM COVERAGE, QUALITY OF COVERAGE, AND MARKET PENETRATION Sponsored by: PSI s Core Values Bottom Line Health Impact * Private Sector

More information

Key Results Liberia Demographic and Health Survey

Key Results Liberia Demographic and Health Survey Key Results 2013 Liberia Demographic and Health Survey The 2013 Liberia Demographic and Health Survey (LDHS) was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS)

More information

Romania (2006): HIV/AIDS TraC Study Evaluating the Effect of a POL-type Program among Men who have Sex with Men in Bucharest.

Romania (2006): HIV/AIDS TraC Study Evaluating the Effect of a POL-type Program among Men who have Sex with Men in Bucharest. SOCIAL MARKETING RESEARCH SERIES Romania (2006): HIV/AIDS TraC Study Evaluating the Effect of a POL-type Program among Men who have Sex with Men in Bucharest Second Round The PSI Dashboard Bucharest, Romania

More information

Daniel Boduszek University of Huddersfield

Daniel Boduszek University of Huddersfield Daniel Boduszek University of Huddersfield d.boduszek@hud.ac.uk Introduction to Multinominal Logistic Regression SPSS procedure of MLR Example based on prison data Interpretation of SPSS output Presenting

More information

National Survey of Young Adults on HIV/AIDS

National Survey of Young Adults on HIV/AIDS Topline Kaiser Family Foundation National Survey of Young Adults on HIV/AIDS November 30, 2017 The 2017 Kaiser Family Foundation National Survey of Young Adults on HIV/AIDS is based on interviews with

More information

National Survey of Teens and Young Adults on HIV/AIDS

National Survey of Teens and Young Adults on HIV/AIDS Topline Kaiser Family Foundation National Survey of Teens and Young Adults on HIV/AIDS November 2012 This National Survey of Teens and Young Adults on HIV/AIDS was designed and analyzed by public opinion

More information

Public Attitudes and Knowledge about HIV/AIDS in Georgia Kaiser Family Foundation

Public Attitudes and Knowledge about HIV/AIDS in Georgia Kaiser Family Foundation Public Attitudes and Knowledge about HIV/AIDS in Georgia Kaiser Family Foundation Chart Pack November 2015 Methodology Public Attitudes and Knowledge about HIV/AIDS in Georgia is a representative, statewide

More information

SOCIAL MARKETING RESEARCH SERIES

SOCIAL MARKETING RESEARCH SERIES SOCIAL MARKETING RESEARCH SERIES Cambodia (2008): HIV TRaC Study Evaluating Condom Use with Sweethearts among High Risk Urban Men from four cities in Cambodia. First Round. The PSI Dashboard Cambodia December

More information

Part 8 Logistic Regression

Part 8 Logistic Regression 1 Quantitative Methods for Health Research A Practical Interactive Guide to Epidemiology and Statistics Practical Course in Quantitative Data Handling SPSS (Statistical Package for the Social Sciences)

More information

Mozambique (2013): Condom Use Among Males Behavioral Tracking Survey

Mozambique (2013): Condom Use Among Males Behavioral Tracking Survey TRaC SUMMARY AND SUPPLEMENTAL REPORT Mozambique (2013): Condom Use Among Males 15-49 Behavioral Tracking Survey ROUND 1 Sponsored by: PSI s Four Pillars Bottom Line Health Impact * Private Sector Speed

More information

Third Round. The PSI Dashboard

Third Round. The PSI Dashboard SOCIAL MARKETING RESEARCH SERIES Romania (2007): HIV/AIDS TRaC Study Evaluating the Effect of a POL-type Program among Men who have Sex with Men in Bucharest Third Round The PSI Dashboard Bucharest, Romania

More information

Problem Two (Data Analysis) Grading Criteria and Suggested Answers

Problem Two (Data Analysis) Grading Criteria and Suggested Answers Department of Epidemiology School of Public Health University of California, Los Angeles Problem Two (Data Analysis) Grading Criteria and Suggested Answers Enclosed is the form that will be used to grade

More information

Ministry of Health. National Center for HIV/AIDS, Dermatology and STD. Report of a Consensus Workshop

Ministry of Health. National Center for HIV/AIDS, Dermatology and STD. Report of a Consensus Workshop Ministry of Health National Center for HIV/AIDS, Dermatology and STD Report of a Consensus Workshop HIV Estimates and Projections for Cambodia 2006-2012 Surveillance Unit Phnom Penh, 25-29 June 2007 1

