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are certain types of procedures that allow researchers to make inferences about a population based on findings from a sample. |
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Samples are not likely to be identical to their parent populations. This difference between a sample and its population is referred to as |
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Distribution of sample means |
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Once sample size reaches 30, however, the _________ is very nearly normal, even if the population is not normally distributed. |
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a distribution of sample means |
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The mean of a sampling distribution |
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standard error of the mean (SEM) |
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The standard deviation of a sampling distribution of means is called the |
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SEM= SD _____ squareroot of n-1 |
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We now can use the SEM to indicate boundaries, or limits, within which the population mean lies. Such boundaries are called |
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s nothing more than predicted relative occurrence, or relative frequency. |
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Standard error of difference (SED) |
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e means also has its own mean and standard deviation. The mean of the sampling distribution of differences between sample means is equal to the difference between the means of the two populations. The standard deviation of this distribution is called the |
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specifies the predicted outcome of a study. |
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most commonly used specifies there is no relationship in the population; for example: “There is no difference between the population mean of students using method A and the population mean of students using method B.” |
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It is customary in educational research to view as unlikely any outcome that has a probability of .05 (p .05) or less. This is referred to as the |
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State the research hypothesis State the null hypothesis Determine the sample statistics pertinent to the hypothesis Determine the probability of obtaining the sample results
If the probability is small, reject the null hypothesis, thus affirming the research hypothesis
If the probability is large, do not reject the null hypothesis, which means you cannot affirm the research hypothesis. |
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logical sequence for a researcher who wishes to engage in hypothesis testing is as follows: |
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only means that one’s results are likely to occur by chance less than a certain percentage of the time, say 5 percent. |
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a very small correlation coefficient, for example, may turn out to be statistically significant but have little (if any) _______ |
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Because the researchers’ hypothesis can be supported only if he or she obtains a positive difference between the sample means, the researcher is justified in using only the positive tail of the sampling distribution to locate the obtained difference. This is referred to as a |
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The hypothesis would then be supported by a suitable difference in either tail. This is called a |
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this error results when a researcher fails to reject a null hypothesis that is false |
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on the other hand, results when a researcher rejects a null hypothesis that is true. |
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parametric and nonparametric techniques |
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y be inappropriate for categorical data. There are two basic types of inference techniques that researchers use. |
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make various kinds of assumptions about the nature of the population from which the sample(s) involved in the research study are drawn. |
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on the other hand, make few (if any) assumptions about the nature of the population from which the samples are taken. |
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they are generally more powerful* than nonparametric techniques and hence much more likely to reveal a true difference or relationship if one really exists. |
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An advantage of parametric techniques is that |
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is a parametric statistical test used to see whether a difference between the means of two samples is significant. |
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t-test for independent means and t-test for correlated means |
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two forms of this t-test, |
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t-test for independent means |
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is used to compare the mean scores of two different, or independent, groups. |
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This concept is quite important in many inferential statistics tests. In essence, it refers to the number of scores in a frequency distribution that are “free to vary”—that is, that are not fixed. |
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t-test for correlated means |
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is used to compare the mean scores of the same group before and after a treatment of some sort is given, to see if any observed gain is significant, |
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analysis of variance (ANOVA) |
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When researchers desire to find out whether there are significant differences between the means of more than two groups, they commonly use a technique called |
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only means that one’s results are likely to occur by chance less than a certain percentage of the time, say 5 percent. |
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a very small correlation coefficient, for example, may turn out to be statistically significant but have little (if any) _______ |
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Because the researchers’ hypothesis can be supported only if he or she obtains a positive difference between the sample means, the researcher is justified in using only the positive tail of the sampling distribution to locate the obtained difference. This is referred to as a |
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The hypothesis would then be supported by a suitable difference in either tail. This is called a |
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this error results when a researcher fails to reject a null hypothesis that is false |
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on the other hand, results when a researcher rejects a null hypothesis that is true. |
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parametric and nonparametric techniques |
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y be inappropriate for categorical data. There are two basic types of inference techniques that researchers use. |
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make various kinds of assumptions about the nature of the population from which the sample(s) involved in the research study are drawn. |
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on the other hand, make few (if any) assumptions about the nature of the population from which the samples are taken. |
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they are generally more powerful* than nonparametric techniques and hence much more likely to reveal a true difference or relationship if one really exists. |
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An advantage of parametric techniques is that |
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is a parametric statistical test used to see whether a difference between the means of two samples is significant. |
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Term
t-test for independent means and t-test for correlated means |
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Definition
two forms of this t-test, |
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Term
t-test for independent means |
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Definition
is used to compare the mean scores of two different, or independent, groups. |
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Term
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Definition
This concept is quite important in many inferential statistics tests. In essence, it refers to the number of scores in a frequency distribution that are “free to vary”—that is, that are not fixed. |
