Unit 5 lesson 2
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The document discusses the independent samples t-test, which is used to compare the means of two independent groups on the same variable. It provides examples of independent groups that could be compared, such as males versus females. It also demonstrates how to perform an independent samples t-test in SPSS and interpret the output, concluding with an example comparing the means of a treatment group and control group.
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Non parametrics
Non-parametric tests are used when data is not normally distributed. They analyze rankings of raw scores rather than means. The Mann-Whitney and Wilcoxon rank-sum tests compare two independent groups and are equivalent to a t-test. They ignore groupings and rank all data points, expecting similar ranks between groups if they are the same. The Kruskal-Wallis test compares multiple groups and is akin to an ANOVA. Chi square examines relationships between categorical variables by comparing observed and expected frequencies in a contingency table to determine if differences are due to chance.
Research in Psychology
Psychology, Research, Independent Variable, Dependent Variable, Experimental, Correlation, Cause and Effect
Parametric Test
Amrita Kumari from Banaras Hindu University submitted an application discussing parametric tests. Parametric tests were developed by R. Fisher and make assumptions about the population distribution from which a sample is drawn. The key assumptions are that the population is normally distributed, observations are independent, populations have equal variance, and data is on a ratio or interval scale. Parametric tests can be used even when distributions are skewed or variances differ, and they have more statistical power than non-parametric tests. Common parametric tests include t-tests, z-tests, and ANOVA. The document then discusses one-sample, dependent, and independent t-tests in more detail. Both advantages like precision and disadvantages like sensitivity
Parametric test
This document provides an introduction to parametric tests in statistics. It defines a parametric test as a statistical test that makes specific assumptions about the population parameter. For a test to be considered parametric, the data must meet four conditions: it must be on an interval or ratio scale, subjects must be randomly selected, and the data must be normally distributed. Examples of parametric tests include t-tests, ANOVA, and z-tests. Parametric tests are more powerful than non-parametric tests if their assumptions are met, but non-parametric tests can be used when the data does not meet parametric assumptions, such as with nominal scale data.
香港六合彩
The document describes a study that compared the drunkenness of 10 cranberry vodka drinkers to 7 boilermaker whiskey drinkers. The vodka drinkers had a mean drunkenness of 52 and the whiskey drinkers had a mean of 39. To account for the effect of weight on drunkenness, the researcher conducted a matched samples t-test by pairing vodka and whiskey drinkers of the same weight. The t-test found no significant difference in drunkenness between the two groups.
T test for two independent samples
The T-test is used to compare the means of two independent groups on an interval or ratio variable. It determines if there is a statistically significant difference between the population means of the two groups. The T-test is simple to use and adaptable to many situations. It answers questions about the relationship between two variables and how altering one may impact the other. The steps include stating hypotheses, determining the significance level, setting up tables to calculate the T-value, and drawing a conclusion about whether there is a significant difference. An example compares the effects of two types of music on college students' concentration abilities.
Statistics
The document discusses using a t-test to test the difference between means of two independent samples. A t-test can be used when samples are from two normally distributed populations and are independent. The formula for a t-test calculates the test value as the difference between the observed and expected values divided by the standard error. An example compares the average farm sizes in two counties and performs a t-test to determine if the difference in means is statistically significant at the 0.05 level. The t-test results show there is not enough evidence to conclude the average farm sizes are different between the counties.
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Recommended
Non parametrics
Non-parametric tests are used when data is not normally distributed. They analyze rankings of raw scores rather than means. The Mann-Whitney and Wilcoxon rank-sum tests compare two independent groups and are equivalent to a t-test. They ignore groupings and rank all data points, expecting similar ranks between groups if they are the same. The Kruskal-Wallis test compares multiple groups and is akin to an ANOVA. Chi square examines relationships between categorical variables by comparing observed and expected frequencies in a contingency table to determine if differences are due to chance.