More information

Technical appendix Strengthening accountability through media in Bangladesh: final evaluation

Technical appendix Strengthening accountability through media in Bangladesh: final evaluation Technical appendix Strengthening accountability through media in Bangladesh: final evaluation July 2017 Research and Learning Contents Introduction... 3 1. Survey sampling methodology... 4 2. Regression

More information

11/4/2010. represent the average scores for BOTH A1 & A2 at each level of B. The red lines. are graphing B Main Effects. Red line is the Average A1

11/4/2010. represent the average scores for BOTH A1 & A2 at each level of B. The red lines. are graphing B Main Effects. Red line is the Average A1 Factorial ANOVA Chapter 12 Research Designs Between Between (2 between subjects factors) Mixed Design (1 between, 1 within subjects factor) Within Within (2 within subjects factors) The purpose of this

More information

El Tablero de Instrumentos de PSI

El Tablero de Instrumentos de PSI SERIE DE INVESTIGACIÓN DE MERCADEO SOCIAL HIV/AIDS RISK BEHAVIOR TRaC STUDY AMONG PEOPLE LIVING IN BATEYES IN DOMINICAN REPUBLIC. SECOND ROUND. 2008. El Tablero de Instrumentos de PSI SANTO DOMINGO, REPÚBLICA

More information

Steady Ready Go! teady Ready Go. Every day, young people aged years become infected with. Preventing HIV/AIDS in young people

Steady Ready Go! teady Ready Go. Every day, young people aged years become infected with. Preventing HIV/AIDS in young people teady Ready Go y Ready Preventing HIV/AIDS in young people Go Steady Ready Go! Evidence from developing countries on what works A summary of the WHO Technical Report Series No 938 Every day, 5 000 young

More information

Survey of G7 Nations on HIV Spending in Developing Countries

Survey of G7 Nations on HIV Spending in Developing Countries Chartpack The Kaiser Family Foundation Survey of G7 Nations on HIV Spending in Developing Countries July 2005 Methodology The Kaiser Family Foundation Survey of G7 Nations on HIV Spending in Developing

More information

One-Way ANOVAs t-test two statistically significant Type I error alpha null hypothesis dependant variable Independent variable three levels;

One-Way ANOVAs t-test two statistically significant Type I error alpha null hypothesis dependant variable Independent variable three levels; 1 One-Way ANOVAs We have already discussed the t-test. The t-test is used for comparing the means of two groups to determine if there is a statistically significant difference between them. The t-test

More information

Stata: Merge and append Topics: Merging datasets, appending datasets - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1. Terms There are several situations when working with

More information

SAMPLING ERROI~ IN THE INTEGRATED sysrem FOR SURVEY ANALYSIS (ISSA)

SAMPLING ERROI~ IN THE INTEGRATED sysrem FOR SURVEY ANALYSIS (ISSA) SAMPLING ERROI~ IN THE INTEGRATED sysrem FOR SURVEY ANALYSIS (ISSA) Guillermo Rojas, Alfredo Aliaga, Macro International 8850 Stanford Boulevard Suite 4000, Columbia, MD 21045 I-INTRODUCTION. This paper

More information

HIV/AIDS Prevalence Among South African Health Workers, 2002

HIV/AIDS Prevalence Among South African Health Workers, 2002 HIV/AIDS Prevalence Among South African Health Workers, 2002 Presented at the Kwazulu/Natal INDABA on AIDS 2 December 2003 O. Shisana, Sc.D Executive Director, SAHA Human Sciences Research Council Introduction

More information

Weight Adjustment Methods using Multilevel Propensity Models and Random Forests

Weight Adjustment Methods using Multilevel Propensity Models and Random Forests Weight Adjustment Methods using Multilevel Propensity Models and Random Forests Ronaldo Iachan 1, Maria Prosviryakova 1, Kurt Peters 2, Lauren Restivo 1 1 ICF International, 530 Gaither Road Suite 500,

More information

DUAL PROTECTION DILEMMA

DUAL PROTECTION DILEMMA PAA 2012 PAPER SUBMISSION Do not cite without permission from authors DUAL PROTECTION DILEMMA KIYOMI TSUYUKI University of California Los Angeles REGINA BARBOSA University of Campinas Campinas, Brazil

