# Cross-Sectional Study Design and Data Analysis

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1 The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Cross-Sectional Study Design and Data Analysis Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa and Diane Marie M. St. George Master s Programs in Public Health Walden University Chicago, Illinois

2 Contents Lesson Plan Section I: Introduction to the Cross-Sectional Study Section II: Overview of Questionnaire Design Section III: Question Construction Section IV: Sampling Section V: Questionnaire Administration Section VI: Secondary Analysis of Data Section VII: Using Epi Info to Analyze YRBS Data Worked Example for Teachers Assessment Appendix 1: YRBS 2001 Data Documentation/Codebook Appendix 2: Interpreting Chi-Square A Quick Guide for Teachers Copyright 2004 by College Entrance Examination Board. All rights reserved. College Board and the acorn logo are registered trademarks of the College Entrance Examination Board. Microsoft Word, Microsoft Excel and Windows are registered trademarks of Microsoft Corporation. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: 2

4 ASSESSMENT: At end of module. There are four options provided, one of which includes suggested answers. LINK TO STANDARDS: This module addresses the following mathematics standards: The Standard Data Analysis and Probability Instructional programs from prekindergarten through grade 12 should enable all students to: Formulate questions that can be addressed with data and collect, organize and display relevant data to answer them. Select and use appropriate statistical methods to analyze data. Develop and evaluate inferences and predictions that are based on data. The Grades 9 12 Expectations Understand the differences among various kinds of studies and which types of inferences can legitimately be drawn from each; know the characteristics of welldesigned studies, including the role of randomization in surveys and experiments; understand the meaning of measurement data and categorical data, of univariate and bivariate data, and of the term variable; understand histograms, parallel box plots, and scatter plots and use them to display data; compute basic statistics and understand the distinction between a statistic and a parameter. For univariate measurement data, be able to display the distribution, describe its shape, and select and calculate summary statistics; for bivariate measurement data, be able to display a scatter plot, describe its shape, and determine regression coefficients, regression equations, and correlation coefficients using technological tools; display and discuss bivariate data where at least one variable is categorical; recognize how linear transformations of univariate data affect shape, center and spread; identify trends in bivariate data and find functions that model the data or transform the data so that they can be modeled. Use simulations to explore the variability of sample statistics from a known population and to construct sampling distributions; understand how sample statistics reflect the values of population parameters and 4

5 use sampling distributions as the basis for informal inference; evaluate published reports that are based on data by examining the design of the study, the appropriateness of the data analysis, and the validity of conclusions; understand how basic statistical techniques are used to monitor process characteristics in the workplace. Understand and apply basic concepts of probability. Understand the concepts of sample space and probability distribution and construct sample spaces and distributions in simple cases; use simulations to construct empirical probability distributions; compute and interpret the expected value of random variables in simple cases; understand the concepts of conditional probability and independent events; understand how to compute the probability of a compound event. Problem Solving Instructional programs from prekindergarten through grade 12 should enable all students to: Build new mathematical knowledge through problem solving Solve problems that arise in mathematics and in other contexts Apply and adapt a variety of appropriate strategies to solve problems Monitor and reflect on the process of mathematical problem solving Communication Instructional programs from prekindergarten through grade 12 should enable all students to: Organize and consolidate their mathematical thinking through communication Communicate their mathematical thinking coherently and clearly to peers, teachers, and others Analyze and evaluate the mathematical thinking and strategies of others Use the language of mathematics to express mathematical ideas precisely Connections Instructional programs from prekindergarten through grade 12 should enable all students to: Recognize and use connections among mathematical ideas 5