Research in Psychology
Psychology, Research, Independent Variable, Dependent Variable, Experimental, Correlation, Cause and Effect
Parametric Test
Amrita Kumari from Banaras Hindu University submitted an application discussing parametric tests. Parametric tests were developed by R. Fisher and make assumptions about the population distribution from which a sample is drawn. The key assumptions are that the population is normally distributed, observations are independent, populations have equal variance, and data is on a ratio or interval scale. Parametric tests can be used even when distributions are skewed or variances differ, and they have more statistical power than non-parametric tests. Common parametric tests include t-tests, z-tests, and ANOVA. The document then discusses one-sample, dependent, and independent t-tests in more detail. Both advantages like precision and disadvantages like sensitivity
Parametric test
This document provides an introduction to parametric tests in statistics. It defines a parametric test as a statistical test that makes specific assumptions about the population parameter. For a test to be considered parametric, the data must meet four conditions: it must be on an interval or ratio scale, subjects must be randomly selected, and the data must be normally distributed. Examples of parametric tests include t-tests, ANOVA, and z-tests. Parametric tests are more powerful than non-parametric tests if their assumptions are met, but non-parametric tests can be used when the data does not meet parametric assumptions, such as with nominal scale data.
香港六合彩
The document describes a study that compared the drunkenness of 10 cranberry vodka drinkers to 7 boilermaker whiskey drinkers. The vodka drinkers had a mean drunkenness of 52 and the whiskey drinkers had a mean of 39. To account for the effect of weight on drunkenness, the researcher conducted a matched samples t-test by pairing vodka and whiskey drinkers of the same weight. The t-test found no significant difference in drunkenness between the two groups.
T test for two independent samples
The T-test is used to compare the means of two independent groups on an interval or ratio variable. It determines if there is a statistically significant difference between the population means of the two groups. The T-test is simple to use and adaptable to many situations. It answers questions about the relationship between two variables and how altering one may impact the other. The steps include stating hypotheses, determining the significance level, setting up tables to calculate the T-value, and drawing a conclusion about whether there is a significant difference. An example compares the effects of two types of music on college students' concentration abilities.
Statistics
The document discusses using a t-test to test the difference between means of two independent samples. A t-test can be used when samples are from two normally distributed populations and are independent. The formula for a t-test calculates the test value as the difference between the observed and expected values divided by the standard error. An example compares the average farm sizes in two counties and performs a t-test to determine if the difference in means is statistically significant at the 0.05 level. The t-test results show there is not enough evidence to conclude the average farm sizes are different between the counties.
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Aron chpt 9 ed t test independent samples
This document describes the t-test for independent means, which is used to compare the means of two independent groups when population variances are unknown. It involves calculating the variance of each group, pooling the variances to estimate the population variance, and determining the variance and standard deviation of the distribution of differences between the two group means. The t-value is then calculated and compared to critical values from the t-distribution to determine if the group means are significantly different.
The two sample t-test
This document discusses statistical tests for comparing groups on continuous and categorical outcomes. For binary outcomes, it describes chi-square tests, logistic regression, McNemar's tests, and conditional logistic regression for independent and correlated groups. For continuous outcomes, it discusses t-tests, ANOVA, linear regression, paired t-tests, repeated measures ANOVA, mixed models, and non-parametric alternatives. It also provides examples of calculating odds ratios, standard errors, and performing hypothesis tests like the two-sample t-test.
Two sample t-test
The document discusses comparing measurements from two physicians who measured tumor volumes. A paired t-test was used to test if the measurements significantly differed. The t-test showed no significant difference between physicians. Additionally, two sample t-tests were used to compare tumor volumes between different cancer types, finding significant differences between brain and breast cancers and brain and liver cancers.
The t Test for Two Independent Samples
Chapter 10 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
What is a paired samples t test
The document discusses the paired samples t-test, which is used to compare two sets of measurements made on the same individuals. It notes that this test is appropriate when there are two correlated distributions, such as pre-test and post-test scores from the same people. The null hypothesis is that there is no difference between the pairs. The test calculates the differences between pairs, sums them, and divides this by the standard error of the differences to obtain a t-value, which can be compared to critical values to determine if the null hypothesis can be rejected.
Dependent T Test
The document provides information about conducting a dependent t-test, also known as a paired samples t-test. It is used to compare two dependent or related samples, such as the same group measured at two different time points. The test involves calculating a t-statistic based on the mean difference between pairs and comparing it to a critical value from a t-distribution to determine if the difference is statistically significant. Examples are given of research questions and study designs that could use a dependent t-test to analyze data. The steps of the test procedure are outlined, including stating hypotheses, setting an alpha level, calculating the t-statistic, comparing it to critical values, and making a decision about the null hypothesis.