More information

Protocol of National TB Prevalence Survey in Cambodia

Protocol of National TB Prevalence Survey in Cambodia Training workshop for consultants of TB prevalence surveys Protocol of National TB Prevalence Survey in Cambodia 26 February 2011 Phnom Penh, Cambodia Norio Yamada, RIT/JATA Training workshop for consultants

More information

Chapter 3. Producing Data

Chapter 3. Producing Data Chapter 3. Producing Data Introduction Mostly data are collected for a specific purpose of answering certain questions. For example, Is smoking related to lung cancer? Is use of hand-held cell phones associated

More information

Vocabulary. Bias. Blinding. Block. Cluster sample

Vocabulary. Bias. Blinding. Block. Cluster sample Bias Blinding Block Census Cluster sample Confounding Control group Convenience sample Designs Experiment Experimental units Factor Level Any systematic failure of a sampling method to represent its population

More information

CAMBODIAN HOUSEHOLD MALE SURVEY (JULY 2001)

CAMBODIAN HOUSEHOLD MALE SURVEY (JULY 2001) COUNTRY COORDINATED PROPOSAL (CCP) FOR THE GLOBALFUND TO FIGHT AIDS, TB AND MALARIA CAMBODIAN HOUSEHOLD MALE SURVEY (JULY 2001) SUPPORTIVE DOCUMENTS FOR HIV/AIDS PROGRAM Submitted by CAMBODIA COORDINATING

More information

Intro to SPSS. Using SPSS through WebFAS

Intro to SPSS. Using SPSS through WebFAS Intro to SPSS Using SPSS through WebFAS http://www.yorku.ca/computing/students/labs/webfas/ Try it early (make sure it works from your computer) If you need help contact UIT Client Services Voice: 416-736-5800

More information

BLACK RESIDENTS VIEWS ON HIV/AIDS IN THE DISTRICT OF COLUMBIA

BLACK RESIDENTS VIEWS ON HIV/AIDS IN THE DISTRICT OF COLUMBIA PUBLIC OPINION DISPARITIES & PUBLIC OPINION DATA NOTE A joint product of the Disparities Policy Project and Public Opinion and Survey Research October 2011 BLACK RESIDENTS VIEWS ON HIV/AIDS IN THE DISTRICT

More information

Cambodia Key Data Issues and Suggestions

Cambodia Key Data Issues and Suggestions 1251 DECEMBER 2011 REFERENCE SOURCES This review of reference sources is categorized into two groups, namely general sources and country sources. The general sources include the regional or global documents,

More information

Determinants of Infertility and Treatment Seeking Behaviour among Currently Married Women in India. Ramesh Chellan India

Determinants of Infertility and Treatment Seeking Behaviour among Currently Married Women in India. Ramesh Chellan India Determinants of Infertility and Treatment Seeking Behaviour among Currently Married Women in India Ramesh Chellan India Background Infertility is a worldwide problem affecting about 50 80 million couples

More information

Why Does Sampling Matter? Answers From the Gender Norms and Labour Supply Project

Why Does Sampling Matter? Answers From the Gender Norms and Labour Supply Project Briefing Paper 1 July 2015 Why Does Sampling Matter? Answers From the Gender Norms and Labour Supply Project In this briefing paper you will find out how to: understand and explain different sampling designs

More information

Estimating Incidence of HIV with Synthetic Cohorts and Varying Mortality in Uganda

Estimating Incidence of HIV with Synthetic Cohorts and Varying Mortality in Uganda Estimating Incidence of HIV with Synthetic Cohorts and Varying Mortality in Uganda Abstract We estimate the incidence of HIV using two cross-sectional surveys in Uganda with varying mortality rates. The

More information

Botswana - Botswana AIDS Impact Survey 2001

Botswana - Botswana AIDS Impact Survey 2001 Statistics Botswana Data Catalogue Botswana - Botswana AIDS Impact Survey 2001 Central Statistics Office (CSO) - Ministry of Finance and Development Planning Report generated on: September 28, 2016 Visit

More information

HIV/AIDS Prevention among Female Sex Workers in AVAHAN districts of India

HIV/AIDS Prevention among Female Sex Workers in AVAHAN districts of India COMMUNITY GROUP, PHYSICAL VIOLENCE AND HIV/AIDS PREVENTION IN INDIA 1 Title: Impact Evaluation of Community Group Membership on Physical Violence and HIV/AIDS Prevention among Female Sex Workers in AVAHAN

