Demography - Human Population Ecology A cemetery is an excellent place to study human demography.
Details[ edit ] Inferences are said to possess internal validity if Demographics lab assingment causal relationship between two variables is properly demonstrated. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences.
When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable Demographics lab assingment is, when the researcher observes an association between these variables and can rule out other explanations or rival hypothesesthen the causal inference is said to be internally valid.
Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity. In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study.
As a rule of thumb, conclusions based on direct manipulation of the independent variable allow for greater internal validity than conclusions based on an association observed without manipulation.
When considering only Internal Validity, highly controlled true experimental designs i. However, the very methods used to increase internal validity may also limit the generalizability or external validity of the findings. For example, studying the behavior of animals in a zoo may make it easier to draw valid causal inferences within that context, but these inferences may not generalize to the behavior of animals in the wild.
In general, a typical experiment in a laboratory, studying a particular process, may leave out many variables that normally strongly affect that process in nature.
Confounding[ edit ] A major threat to the validity of causal inferences is confounding: Changes in the dependent variable may rather be attributed to variations in a third variable which is related to the manipulated variable. Where spurious relationships cannot be ruled out, rival hypotheses to the original causal inference may be developed.
Selection bias[ edit ] Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome.
Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent. For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity. For example, a researcher created two test groups, the experimental and the control groups. The subjects in both groups are not alike with regard to the independent variable but similar in one or more of the subject-related variables.
Self-selection also has a negative effect on the interpretive power of the dependent variable. This occurs often in online surveys where individuals of specific demographics opt into the test at higher rates than other demographics.
Often, these are large-scale events natural disaster, political change, etc. Maturation[ edit ] Subjects change during the course of the experiment or even between measurements.
For example, young children might mature and their ability to concentrate may change as they grow up. Both permanent changes, such as physical growth and temporary ones like fatigue, provide "natural" alternative explanations; thus, they may change the way a subject would react to the independent variable.
So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable. Repeated testing also referred to as testing effects [ edit ] Repeatedly measuring the participants may lead to bias.
Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeatedly taking the same or similar intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides a good rival hypotheses.
Instrument change instrumentality [ edit ] The instrument used during the testing process can change the experiment. This also refers to observers being more concentrated or primed, or having unconsciously changed the criteria they use to make judgments.
This can also be an issue with self-report measures given at different times. In this case the impact may be mitigated through the use of retrospective pretesting.
If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
Regression toward the mean[ edit ] Main article: Regression toward the mean This type of error occurs when subjects are selected on the basis of extreme scores one far away from the mean during a test.
For example, when children with the worst reading scores are selected to participate in a reading course, improvements at the end of the course might be due to regression toward the mean and not the course's effectiveness.
If the children had been tested again before the course started, they would likely have obtained better scores anyway. Likewise, extreme outliers on individual scores are more likely to be captured in one instance of testing but will likely evolve into a more normal distribution with repeated testing.
Survivorship bias This error occurs if inferences are made on the basis of only those participants that have participated from the start to the end. However, participants may have dropped out of the study before completion, and maybe even due to the study or programme or experiment itself.
For example, the percentage of group members having quit smoking at post-test was found much higher in a group having received a quit-smoking training program than in the control group.
If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.
Selection-maturation interaction[ edit ] This occurs when the subject-related variables, color of hair, skin color, etc. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.This course introduces the basic techniques of demographic analysis.
Students will become familiar with the sources of data available for demographic research. Population composition and change measures will be presented.
Measures of mortality, fertility, marriage and migration levels and patterns will be defined. Life table, standardization . demography are available from three sources: (1) tombstones in local cemeteries that provide ages at death, (2) on-line cemetery records, and (3) census records that provide the numbers of people alive in different age classes.
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Lab 15 Population Genetics Lab Report. Experiment 1: Genetic Variation After completing Experiment 1 answer the following questions 1. What is the gene pool of beaker #1? 2. What is the gene pool of beaker #2?
3. What is the gene frequency of beaker #1? 4. What is the gene frequency of beaker #2? In this virtual lab, grow two species of paremecium in test tubes and record data on their population growth.
Experiment shows that when grown together, one species will die, illustrating the competitive exclusion principle. Emma Portia Neilsen Demography Lab Date: 5/2/17 Lesson 1: The demographic transition STEP 1 Before doing anything, click through the nine countries provided (Brazil, China, India, Indonesia, Iraq, Italy, Japan, Nigeria, USA) and observe the shapes of each country’s population structure (graph on right showing the population by age group) in the year