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The document discusses different types of t-tests used to determine if the means of two samples are statistically significantly different from each other. It describes paired sample t-tests used to compare means when the same subjects are measured before and after a treatment. It also describes two-sample t-tests used to compare independent samples that may have equal or unequal variances, and whether the tests are one-tailed or two-tailed. Examples are provided of interpreting t-test output and determining if differences are statistically significant based on the t-statistic and p-values. Non-parametric alternatives like the Mann-Whitney U test are also briefly mentioned.
T test
The t-test is used to compare the means of two groups and has three main applications:
1) Compare a sample mean to a population mean.
2) Compare the means of two independent samples.
3) Compare the values of one sample at two different time points.
There are two main types: the independent-measures t-test for samples not matched, and the matched-pair t-test for samples in pairs. The t-test assumes normal distributions and equal variances between groups. Examples are provided to demonstrate hypothesis testing for each application.
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This document provides an overview of quantitative data analysis and statistical tests. It discusses research questions, variables, descriptive and inferential statistics. Common statistical tests are explained like the Mann-Whitney U test, Spearman rank correlation, Kruskal-Wallis test, t-test, Pearson correlation, ANOVA, and chi-square test. Factors to consider when selecting a statistical test are highlighted like level of data, number of groups, independent or related groups, and data distribution. The document emphasizes keeping analyses simple and statistics in context of discussion.
T test for independent variables
The document describes how to conduct an independent t-test to compare the means of two unrelated groups. It explains that the t-test assesses whether the means of two samples are statistically different from each other. It provides steps for running a t-test, including determining hypotheses, collecting data, calculating statistics like means and standard deviations, using a formula to calculate the t-value, and comparing the t-value to critical values to determine statistical significance. An example compares cognitive test scores of participants who slept for 4 hours versus 8 hours. The t-test found no significant difference between the groups.
Unit 6 lesson 1
This document provides an introduction to analysis of variance (ANOVA), which allows researchers to compare the means of two or more groups. It discusses key ANOVA concepts like null hypotheses, sources of variation, and assumptions. Examples are provided to demonstrate how to set up and interpret ANOVA tests in SPSS, including output and decisions about rejecting the null hypothesis. The goal is to help students learn the basics of using ANOVA to analyze differences between multiple sample means.
Chapter NineShow all workProblem 1)A skept.docx
Chapter Nine
Show all work
Problem 1)
A skeptical paranormal researcher claims that the proportion of Americans that have seen a UFO is less than 1 in every one thousand.
State the null hypothesis and the alternative hypothesis for a test of significance.
Problem 2)
At one school, the average amount of time that tenth-graders spend watching television each week is 18.4 hours.
The principal introduces a campaign to encourage the students to watch less television.
One year later, the principal wants to perform a hypothesis test to determine whether the average amount of time spent watching television per week has decreased.
Formulate the null and alternative hypotheses for the study described.
Problem 3)
A two-tailed test is conducted at the 5% significance level.
What is the P-value required to reject the null hypothesis?
Problem 4)
A two-tailed test is conducted at the 5% significance level.
What is the
right tail percentile
required to reject the null hypothesis?
Problem 5)
What is the difference between an Type I and a Type II error?
Provide an example of both.
Chapter 10
Show all work
Problem 1)
Steven collected data from 20 college students on their emotional responses to classical music.
Students listened to two 30-second segments from “The Collection from the Best of Classical Music.”
After listening to a segment, the students rated it on a scale from 1 to 10, with 1 indicating that it “made them very sad” to 10 indicating that it “made them very happy.” Steve computes the total scores from each student and created a variable called “hapsad.”
Steve then conducts a one-sample t-test on the data, knowing that there is an established mean for the publication of others that have taken this test of 6.
The following is the scores:
5.0
5.0
10.0
3.0
13.0
13.0
7.0
5.0
5.0
15.0
14.0
18.0
8.0
12.0
10.0
7.0
3.0
15.0
4.0
3.0
Conduct a one-sample t-test.
What is the t-test score?
What is the mean?
Was the test significant?
If it was significant at what P-value level was it significant?
What is your null and alternative hypothesis? Given the results did you reject or fail to reject the null and why?
(Use instructions on page 437 of your textbook, under Hypothesis Tests with the t Distribution to conduct SPSS or Excel analysis).
Problem 2)
Billie wishes to test the hypothesis that overweight individuals tend to eat faster than normal-weight individuals.
To test this hypothesis, she has two assistants sit in a McDonald’s restaurant and identify individuals who order the Big Mac special for lunch.