More information

Until recently, countries in Eastern

Until recently, countries in Eastern 10 C H A P T E R KNOWLEDGE OF HIV/AIDS TRANSMISSION AND PREVENTION Until recently, countries in Eastern Europe, the, and Central Asia had not experienced the epidemic levels of HIV/AIDS found in other

More information

Ethnicity and Maternal Health Care Utilization in Nigeria: the Role of Diversity and Homogeneity

Ethnicity and Maternal Health Care Utilization in Nigeria: the Role of Diversity and Homogeneity Ethnicity and Maternal Health Care Utilization in Nigeria: the Role of Diversity and Homogeneity In spite of the significant improvements in the health of women worldwide, maternal mortality ratio has

More information

Sexual Behaviour in Rural Northern India: An Insight

Sexual Behaviour in Rural Northern India: An Insight Sexual Behaviour in Rural Northern India: An Insight Meren Longkumer* Dr S.K.Singh** Dr.H.Lhungdim,*** Introduction As we all know, sex is usually not an overt discussion in India, but the spread of HIV/AIDS

More information

PREVALENCE OF HIV AND SYPHILIS 14

PREVALENCE OF HIV AND SYPHILIS 14 PREVALENCE OF HIV AND SYPHILIS 14 Kumbutso Dzekedzeke Zambia has used the antenatal care (ANC) sentinel surveillance data as a principal means of monitoring the spread of HIV for almost a decade (Fylkesnes

More information

A situation analysis of health services. Rachel Jewkes, MRC Gender & Health Research Unit, Pretoria, South Africa

A situation analysis of health services. Rachel Jewkes, MRC Gender & Health Research Unit, Pretoria, South Africa A situation analysis of health services Rachel Jewkes, MRC Gender & Health Research Unit, Pretoria, South Africa Introduction A situation analysis of post-rape health services is a relatively simple piece

More information

The Australian longitudinal study on male health sampling design and survey weighting: implications for analysis and interpretation of clustered data

The Australian longitudinal study on male health sampling design and survey weighting: implications for analysis and interpretation of clustered data The Author(s) BMC Public Health 2016, 16(Suppl 3):1062 DOI 10.1186/s12889-016-3699-0 RESEARCH Open Access The Australian longitudinal study on male health sampling design and survey weighting: implications

More information

THE STATSWHISPERER. Introduction to this Issue. Doing Your Data Analysis INSIDE THIS ISSUE

THE STATSWHISPERER. Introduction to this Issue. Doing Your Data Analysis INSIDE THIS ISSUE Spring 20 11, Volume 1, Issue 1 THE STATSWHISPERER The StatsWhisperer Newsletter is published by staff at StatsWhisperer. Visit us at: www.statswhisperer.com Introduction to this Issue The current issue

More information

Subject index. bootstrap...94 National Maternal and Infant Health Study (NMIHS) example

Subject index. bootstrap...94 National Maternal and Infant Health Study (NMIHS) example Subject index A AAPOR... see American Association of Public Opinion Research American Association of Public Opinion Research margins of error in nonprobability samples... 132 reports on nonprobability

More information

Fit to play but goalless: Labour market outcomes in a cohort of public sector ART patients in Free State province, South Africa

Fit to play but goalless: Labour market outcomes in a cohort of public sector ART patients in Free State province, South Africa Fit to play but goalless: Labour market outcomes in a cohort of public sector ART patients in Free State province, South Africa Frikkie Booysen Department of Economics / Centre for Health Systems Research

More information

Co-Variation in Sexual and Non-Sexual Risk Behaviors Over Time Among U.S. High School Students:

Co-Variation in Sexual and Non-Sexual Risk Behaviors Over Time Among U.S. High School Students: Co-Variation in Sexual and Non-Sexual Risk Behaviors Over Time Among U.S. High School Students: 1991-2005 John Santelli, MD, MPH, Marion Carter, PhD, Patricia Dittus, PhD, Mark Orr, PhD APHA 135 th Annual

More information

Background paper number 7

Background paper number 7 Background paper number 7 Lessons learnt and policy implications from recently completed TB disease prevalence surveys and their interpretation - Vietnam Binh Hoa Nguyen Prevalence survey in Vietnam 2006-2007

More information

The Influence of Geographic Location on Sexual Behaviors Related to the Transmission of HIV in Mainland Tanzania