6 Understand how mathematical ideas interconnect and build on one another to produce a coherent whole Recognize and apply mathematics in contexts outside of mathematics Representation Instructional programs from prekindergarten through grade 12 should enable all students to: Create and use representations to organize, record, and communicate mathematical ideas Select, apply and translate among mathematical representations to solve problems Use representations to model and interpret physical, social, and mathematical phenomena This module also addresses the following science standards: Science As Inquiry Abilities necessary to do scientific inquiry Unifying Concepts and Processes Evidence, models and explanation Bibliography Aday L. Designing & Conducting Health Surveys. 2nd ed. San Francisco: Jossey-Bass Publishers; Biemer, P. P., & Lyberg, L. E. Introduction to Survey Quality. Hoboken, NJ: John Wiley & Sons; Centers for Disease Control and Prevention Youth Risk Behavior Survey Results, United States High School Survey Codebook. Available at: Converse J, Presser S. Survey Questions: Handcrafting the Standardized Questionnaire. Thousand Oaks, CA: Sage Publications; Fowler F. Improving Survey Questions: Design and Evaluation. Thousand Oaks, CA: Sage Publications; Schuman H, Presser S. Questions & Answers in Attitude Surveys: Experiments on Question Form, Wording, and Context. Thousand Oaks, CA: Sage Publications; Sudman S, Bradburn N. Asking Questions: A Practical Guide to Questionnaire Design. San Francisco: Jossey-Bass Publishers; Sudman S, Bradburn N, Schwarz N. Thinking about Answers: The Application of Cognitive Processes to Survey Methodology. San Francisco: Jossey-Bass Publishers; Tourangeau R, Rips L, Rasinski K. The Psychology of Survey Response. New York: Cambridge University Press;

8 What she did was a cross-sectional study, and the document she mailed out was a simple questionnaire. In reading public health research, you may encounter many terms that appear to be used interchangeably: cross-sectional study, survey, questionnaire, survey questionnaire, survey tool, survey instrument, cross-sectional survey. Although many of those terms are indeed used interchangeably, they are not all synonymous. This module will use the term cross-sectional study to refer to this particular research design and the term questionnaire to refer to the data collection form that is used to ask questions of research participants. Data can be collected using instruments other than questionnaires, such as pedometers, which measure distances walked, or scales, which measure weight. However, most cross-sectional studies collect at least some data using questionnaires. 8

9 Section II: Overview of Questionnaire Design A questionnaire is a way of collecting information by engaging in a special kind of conversation. This conversation, which could actually take place face to face, by telephone or even via the mail, has certain rules that separate the questionnaire from usual conversations. The researcher decides what is relevant to his or her study and may ask questions, possibly personal or even embarrassing questions. These questions should be both understandable and relevant to the purpose of the research. The respondent in turn may refuse to participate in the conversation and may refuse to answer any particular question. But having agreed to participate in the study, the respondent has the responsibility to answer questions truthfully. 9

11 Task I: Comprehension The first task of the respondent is to understand the directions and then each question as it is asked. Comprehension is the single most important task facing the respondent, and fortunately it is the characteristic of a question that is most easily controlled by the interviewer. Comprehensible questions are characterized by: 1. A vocabulary appropriate for the target population 2. Simple sentence structure 3. Little or no ambiguity and vagueness Vocabulary is often a problem. The researcher usually knows a great deal about the topic of the questionnaire, and it may be difficult to remember that others do not have that special knowledge. In addition, researchers tend to be very well educated and may have a more extensive vocabulary than people responding to the questionnaire. As a rule, it is best to use the simplest possible word that can be used without sacrificing clear meaning. A dictionary and thesaurus are invaluable in the search for simplicity. Simple sentence structure also makes it easier for the respondent to understand the questions. A very famous example of difficult syntax occurred in 1993 when the Roper Organization created a questionnaire related to the Holocaust, the Nazi extermination of Jews during World War II. One question in this questionnaire was: Does it seem possible or does it seem impossible to you that the Nazi extermination of the Jews never happened? The question has a complicated structure and a double negative impossible and never happened" that could lead respondents to give an answer opposite to what they actually believed. The question was rewritten and given a year later in an otherwise unchanged questionnaire. The reworded question was: Does it seem possible to you that the Nazi extermination of the Jews never happened, or do you feel certain that it happened? This question wording is much clearer. Keeping vocabulary and sentence structure simple is relatively easy compared with stamping out ambiguity in questions. In part, this is because precise and unambiguous language may be difficult to comprehend, as evidenced by definitions we see in mathematics books; they are precise but sometimes difficult to comprehend. Even the most innocent and seemingly clear questions can have a number of possible interpretations. For example, suppose you are asked, When did you move to Chicago? This would seem to be an unambiguous question, but some possible answers might be: 11