The Big Mackers as they become known are then classified by the assistants as overweight, normal weight, or neither overweight nor normal weight.
The assistants identify 10 overweight and 10 normal weight Big Mackers.
The assistants record the amount of time it takes them to eight the Big Mac special.
1.0
585.0
1.0
540.0
1.0
660.0
1.0
571.0
1.0
584.0
1..
Chapter 3 part1-Design of Experiments
This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.
Dr. Chan developed a medication that she hypothesizes will reduce .docx
Dr. Chan developed a medication that she hypothesizes will reduce the obsessive thinking in individuals with paraphilias. A colleague suggests they also examine the effect of the medication on sexual arousal. She needs to demonstrate the efficacy of this medication. She obtains permission to recruit subjects from databanks of known sex offenders. Thirty-five people agree who met diagnostic criteria for a paraphila agreed to participate and she gives them a six-month supply of the medication and instructs them to take one pill each morning. After six-months, the subjects return to the clinic. Only thirty return and she cannot locate the other two. She finds twenty other subjects through counselor referrals. They serve as the control group and do not receive the medication. Dr. Chan tests both groups of participants on physiological measures of sexual arousal in the presences of paraphilic stimuli. She also has participants record their thoughts into a digital recorder and journal throughout the day when having thoughts of a sexual nature involving paraphilic stimuli. She finds that the subjects who took the medication showed less sexual arousal in response to the stimuli than the control group. The researcher and her colleagues conclude that the medication is effective for the obsessive thinking and arousal in individuals with paraphilias.
Dr. Chan and her colleagues, encouraged by the original findings, decided to conduct the experiment using a double-blind procedure. Which of the following would be consistent with using a double-blind procedure? (Points : 1)
Avoid telling the participants that the medication is supposed to stop obsessive thinking.
Add a control group of individuals who do not have paraphilias and give them the medication.
Add a group of individuals who meet diagnostic criteria for paraphilias who are given a placebo and ensure that neither the participants nor those administering the post-tests and evaluating the audio and written journals know who is in the mdication group and who is in the control group.
Use the same design as in the original study but tell the subjects that the medicatio nis supposed to help a symptom other than obsessive thinking, such as mood.
===============\
A researcher decided to examine the development of second language learning using shallow-processing and deep-processing tasks. He tested 6-year-olds and 15-year-olds from the local schools. Half of the students in each age group were in the shallow-processing condition; the other half were in the deep-processing condition. The following table shows the percentage of second language words correctly used by the participatns in each of the four conditions.
An analysis reveals a statistically significant (reliable) interaction between the participants’ age and the processing condition. Which of the following statements would be the best interpretation of that interaction? (Points : 1)
Depth of processing h ...
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructions in reducing their high blood pressure whereas another was not given any instructions.
One group of men was provided access to an exercise program and tested two times over a 6-month period for heart health.
For Ch. 12 Data Set 3, comput.
HYPOTHESIS TESTING.pptx
This document discusses hypothesis testing, which is a statistical method used to make decisions using experimental data. It involves proposing and testing a null hypothesis against an alternative hypothesis. The null hypothesis is an initial claim that shows no significant difference or effect, while the alternative hypothesis is contrary to the null hypothesis and shows a real effect or difference. Some examples of hypothesis tests are provided relating to mean scores, medicine efficacy, student preferences, and more. Key terms discussed include null hypothesis, alternative hypothesis, population parameter, hypothesis testing, and p-value.
Psyc 355 Success Begins / snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
8. The paired-samples t test has three assumptions, including all but:
9. We have learned three t tests, including all of the following except
Psyc 355 Massive Success - snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
8. The paired-samples t test has three assumptions, including all but:
Experimentalresearch[1]
This chapter discusses experimental research methodology. Some key points covered include:
1. Experimental research aims to establish cause-and-effect relationships by manipulating an independent variable and observing its effects on a dependent variable.
2. True experimental designs involve random assignment of subjects to treatment and control groups in order to control for extraneous variables.
3. Threats to internal validity like subject characteristics, mortality, instrumentation, history, and implementation must be evaluated and controlled for. Random assignment, pretesting, and use of control groups help address these threats.