The Influence of Geographic Location on Sexual Behaviors Related to the Transmission of HIV in Mainland Tanzania University of Connecticut DigitalCommons@UConn Master's Theses University of Connecticut Graduate School 5-10-2014 The Influence of Geographic Location on Sexual Behaviors Related to the Transmission of

More information

USING THE WORKBOOK METHOD

USING THE WORKBOOK METHOD USING THE WORKBOOK METHOD TO MAKE HIV/AIDS ESTIMATES IN COUNTRIES WITH LOW-LEVEL OR CONCENTRATED EPIDEMICS Manual Joint United Nations Programme on HIV/AIDS (UNAIDS) Reference Group on Estimates, Models

More information

Use of GEEs in STATA

Use of GEEs in STATA Use of GEEs in STATA 1. When generalised estimating equations are used and example 2. Stata commands and options for GEEs 3. Results from Stata (and SAS!) 4. Another use of GEEs Use of GEEs GEEs are one

More information

Research Commentary volume 1, issue 7. Cambodia and HIV: Winning Round Two in a Preventive Fight. By Nada Chaya JULY 2006

Research Commentary volume 1, issue 7. Cambodia and HIV: Winning Round Two in a Preventive Fight. By Nada Chaya JULY 2006 Research Commentary volume 1, issue 7 JULY 2006 Cambodia and HIV: Winning Round Two in a Preventive Fight By Nada Chaya A generation has passed since the onset of the HIV/AIDS pandemic. During this time,

More information

Using the Workbook Method to Make HIV/AIDS Estimates in Countries with Low-Level or Concentrated Epidemics. Participant Manual

Using the Workbook Method to Make HIV/AIDS Estimates in Countries with Low-Level or Concentrated Epidemics. Participant Manual Using the Workbook Method to Make HIV/AIDS Estimates in Countries with Low-Level or Concentrated Epidemics Participant Manual Joint United Nations Programme on HIV/AIDS (UNAIDS), Reference Group on Estimates,

More information

Propensity Score Methods for Causal Inference with the PSMATCH Procedure

Propensity Score Methods for Causal Inference with the PSMATCH Procedure Paper SAS332-2017 Propensity Score Methods for Causal Inference with the PSMATCH Procedure Yang Yuan, Yiu-Fai Yung, and Maura Stokes, SAS Institute Inc. Abstract In a randomized study, subjects are randomly

More information

Presentation by: Dr. Mun Phalkun, Surveillance unit, NCHADS

Presentation by: Dr. Mun Phalkun, Surveillance unit, NCHADS Integrated HIV Bio-Behavioral Surveillance (IBBS 2016) among Female Entertainment Workers Presentation by: Dr. Mun Phalkun, Surveillance unit, NCHADS Outline Background Objectives Methods Findings Conclusions

More information

HIV Sentinel Surveillance Team

HIV Sentinel Surveillance Team HIV Sentinel Surveillance Team Dr. Mean Chhi Vun, Director of NCHADS Dr. Hor Bunleng, Deputy Director of NCHADS Dr. Seng Sut Wantha, Deputy Director of NCHADS Dr. Ly Penh Sun, Chief of Surveillance Unit

More information

Summary Report - IBBS Balochistan - Quetta Round 1, ROUND 1 SUMMARY REPORT -BALOCHISTAN QUETTA

Summary Report - IBBS Balochistan - Quetta Round 1, ROUND 1 SUMMARY REPORT -BALOCHISTAN QUETTA ROUND 1, 5-6 SUMMARY REPORT - BALOCHISTAN NATIONAL AIDS CONTROL PROGRAM BALOCHISTAN AIDS CONTROL PROGRAM CANADA PAKISTAN HIV/AIDS SURVEILLANCE PROJECT May 6 5-6 ROUND 1 SUMMARY REPORT -BALOCHISTAN QUETTA

More information

FIRST ROUND. Sponsored by:

FIRST ROUND. Sponsored by: TRaC SUMMARY REPORT PSI DASHBOARD LESOTHO (2012): HIV/AIDS TRaC Study Evaluating HIV Counseling and Testing Uptake, Consistent Condom Use, and Concurrent Sexual Partnerships among Men and Women aged 15

More information

Workbook Method for estimating HIV prevalence in low level or concentrated epidemics