12 1. In When I was In the summer The respondent must decide which of these, if any, is the appropriate response. It may be possible to lessen the ambiguity with more precise questions: 1. In what year did you move to Chicago? 2. How old were you when you moved to Chicago? 3. In what season of the year did you move to Chicago? One way to find out if a question is ambiguous is to field test the question and ask the respondents if they were unsure how to answer a question. The table below presents ambiguities identified in the process of debriefing respondents. Question 1. Do you think children suffer any ill effects from watching programs with violence in them? 2. What is the number of servings of eggs you eat in a typical day? 3. What is the average number of days each week you consume butter? Ambiguity 1. The word children was interpreted to mean everyone from babies to teenagers to young adults in their early twenties. 2. It was unclear to the respondents what a serving of eggs was, as well as what the term typical day meant. 3. Respondents were unclear about whether margarine should count as butter. Ambiguity is not only a characteristic of individual questions in a questionnaire. It is also possible for a question to be ambiguous because of its placement in the questionnaire. Here is an example of ambiguity uncovered when the order of two questions differed in two versions of a questionnaire on happiness. The questions were: (i) (ii) [Considering everything], how would you say things are these days: would you say that you are very happy, pretty happy, or not too happy? [Considering everything], how would you describe your marriage: would you say that your marriage is very happy, pretty happy, or not too happy? 12

15 the reading habits of their students. Might students suddenly develop an apparent interest in reading or report they read for pleasure more than is the exact truth? It is clear that constructing questionnaires and writing questions can be a daunting task. Three guidelines to keep in mind are: 1. Questions should be understandable to the individuals in the population being studied. Vocabulary should be of appropriate difficulty, and sentence structure should be simple. 2. Questions should as much as possible recognize that memory is a fickle thing in humans. Questions that are specific will aid the respondent by providing better memory cues. The limitations of memory should be kept in mind when interpreting the respondent's answers. 3. As much as possible, questions should not create opportunities for the respondent to feel threatened or embarrassed. In such cases the responses may be subject to social desirability bias, the degree of which is unknown to the interviewer. This can compromise conclusions drawn from the questionnaire data. 15

17 this method are that it gives results like those of an SRS, and it is easy to actually do. (No barrels or calculators needed!) However, the systematic sample has a clear disadvantage. If there is some known or unknown order to the list, picking every twentieth student may introduce a bias into the sample. A STRATIFIED RANDOM SAMPLE. When doing a cross-sectional study, important subgroups of people may have different views or life experiences or healthrelated behaviors. For example, males and females may have different health issues and different views on how health services should be delivered. As another example, non-english speakers may rate hospital services differently because of the problems inherent in communicating with English-speaking hospital staff. So when gathering information about a diverse population, care must be taken to ensure that the relevant subgroups are adequately represented in the study sample. Which groups are relevant for a particular study may be challenging to determine, but without representation from them the results could be inaccurate. Taking a stratified random sample is easy once the subgroups are identified: Take a simple random sample from each subgroup. These are the three basic methods for taking a sample from a population. However, please do remember that should you decide to take a sample, consult a statistics book for more detail about these methods. 17

18 Section V: Questionnaire Administration Questionnaire design is only one step in the process that ultimately leads to generating answers to research questions of interest. After the questionnaire is designed, researchers should run a pilot test of the questionnaire to make sure it is understandable and acceptable to the intended audience. That process will ideally involve administering the questionnaire to a small group of persons from the intended target group and then following up to get feedback on the questions (e.g., how they were worded, whether the respondents understood them, whether the respondents felt comfortable answering them) and on the questionnaire itself (e.g., whether it was too long, potential barriers to getting good responses). Pilot testing also involves evaluation of other attributes, namely, precision (reliability) and accuracy (validity). Those attributes are critical to developing a questionnaire whose results are reproducible and that provides the researcher with a good measurement of the phenomenon or phenomena of interest. After incorporating feedback from the pilot test, the questionnaire is ready to be administered to a sample from the target population. As mentioned in the section above, the process of responding to interviewer-administered questionnaires depends in part on the respondent, the interviewer and the interaction between the two. To have reliable findings, it is important to have well-trained interviewers. All interviewers should understand the research study and the questionnaire. They should be consistent in the way in which they ask questions, provide prompts and interact with the respondents. Not only should an interviewer be consistent from respondent to respondent but also the questionnaire administration process should be consistent from one interviewer to the next. 18

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