4. Different types of experimental designs are discussed including randomized posttest-only, pretest-posttest, Solomon four-group, matching, counter
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docx
WEEK 6 – EXERCISES
Enter your answers in the spaces provided. Save the file using your last name as the beginning of the file name (e.g., ruf_week6_exercises) and submit via “Assignments.” When appropriate,
show your work
. You can do the work by hand, scan/take a digital picture, and attach that file with your work.
1
.
A psychotherapist studied whether his clients self-disclosed more while sitting in an easy chair or lying down on a couch. All clients had previously agreed to allow the sessions to be videotaped for research purposes. The therapist randomly assigned 10 clients to each condition. The third session for each client was videotaped and an independent observer counted the clients’ disclosures. The therapist reported that “clients made more disclosures when sitting in easy chairs (
M
= 18.20) than when lying down on a couch (
M
= 14.31),
t
(18) = 2.84,
p
< .05, two-tailed.” Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
2.
A researcher compared the adjustment of adolescents who had been raised in homes that were either very structured or unstructured. Thirty adolescents from each type of family completed an adjustment inventory. The results are reported in the table below. Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Means on Four Adjustment Scales for
Adolescents from Structured versus Unstructured Homes
Scale
Structured Homes
Unstructured Homes
t
Social Maturity
106.82
113.94
–1.07
School Adjustment
116.31
107.22
2.03*
Identity Development
89.48
94.32
1.93*
Intimacy Development
102.25
104.33
.32
______________________
*
p
< .05
3.
Do men with higher levels of a particular hormone show higher levels of assertiveness? Levels of this hormone were tested in 100 men. The top 10 and the bottom 10 were selected for the study. All participants took part in a laboratory simulation in which they were asked to role-play a person picking his car up from a mechanic’s shop. The simulation was videotaped and later judged by independent raters on each of four types of assertive statements made by the participant. The results are shown in the table below. Explain these results to a person who fully understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Mean Number of Assertive Statements
Type of Assertive Statement
Group
1
2
3
4
Men with High Levels
2.14
1.16
3.83
0.14
Men with Low Levels
1.21
1.32
2.33
0.38
t
3.81**
0.89
2.03*
0.58
______________________
*
p
< .05;
**
p
< 0.1
4.
A manager of a small store wanted to discourage shoplifters by putting signs around the store saying “Shoplifting is a crime!” However, he wanted to make sure this would not result in customers buying less. To test this, he displayed the signs every other W.
Week 5 Independent t Test ExercisesPart IThe hypothesis bein.docx
Week 5 Independent t Test Exercises
Part I
The hypothesis being tested is: Women who are working will have a lower level of depression as compared to women who are not working.
Using Polit2SetC SPSS dataset, which contains a number of mental health variables, determine if the above hypothesis is true.
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Independent Sample T-test.
Move the Dependent Variable (CES_D Score “cesd”) in the area labelled Test Variable.
Move the Independent Variable (Currently Employed “worknow”) into the area labelled Grouping Variable. The worknow variable is coded as (0= those women who do not work and 1= those women who are working). Click on Define Groups in group 1 box type 0 and in group 2 box type 1. Click Continue.
Click continue and then click OK.
Assignment: Through analysis of the data and use of the questions below write one to two paragraphs summarizing your findings from this t-test.
How many women were employed versus not employed in the sample?
What is the total sample size?
What are the mean (SD) CES-D scores for each group?
Interpret the Levene’s statistic. (Hint: Is the assumption of homogeneity of variance met? Are equal variances assumed or not assumed?)
What is the value of the t-statistic, number of degrees of freedom and the p-value?
Does the data support the hypothesis? Why or why not?
Part II
Hypothesis: Women who reported depression scores in wave 1 and wave 2 of the study did not have a significant difference in their level of depression.
Using Polit2SetC SPSS dataset, determine if the above hypothesis is true.
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Paired Samples T-test.
First click on CES-D Score (cesd) and move it into the box labelled Paired Variables (in the rectangle for Pair 1 of Variable 1 and then click on CESD Score, Wave 1 (cesdwav1) and move it into the Paired Variables box (in the rectangle next to CES-D Score, pair 1, variable 2).
Click continue and then click OK.
Assignment: Through analysis of the data and use of the questions below write one to two paragraphs summarizing your findings from this t-test.
What is the total sample size?
What are the mean (SD) CES-D scores at wave 1 and wave 2?
What is the mean difference between the two time periods?
What is the value of the t-statistic, number of degrees of freedom and the p-value(sig)?