Workbook Method for estimating HIV prevalence in low level or concentrated epidemics Workbook Method for estimating HIV prevalence in low level or concentrated epidemics Joint United Nations Programme on HIV/AIDS World Health Organization Horacio Ruiseñor Escudero INSP Background Estimation

More information

AnExaminationoftheQualityand UtilityofInterviewerEstimatesof HouseholdCharacteristicsinthe NationalSurveyofFamilyGrowth. BradyWest

AnExaminationoftheQualityand UtilityofInterviewerEstimatesof HouseholdCharacteristicsinthe NationalSurveyofFamilyGrowth. BradyWest AnExaminationoftheQualityand UtilityofInterviewerEstimatesof HouseholdCharacteristicsinthe NationalSurveyofFamilyGrowth BradyWest An Examination of the Quality and Utility of Interviewer Estimates of Household

More information

Multi-Country Evaluation of Social Marketing Programs for Promoting HIV Voluntary Counseling and Testing

Multi-Country Evaluation of Social Marketing Programs for Promoting HIV Voluntary Counseling and Testing Multi-Country Evaluation of Social Marketing Programs for Promoting HIV Voluntary Counseling and Testing Dvora Joseph Kerry Richter Summer Rosenstock Shannon England PSI Research & Metrics Department Working

More information

Nepal (2008): Zinc TRaC Survey. Round One. The PSI Dashboard

Nepal (2008): Zinc TRaC Survey. Round One. The PSI Dashboard SOCIAL MARKETING RESEARCH SERIES Nepal (2008): Zinc TRaC Survey Round One The PSI Dashboard Kathmandu, Nepal November, 2008 PSI s Core Values Bottom Line Health Impact * Private Sector Speed and Efficiency

More information

USING THE CENSUS 2000/2001 SUPPLEMENTARY SURVEY AS A SAMPLING FRAME FOR THE NATIONAL EPIDEMIOLOGICAL SURVEY ON ALCOHOL AND RELATED CONDITIONS

USING THE CENSUS 2000/2001 SUPPLEMENTARY SURVEY AS A SAMPLING FRAME FOR THE NATIONAL EPIDEMIOLOGICAL SURVEY ON ALCOHOL AND RELATED CONDITIONS USING THE CENSUS 2000/2001 SUPPLEMENTARY SURVEY AS A SAMPLING FRAME FOR THE NATIONAL EPIDEMIOLOGICAL SURVEY ON ALCOHOL AND RELATED CONDITIONS Marie Stetser, Jana Shepherd, and Thomas F. Moore 1 U.S. Census

More information

Supplementary Web Appendix Transactional Sex as a Response to Risk in Western Kenya American Economic Journal: Applied Economics

Supplementary Web Appendix Transactional Sex as a Response to Risk in Western Kenya American Economic Journal: Applied Economics Supplementary Web Appendix Transactional Sex as a Response to Risk in Western Kenya American Economic Journal: Applied Economics Jonathan Robinson University of California, Santa Cruz Ethan Yeh y World

More information

Sexual multipartnership and condom use among adolescent boys in four sub-saharan African countries

Sexual multipartnership and condom use among adolescent boys in four sub-saharan African countries 1 Sexual multipartnership and condom use among adolescent boys in four sub-saharan African countries Guiella Georges, Department of demography, University of Montreal Email: georges.guiella@umontreal.ca

More information

Male Fertility and Male Sexuality: The Role of Social and Cultural Factors

Male Fertility and Male Sexuality: The Role of Social and Cultural Factors Kamla-Raj 2005 Stud. Tribes Tribals, 3(2): 79-84 (2005) Male Fertility and Male Sexuality: The Role of Social and Cultural Factors M.S.R. Murthy*, V.K.R. Kumar, M. Hari, P. Vinayaka Murthy and K. Rajasekhar

More information

Abstract Background Aims Methods Results Conclusion: Key Words

Abstract Background Aims Methods Results Conclusion: Key Words Association between socio-demographic factors and knowledge of contraceptive methods with contraceptive use among women of reproductive age: a cross-sectional study using the 2013 Liberia DHS Tara Rourke

More information

An Application of Propensity Modeling: Comparing Unweighted and Weighted Logistic Regression Models for Nonresponse Adjustments

An Application of Propensity Modeling: Comparing Unweighted and Weighted Logistic Regression Models for Nonresponse Adjustments An Application of Propensity Modeling: Comparing Unweighted and Weighted Logistic Regression Models for Nonresponse Adjustments Frank Potter, 1 Eric Grau, 1 Stephen Williams, 1 Nuria Diaz-Tena, 2 and Barbara

