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Chapter 5

Chapter 5 Notes

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Department
Kinesiology & Health Science
Course
KINE 2049
Professor
Merv Mosher
Semester
Fall

Description
Chapter 5:Experimental Research Methods Overview  Purpose of methods section: explain the details of how the investigation was conducted, it is after the introduction  Introduction: explanation of what the researcher is investigating and description of the scope  Literature review is then summarized to provide background information that leads to the problem  Methods section must be in enough detail to let another person replicate the study, must have enough detailed info  Important info about the facilities and the equipment is used and presented  The methods section contains info about the procedure: length, duration, types of treatments  Methods determine the “type” of research conducted Editors Viewpoint: Who’s on First, What’s second?  Compares methods section to sorting out a line up in baseball  It is the authors responsibility to make sure that there is very little confusion for the reader and all relevant info is presented  The purpose of this editors viewpoint is to show what info is needed to score points with fans (readers)  You must describe your subjects/sample (race, age, ethnicity)  Provide clear operational definitions  Clarify where subjects were included/excluded  Identify independent and dependent variables  Talk about validity and reliability of measures taken in the experiment  Provide a step-by-step outline of how the study was carried out  In survey studies: describe the series of steps designed to maximize the return rate  Qualitative studies: document procedures such as in-depth interviews should be written in detail  In the methods section you describe how the subjects were selected  Subset of population = sample (greek u symbol)  Measure all member of pop = census (greek X symbol), can be an estimate of u) =parameter  Statistics: facts about a sample The sample:  The researcher must clearly identify the population of interest, must be clear and not have any ambiguity regarding who is included  Next you must identify all members of the population selected  The researcher then has 2 questions: 1. How large the sample should be 2. How the people should be picked in the sample  Number of people in sample depends on: precision, reduction of error probability and reliability  As the size of the sample size increases, the reliability increases BUT no always:  Some researches may cost a lot more if the sample size is increased, and the cost may not be worth the reliability  And sometimes, it does not matter how large the sample size is, if it is biased it does not represent the population  Example: The Literary Digest Poll - Polled 10 million voters on who would win Roosevelt or Landon - They predicted that Landon would win but Roosevelt won - They sent out ballots via mail and people who were listed in the directories were car owners, thus the sample was mostly upper class people, who favored Landon - People who were most likely to return mailed ballots were upper class and more educated - The poorer population was not shown in the poll, who favored Roosevelt - The literary digest failed to select a proper cross section Sample selection:  Must use a simple random process to select subjects from the population  Every member of the population must have an equal chance of being selected  One persons selection should not affect the selection of another ex. Adding another person to the sample because they are a sibling of a person in the sample = violation of the independent condition 5 Types of Sampling Techniques: Systematic Sampling:  When an entire list of people that could be in the sample is listed, you can use a systematic way into selecting the sample  Ex. In a list of 1000 names, if you want 50 subjects, you’d choose every 20 th person on the list  This leads to random sampling as long as the list contains all members of the population Stratified Random Sampling  Population is first divided into relevant subgroups and then random sampling techniques are applied to each subgroup  Ex. If you want 100 students, you would pick students from each group (senor, junior, sophomore) to come up with a stratified sample. This way the sample represents the student population paying attention to student level as well  Stratification may be done on more than one variable but normally is done only on variables that are relevant to the dependent variable  Stratified sampling techniques ensure that small samples are still representative of the population in what the y are investigating Quota Sampling  Is like stratified sampling, but does NOT involve random selection from the various levels stratified  Ex. You survey the FIRST # of people that come to the survey  This may not be representative because it is not random sampling  This method would exclude students who didn’t come to do the survey  This method is convenient and requires you to have a list of all students, it is not likely to be representative Cluster Sampling  Designed for surveying large populations where is it hard to list all members of the population  Ex. Wanted to survey elementary kids in your province, instead of a random sample, you’d chose to do a random cluster sample: Randomly select 5 counties out of 100  randomly select 5 school districts  randomly select 5 schools -All of the students in the 5 schools would be chosen - This method would violate the “independence” rule because the kids at each school are connected to each other and are being chosen because they go to the same school Accidental Sampling  Involves the use of a self-selected sample, mostly used in televion surveys, magazine surveys  The people that participate in these surveys are those who are interested and willing to take the actions to become involved  This method is completely unacceptable in research reports  Ex. A radio station tell to call a # to talk about their views on abortion - those with the strongest opinion will call - does not represent people with moderate views and also excludes people who did not know about it (do not listen to that radio station) Summary on Sampling  The only sampling that is accepted in a research report is simple random sampling  But sometime it is not possible  Does this mean that any research that does not use simple random sampling is invalid? Yes.  