Does the data support the hypothesis? Why or why not?
Part III
Using Polit2SetC dataset, run independent groups t-tests for three outcomes. The outcome variables are CES-D Score (cesd), SF12: Physical Health Component Score, standardized (sf12phys) and SF12: Mental Health Component Score, standardized (sf12ment).
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Independent Sample T-test.
Move the Dependent Variables (.
This is the final projectIn this final assignment, we will revi.docx
This document outlines the final project assignment which involves revisiting and analyzing datasets from previous weekly assignments. It provides instructions to analyze mortality data by pooling all cancer causes together, analyze birth data by racial group and seasonality, evaluate a diagnostic test for liver cancer using biomarker data, test for period effects in a medical trial, and analyze differences in physical and cognitive variables by gender and birth order using youth data. It specifies the requirements for completing and formatting the final paper report.
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Aron chpt 9 ed t test independent samples
This document describes the t-test for independent means, which is used to compare the means of two independent groups when population variances are unknown. It involves calculating the variance of each group, pooling the variances to estimate the population variance, and determining the variance and standard deviation of the distribution of differences between the two group means. The t-value is then calculated and compared to critical values from the t-distribution to determine if the group means are significantly different.
The two sample t-test
This document discusses statistical tests for comparing groups on continuous and categorical outcomes. For binary outcomes, it describes chi-square tests, logistic regression, McNemar's tests, and conditional logistic regression for independent and correlated groups. For continuous outcomes, it discusses t-tests, ANOVA, linear regression, paired t-tests, repeated measures ANOVA, mixed models, and non-parametric alternatives. It also provides examples of calculating odds ratios, standard errors, and performing hypothesis tests like the two-sample t-test.
Two sample t-test
The document discusses comparing measurements from two physicians who measured tumor volumes. A paired t-test was used to test if the measurements significantly differed. The t-test showed no significant difference between physicians. Additionally, two sample t-tests were used to compare tumor volumes between different cancer types, finding significant differences between brain and breast cancers and brain and liver cancers.
The t Test for Two Independent Samples
Chapter 10 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
What is a paired samples t test
The document discusses the paired samples t-test, which is used to compare two sets of measurements made on the same individuals. It notes that this test is appropriate when there are two correlated distributions, such as pre-test and post-test scores from the same people. The null hypothesis is that there is no difference between the pairs. The test calculates the differences between pairs, sums them, and divides this by the standard error of the differences to obtain a t-value, which can be compared to critical values to determine if the null hypothesis can be rejected.
Dependent T Test
The document provides information about conducting a dependent t-test, also known as a paired samples t-test. It is used to compare two dependent or related samples, such as the same group measured at two different time points. The test involves calculating a t-statistic based on the mean difference between pairs and comparing it to a critical value from a t-distribution to determine if the difference is statistically significant. Examples are given of research questions and study designs that could use a dependent t-test to analyze data. The steps of the test procedure are outlined, including stating hypotheses, setting an alpha level, calculating the t-statistic, comparing it to critical values, and making a decision about the null hypothesis.
Introduction to t-tests (statistics)
The document discusses different types of t-tests used to determine if the means of two samples are statistically significantly different from each other. It describes paired sample t-tests used to compare means when the same subjects are measured before and after a treatment. It also describes two-sample t-tests used to compare independent samples that may have equal or unequal variances, and whether the tests are one-tailed or two-tailed. Examples are provided of interpreting t-test output and determining if differences are statistically significant based on the t-statistic and p-values. Non-parametric alternatives like the Mann-Whitney U test are also briefly mentioned.
T test
The t-test is used to compare the means of two groups and has three main applications:
1) Compare a sample mean to a population mean.
2) Compare the means of two independent samples.
3) Compare the values of one sample at two different time points.
There are two main types: the independent-measures t-test for samples not matched, and the matched-pair t-test for samples in pairs. The t-test assumes normal distributions and equal variances between groups. Examples are provided to demonstrate hypothesis testing for each application.
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Quantitative data analysis
This document provides an overview of quantitative data analysis and statistical tests. It discusses research questions, variables, descriptive and inferential statistics. Common statistical tests are explained like the Mann-Whitney U test, Spearman rank correlation, Kruskal-Wallis test, t-test, Pearson correlation, ANOVA, and chi-square test. Factors to consider when selecting a statistical test are highlighted like level of data, number of groups, independent or related groups, and data distribution. The document emphasizes keeping analyses simple and statistics in context of discussion.