More information

Willingness to pay for IUDs among women in Madagascar a comparative analysis

Willingness to pay for IUDs among women in Madagascar a comparative analysis Willingness to pay for IUDs among women in Madagascar a comparative analysis Authors: Nirali M. Chakraborty, Ph.D.; Justin Rahariniaina; Ietje Reerink Background: Long acting reversible contraceptive methods

More information

Simple Linear Regression One Categorical Independent Variable with Several Categories

Simple Linear Regression One Categorical Independent Variable with Several Categories Simple Linear Regression One Categorical Independent Variable with Several Categories Does ethnicity influence total GCSE score? We ve learned that variables with just two categories are called binary

More information

Donna L. Coffman Joint Prevention Methodology Seminar

Donna L. Coffman Joint Prevention Methodology Seminar Donna L. Coffman Joint Prevention Methodology Seminar The purpose of this talk is to illustrate how to obtain propensity scores in multilevel data and use these to strengthen causal inferences about mediation.

More information

Media, Discussion and Attitudes Technical Appendix. 6 October 2015 BBC Media Action Andrea Scavo and Hana Rohan

Media, Discussion and Attitudes Technical Appendix. 6 October 2015 BBC Media Action Andrea Scavo and Hana Rohan Media, Discussion and Attitudes Technical Appendix 6 October 2015 BBC Media Action Andrea Scavo and Hana Rohan 1 Contents 1 BBC Media Action Programming and Conflict-Related Attitudes (Part 5a: Media and

More information

Reproductive Health s Knowledge, Attitudes, and Practices A European Youth Study Protocol October 13, 2009

Reproductive Health s Knowledge, Attitudes, and Practices A European Youth Study Protocol October 13, 2009 Reproductive Health s Knowledge, Attitudes, and Practices A European Youth Study Protocol October 13, 2009 I. Introduction European youth has been facing major socio-demographic and epidemiological changes

More information

Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H

Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H Midterm Exam ANSWERS Categorical Data Analysis, CHL5407H 1. Data from a survey of women s attitudes towards mammography are provided in Table 1. Women were classified by their experience with mammography

More information

aids in asia and the pacific

aids in asia and the pacific HIV AIDS AND DEVELOPMENT IN ASIA AND THE PACIFIC a lengthening shadow aids in asia and the pacific World Health Organization Regional Offices for South East Asia and the Western Pacific Region 9 10 OCTOBER

More information

Funnelling Used to describe a process of narrowing down of focus within a literature review. So, the writer begins with a broad discussion providing b

Funnelling Used to describe a process of narrowing down of focus within a literature review. So, the writer begins with a broad discussion providing b Accidental sampling A lesser-used term for convenience sampling. Action research An approach that challenges the traditional conception of the researcher as separate from the real world. It is associated

More information

Appendix: Anger and Support for Vigilante Justice in Mexico s Drug War

Appendix: Anger and Support for Vigilante Justice in Mexico s Drug War Appendix: Anger and Support for Vigilante Justice in Mexico s Drug War Omar García-Ponce, Lauren Young, and Thomas Zeitzoff August 27, 2018 A Sampling A.1 Sampling Design Our target population was adults

More information

Chiang Mai University/Johns Hopkins University HIV/AIDS Research on VCT

Chiang Mai University/Johns Hopkins University HIV/AIDS Research on VCT Chiang Mai University/Johns Hopkins University HIV/AIDS Research on VCT David Celentano, Professor of Epidemiology May 26, 2005 Scope of the CMU/JHU Collaborative HIV/AIDS Research Agenda HIV/AIDS research

More information

Version No. 7 Date: July Please send comments or suggestions on this glossary to

Version No. 7 Date: July Please send comments or suggestions on this glossary to Impact Evaluation Glossary Version No. 7 Date: July 2012 Please send comments or suggestions on this glossary to 3ie@3ieimpact.org. Recommended citation: 3ie (2012) 3ie impact evaluation glossary. International

More information

Statistics as a Tool. A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations.