BUT. If the researcher can show that the sample chosen is not different than the actual population, it is okay, but results must be seen with caution Variables  Variable: a characteristic that will show different values under different conditions (exercise intensity, emotional state, temperature)  A main goal of research is to see how variables will behave or react to different conditions in the environment Independent  Independent variable: those that are manipulated by the investigator  Cause-and-effect shows the relation in the affected variable when the independent variable is manipulated  Some projects may have more than 1 independent variable because more than 1 variable may have an effect on the dependent variable/ behavior being studied  The simplest experiment = independent variable with 2 levels and 1 dependent variable Dependent  Dependent variable: measured in an experiment that is affected by the independent variable, it is the outcome (look at studies on page 77-78) Operational Definitions  Defines the characteristic of a variable (independent of dependent)  Ex. Endurance athlete can be put in categories such as 5k, 10k  Ex. People being weighed or in an experiment can be put in categories: 100lbs, 200lbs,  In an experiment, menstrual cycle was operationally defined as premenstrual: 3-5 days before menses, menstrual: day 1-3 during menses, and postmenstrual;: 10-12 days after onset of menses  Operational definitions are important so that researchers can completely understand the details of the research Control Variables  Factors other than the ones chosen/studied that could affect the outcome  Control variable: variable that is held constant during an experiment  The number of control variables are greater than the # of independent and dependent variables  Crucial aspect is to find and fix as many control variables  2 categories: 1. Experiemental: -variables that could be independent variables if the researcher wanted to make them 2. Statistical: - control variables that might impact the results of an experiment - can use the Analysis of Covariance (ANOVA) to “control” the variables, but this procedure costs in terms of statistical concerns and can only be used in certain situations Control Variables  Adding an additional level to the independent variable and this group is exposed to everything as the experimental group except the treatments Confounding Variables  The effect of an uncontrolled variable in an investigation. The presence of a confounding variable may threaten internal validity of the investigation  Ex. In the racket experiment, the angle of the racket when hitting was 90 degrees the first time and then 93 the next, therefore, the angle of the racket is a confounding variable  “The effect of string tension on, rebound, was confounded by the difference in racket angle”  confounding variables can undo the results of an experiment by providing alternate explanations for the findings Variance  a unit of measure of score variability Primary Variance (systematic variance):  Expected or desired variance in the dependent variable by manipulating the independent variable. It is wanted variation. Secondary Variance (extraneous variance):  Consistent and unwanted variation in the measurement of the dependent variable.  Ex. A scale for measuring weight that is not at 0, it measures 2 pounds over the actual weight  Results will consistently over or under estimate the true value of the dependent variable Error Variance:  Inconsistent and unwanted variance  Random variability in the data that is not caused by the independent variable  Variation that occurs that cannot be explained ex. People differ on characteristics that we don’t exactly know why (gender, heredity, diet, age)  “left over” variance- variance that is left over after controlling as many variables as you can  Could also happen by the researcher taking wrong measurements  The effect of error variance is unpredictable and reduces the accuracy  Procedures and research instruments are designed to reduce error variance Primary Variance Consistent and wanted variation on measures of the dependent variable Secondary Variance Consistent and unwanted variation on measures of the dependent variable Error Variance Inconsistent and unwanted variation on measures of the dependent variable Maximizing Primary Variance  1. goal of research is to see the effect of different levels of an independent variable on a dependent variable, thus the researcher must fix the independent variable levels far enough so that change in the dependent variable can be measures  Ex. In tennis racket study, the string tension was set at 50, 60, and 70 lbs. If the researcher had set it at 51, 52, and 53 lbs, much change wouldbt have been recorded  2. goal is that the levels of the independent variables should be set at realistic levels, in which it could be applied to real life  Ex. The 3 levels used in the racket test are the normal range used by tennis player, thus they are realistic and the research would be useful in real life  Thus, by following these 2 goals, the researcher maximized the ways the research would be used in real life Controlling Secondary Variance  Secondary variance can be reduced by careful selection of subjects to experimental groups  Randomization and matching control unwanted consistent differences  Blind experimental design  Properly calibrated equipment Minimizing Error Variance  Minimize by care and precision when gathering data  Differences between subjects can be controlled by using samples that are representative of the population  Appropriate and careful measurement procedures  C
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