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Lecture 7

HLTC15 Lecture 7

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University of Toronto Scarborough
Health Studies
Suzanne Sicchia

HLTC15 –Introduction to Quantitative and Qualitative Health Research Methodologies Lecture 7: Quantitative Research Design and Methods I–Sampling Designs (Chapter 9 & 11) Tuesday, October 23, 2012 Scientific Method and the Process of Quantitative Research 1. Theory 2. Hypothesis (Ho) 3. Research Design 4. Devise Measures of Concepts 5. Select Research Site(s) 6. Select Subjects/ Respondents 7. Collect Data 8. Analyze Data 9. Write-up Findings and Conclusions 10. Disseminate Common Characteristics of Positivist (Quantitative) Research  Interest in cause and effect relationships  Emphasis is on “objectivity” (scientific knowledge) over other forms of “subjective” knowledge  Validity, reliability, generalizability, and replicability  Control of bias in scientific knowledge production  Reductionist: reduces social situations to smaller parts (often just two variables) for study Statistics  The mathematics of the collection, organization, and interpretation of numerical data, especially the analysis of population characteristics by inference from sampling  Three broad categories of statistical tests: 1. Means: Test for a mean; Difference of two means (independent samples); Difference of two means (paired tests) 2. Proportions: Tests for proportion; Differences of two proportions 3. Relationships: Chi-square test for independent, Regression Analysis  **Choosing the right statistics depends on the data, samples, and purpose of the study** Rational and Some Basic Terminology  Why Sample? o To collect data from a sub-set of a population so that valid inferences can be drawn about the whole (generalizability, predictability, etc.)  Terminology: o Target Population: the population of interest o Sampling Unit or Element: member of the target population o Sampling Frame: list of units in the target population o Probability Sampling: where each population element has a specifiable chance of selection o Non-probability sampling: use of judgement criteria Thinking about Sample Design  Who do you want to generalize to?  This is the theoretical population  What population can you get access to?  This is the study population  How can you get access to them?  Refers to the sampling frame  Who is in your study?  The sample Non-Probability Sample Designs (no random selection, typical in qualitative studies) 1. Convenience Sampling – Based on the ease of selection (e.g. patients attending a clinic) 2. Volunteers – Advertising for sample members, used in both qualitative and experimental research 3. Snowball Sampling – starting with an initial sample, and increasing size via referral by sample members 4. Purposive Sampling – selecting all sample elements according to explicit judgement criteria 5. Theoretical Sampling – qualitative approach in which sample selection is guided by the requirements of generating and testing empirical hypotheses Probability Sample Designs (random selection) 1. Simple Random Design 2. Systematic Sample Design 3. Stratified Sample Design 4. (Multi-Stage) Cluster Sample Design 1. Simple Random Sample (SRS)  All units in a given population have an equal probability of being included in the sample  Example: 30 students in a classroom and you want a SRS of 10 students: 1) Assign a # to all students 2) Randomly choose 10 :  Like a lottery  And if the same # comes up more than once, it is dismissed and another # is pulled 2. Systematic Sampling  Sample is selected directly from the sampling frame at some interval after a random starting point has been chosen o Example: Start with the 2nd person and then include every third person until after that until you reach the desired sample size (n) 3. Stratified Sample  Group population according to some characteristic (e.g., sex)  Each group is called a strata  Obtain a (proportional) simple random sample from each group  Proportionality ensures the sample is distributed in the same way as the population 4. Cluster/ Multi-Cluster Samples  Not to be confused with “stratified” sampling  Obtained by selecting all individuals within a randomly selected collection or group of individuals (clusters)  Works best when clusters are diverse (saves money)  Example: 5 Streets in a particular neighbourhood (five clusters)  Assign a # to each cluster, then use SRS to choose ONE cluster (or street); interview every individual living on that street  Multi-Cluster Sample: when a cluster is sub-sampled one or more times Three Sources of Bias in Sample Design 1) Failure to use a random method to choose the sample (where every element or individual has the same chance of being selected—like #s in a lottery) 2) When the sampling frame or list of potential subjects is inaccurate or excludes some cases the sample derived from it may not represent the population (even with randomization) 3) Non-response: when some people refuse to participate – non-responders may be different from those who agree to participate in some significant way Sampling Error (Standard Error)  The amount of inaccuracy in an estimation of some value caused by the sample (a portion of the population) rather than the whole population  Low sampling error means we have relatively less variability or range in the sampling distribution  The greater the sample size, the smaller the standard error (because your sample is closer to the actual population) Non-Sampling Error  Coverage Error: Sources o Frame – how accurate is the sampling frame? o Response – how high is the response rate? o Response – how representative are responders?  Measurement Error: Sources o Respondent – how accurate and reliable are they? o Instrument – how valid and reliable is it? o Recorder – how much influence on the data collected? Chapter 11: Basic Statistical Methods for Health Data Analysis Overview of Statistical Methods  Statistics: The study of the collection, organization, analysis, interpretation, and presentation of data  Two branches of statistical techniques involved in addressing research questions: 1. Descriptive Statistics: Summarize data collected using tables, graphs, or statistical measures such as the mean and correlation coefficient (e.g., Pearson’s r) 2. Inferential Statistics: Help us determine whether or not we can generalize descriptive statistics for a random sample to a wider population Descriptive vs. Inferential Statistics  Statistics 1. Descriptive Statistics  Collecting, Organizing, Summarizing, Presenting Data 2. Inferential statistics  Making inferences, Hypothesis Testing, Relationships, Predictions  Statistics  Descriptive Statistics 1. Graphs + Tables  Pie charts  Bar graphs  Histograms  Frequency (f) Tables 2. Numerical Measures  Measures of Central Tendency (Mean, Median, Mode)  Measures of Dispersion (Univariate): Range (IQR), Standard Deviation Types of Measurements and their Function Type Description Example: Measuring the health status of patients  Values are distinct categories  Classifying patients according to the organ of  Cannot be quantified or rank ordered the body affected by their disease (e.g., Nominal (e.g., gender, marital status, religion) nervous system, respiratory system)  Only allow for qualitative classification  Values are distinguishable  Classifying patients according to whether they Ordinal  Can be rank ordered (x is > or < than y) rate themselves as Very Unhealthy, Unhealthy,  But intervals between scale points are uneven, and Healthy, or Very Healthy there is no meaningful zero point  Values are often ranked (e.g. 1 , 2 , 3 , ...)  Values are distinguishable, continuous data  Counting the number of times in the previous Interal/  Variables can be rank ordered year a patient has consulted a doctor or other ratio  Distance between categories is equal health professional  Ratio: have an absolute zero point, ie., zero is no arbitrary (e.g. weight, length, time, IQ) Descriptive Statistics  The numerical, graphical, and tabular techniques used for organizing, analyzing, and presenting data Type Function Examples Graphs  Provide a visual representation of the distribution Pie bar, histogram, polygon (Univariate) of a variable or variables  Clustered pie, clustered/stacked bar (bivariate, nominal/ordinal scales)
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