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t-test for correlated means |
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is used to compare the mean scores of the same group before and after a treatment of some sort is given, to see if any observed gain is significant, |
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analysis of variance (ANOVA) |
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When researchers desire to find out whether there are significant differences between the means of more than two groups, they commonly use a technique called |
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Analysis of variance (ANOVA) |
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actually a more general form of the t-test that is appropriate to use with three or more groups. |
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Analysis of Covariance (ANCOVA) |
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is a variation of ANOVA used when, for example, groups are given a pretest related in some way to the dependent variable and their mean scores on this pretest are found to differ. |
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Multivariate Analysis of Variance (MANOVA) |
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differs from ANOVA in only one respect: It incorporates two or more dependent variables in the same analysis, thus permitting a more powerful test of differences among means. |
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multivariate analysis of covariance (MANCOVA) |
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extends ANCOVA to include two or more dependent variables in the same analysis. |
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This t-test is used to see whether a correlation coefficient calculated on sample data is significant—that is, whether it represents a nonzero correlation in the population from which the sample was drawn. |
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is a nonparametric alternative to the t-test used when a researcher wishes to analyze ranked data. |
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Kruskal-Wallis one-way analysis of variance |
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is used when researchers have more than two independent groups to compare. |
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is used when a researcher wants to analyze two related (as opposed to independent) samples. |
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Friedman two-way analysis of variance |
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If more than two related groups are involved, then the ___________________ test can be used. |
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t tests for a difference in proportions |
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The most commonly used parametric tests for analyzing categorical data are |
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Mann-Whitney U test
Kruskal-Wallis one-way analysis of variance
sign test
Friedman two-way analysis of variance
t tests for a difference in proportions |
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NONPARAMETRIC TECHNIQUES FOR ANALYZING QUANTITATIVE DATA |
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The t-Test for independent Means. T-test for correlated means Analysis of Variance. Analysis of Covariance. Multivariate Analysis of Variance. The t-Test for r. |
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PARAMETRIC TECHNIQUES FOR ANALYZING QUANTITATIVE DATA |
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used to analyze data that are reported in categories. |
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The final step in the chisquare test process is to calculate the |
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Power of a statistical test |
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is similar to the power of a telescope |
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is conducted by one or more individuals or groups for the purpose of solving a problem or obtaining information in order to inform local practice. |
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Practical action Research |
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is intended to address a specific problem within a classroom, school, or other “community.” |
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Participatory action research |
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while sharing the focus on a specific local issue and on using the findings to implement action, differs in important ways from practical action research. |
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function as equal partners |
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For this reason, participatory action research is often referred to as |
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identify the research question gather necessary information analyzing/interpreting information developing an action plan |
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is generated before a study begins, |
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is prepared after a study is completed. In this chapter, we shall describe and illustrate what is expected and usually included in each section of these documents. |
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problem to be investigated |
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(1) the purpose of the study, including the researcher’s assumptions; (2) the justification for the study; (3) the research question and/or hypotheses, including the variables to be investigated; and (4) the definition of terms. |
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states succinctly what the researcher proposes to investigate. |
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researchers must make clear why this particular subject is important to investigate. |
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all key terms should be defined. |
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The particular question to be investigated should be stated next. |
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may be a lengthy section, especially in a master’s thesis or a doctoral dissertation. |
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section includes discussions of: (1) research design, (2) sample, (3) instrumentation, (4) procedural details, (5) internal validity, and (6) data analysis. |
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can be described using the symbols |
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In a proposal, a researcher should indicate in considerable detail how he or she will obtain the subjects—the ____________ |
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Whenever possible, existing instruments should be used in a study, since construction of even the most straightforward test or questionnaire is often a very time-consuming and difficult task. |
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Next, the procedures to be followed in the study—what will be done, as well as when, where, and how—should be described in detail. |
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At this point, the essential planning for a study should be nearly completed. |
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The researcher then should indicate how the data to be collected will be organized |
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Publication Manual of the American Psychological Association (APA) |
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ecks on a computer are a big help here!) A style manual should be consulted before beginning the report. A good source, recommended by most journal editors and used by many researchers when preparing their research reports, is the |
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The_______ of a report is the way it is organized. |
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a brief summary of the entire research report. |
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can be presented only in a research report; |
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section of a report presents the author’s interpretation of what the results imply for theory and/or practice. |
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Suggestions for Further Research. |
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Normally, this is the final section of a report. |
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Finally, the references (bibliography) should list all sources that were used in the writing of the report. |
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Footnotes are numbered consecutively, using a superscript Arabic numeral, in the order in which they appear in the text of the report. |
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Figures consist of drawings, graphs, charts, even photographs or pictures. |
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Tables also should be used only when they can summarize or convey information better, more simply, or more clearly than the text alone. |
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a small-scale trial of the proposed procedures. |
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