Statistics as a Tool. A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations. Statistics as a Tool A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations. Descriptive Statistics Numerical facts or observations that are organized describe

More information

The Dynamics of Condom Use with Regular and Casual Partners: Analysis of the 2006 National Sexual Behavior Survey of Thailand

The Dynamics of Condom Use with Regular and Casual Partners: Analysis of the 2006 National Sexual Behavior Survey of Thailand The Dynamics of Condom Use with Regular and Casual Partners: Analysis of the 2006 National Sexual Behavior Survey of Thailand Aphichat Chamratrithirong 1 *, Paulina Kaiser 2 1 Institute for Population

More information

Sex and the Classroom: Can a Cash Transfer Program for Schooling decrease HIV infections?

Sex and the Classroom: Can a Cash Transfer Program for Schooling decrease HIV infections? Sex and the Classroom: Can a Cash Transfer Program for Schooling decrease HIV infections? Sarah Baird, George Washington University Craig McIntosh, UCSD Berk Özler, World Bank Education as a Social Vaccine

More information

Technical Guidance for Global Fund HIV Proposals

Technical Guidance for Global Fund HIV Proposals Technical Guidance for Global Fund HIV Proposals FINAL DRAFT DOCUMENT The document will remain in a final draft form for Round 9 and will be finalized for the Round 10 Resource Toolkit. If you would like

More information

8/10/2015. Introduction: HIV. Introduction: Medical geography

8/10/2015. Introduction: HIV. Introduction: Medical geography Introduction: HIV Incorporating spatial variability to generate sub-national estimates of HIV prevalence in SSA Diego Cuadros PhD Laith Abu-Raddad PhD Sub-Saharan Africa (SSA) has by far the largest HIV

More information

Kigali Province East Province North Province South Province West Province discordant couples

Kigali Province East Province North Province South Province West Province discordant couples EXECUTIVE SUMMARY This report summarizes the processes, findings, and recommendations of the Rwanda Triangulation Project, 2008. Triangulation aims to synthesize data from multiple sources to strengthen

More information

Introduction to Household Surveys

Introduction to Household Surveys 05_XXX_MM1 Introduction to Household Surveys Khin Win Thin RHR\TCC Training Course in Reproductive Health/Sexual Health Research Geneva 2008 Type of Studies Observational Retrospective: Case-control Prospective:

More information

How to analyze correlated and longitudinal data?

How to analyze correlated and longitudinal data? How to analyze correlated and longitudinal data? Niloofar Ramezani, University of Northern Colorado, Greeley, Colorado ABSTRACT Longitudinal and correlated data are extensively used across disciplines

More information

What s New in SUDAAN 11

What s New in SUDAAN 11 What s New in SUDAAN 11 Angela Pitts 1, Michael Witt 1, Gayle Bieler 1 1 RTI International, 3040 Cornwallis Rd, RTP, NC 27709 Abstract SUDAAN 11 is due to be released in 2012. SUDAAN is a statistical software

More information

Social Issues in Nonmetropolitan Nebraska: Perceptions of Social Stigma and Drug and Alcohol Abuse: 2018 Nebraska Rural Poll Results

Social Issues in Nonmetropolitan Nebraska: Perceptions of Social Stigma and Drug and Alcohol Abuse: 2018 Nebraska Rural Poll Results University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Publications of the Rural Futures Institute Rural Futures Institute at the University of Nebraska 8-2-2018 Social Issues

More information

One-Way Independent ANOVA

One-Way Independent ANOVA One-Way Independent ANOVA Analysis of Variance (ANOVA) is a common and robust statistical test that you can use to compare the mean scores collected from different conditions or groups in an experiment.

More information

Modelling Reduction of Coronary Heart Disease Risk among people with Diabetes

Modelling Reduction of Coronary Heart Disease Risk among people with Diabetes Modelling Reduction of Coronary Heart Disease Risk among people with Diabetes Katherine Baldock Catherine Chittleborough Patrick Phillips Anne Taylor August 2007 Acknowledgements This project was made

More information

''A Three Year ( ) Monitoring of External Migration Situation in Armenia through Sample Survey''Program. Methodology in 2017

''A Three Year ( ) Monitoring of External Migration Situation in Armenia through Sample Survey''Program. Methodology in 2017 1 2 3 ''A Three Year (2015 2017) Monitoring of External Migration Situation in Armenia through Sample Survey''Program Methodology in 2017 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

More information

STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS

STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS Circle the best answer. This scenario applies to Questions 1 and 2: A study was done to compare the lung capacity of coal miners to the lung

More information