T test for independent variables
The document describes how to conduct an independent t-test to compare the means of two unrelated groups. It explains that the t-test assesses whether the means of two samples are statistically different from each other. It provides steps for running a t-test, including determining hypotheses, collecting data, calculating statistics like means and standard deviations, using a formula to calculate the t-value, and comparing the t-value to critical values to determine statistical significance. An example compares cognitive test scores of participants who slept for 4 hours versus 8 hours. The t-test found no significant difference between the groups.
Unit 6 lesson 1
This document provides an introduction to analysis of variance (ANOVA), which allows researchers to compare the means of two or more groups. It discusses key ANOVA concepts like null hypotheses, sources of variation, and assumptions. Examples are provided to demonstrate how to set up and interpret ANOVA tests in SPSS, including output and decisions about rejecting the null hypothesis. The goal is to help students learn the basics of using ANOVA to analyze differences between multiple sample means.
Chapter NineShow all workProblem 1)A skept.docx
Chapter Nine
Show all work
Problem 1)
A skeptical paranormal researcher claims that the proportion of Americans that have seen a UFO is less than 1 in every one thousand.
State the null hypothesis and the alternative hypothesis for a test of significance.
Problem 2)
At one school, the average amount of time that tenth-graders spend watching television each week is 18.4 hours.
The principal introduces a campaign to encourage the students to watch less television.
One year later, the principal wants to perform a hypothesis test to determine whether the average amount of time spent watching television per week has decreased.
Formulate the null and alternative hypotheses for the study described.
Problem 3)
A two-tailed test is conducted at the 5% significance level.
What is the P-value required to reject the null hypothesis?
Problem 4)
A two-tailed test is conducted at the 5% significance level.
What is the
right tail percentile
required to reject the null hypothesis?
Problem 5)
What is the difference between an Type I and a Type II error?
Provide an example of both.
Chapter 10
Show all work
Problem 1)
Steven collected data from 20 college students on their emotional responses to classical music.
Students listened to two 30-second segments from “The Collection from the Best of Classical Music.”
After listening to a segment, the students rated it on a scale from 1 to 10, with 1 indicating that it “made them very sad” to 10 indicating that it “made them very happy.” Steve computes the total scores from each student and created a variable called “hapsad.”
Steve then conducts a one-sample t-test on the data, knowing that there is an established mean for the publication of others that have taken this test of 6.
The following is the scores:
5.0
5.0
10.0
3.0
13.0
13.0
7.0
5.0
5.0
15.0
14.0
18.0
8.0
12.0
10.0
7.0
3.0
15.0
4.0
3.0
Conduct a one-sample t-test.
What is the t-test score?
What is the mean?
Was the test significant?
If it was significant at what P-value level was it significant?
What is your null and alternative hypothesis? Given the results did you reject or fail to reject the null and why?
(Use instructions on page 437 of your textbook, under Hypothesis Tests with the t Distribution to conduct SPSS or Excel analysis).
Problem 2)
Billie wishes to test the hypothesis that overweight individuals tend to eat faster than normal-weight individuals.
To test this hypothesis, she has two assistants sit in a McDonald’s restaurant and identify individuals who order the Big Mac special for lunch.
The Big Mackers as they become known are then classified by the assistants as overweight, normal weight, or neither overweight nor normal weight.
The assistants identify 10 overweight and 10 normal weight Big Mackers.
The assistants record the amount of time it takes them to eight the Big Mac special.
1.0
585.0
1.0
540.0
1.0
660.0
1.0
571.0
1.0
584.0
1..
Chapter 3 part1-Design of Experiments
This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.
Dr. Chan developed a medication that she hypothesizes will reduce .docx
Dr. Chan developed a medication that she hypothesizes will reduce the obsessive thinking in individuals with paraphilias. A colleague suggests they also examine the effect of the medication on sexual arousal. She needs to demonstrate the efficacy of this medication. She obtains permission to recruit subjects from databanks of known sex offenders. Thirty-five people agree who met diagnostic criteria for a paraphila agreed to participate and she gives them a six-month supply of the medication and instructs them to take one pill each morning. After six-months, the subjects return to the clinic. Only thirty return and she cannot locate the other two. She finds twenty other subjects through counselor referrals. They serve as the control group and do not receive the medication. Dr. Chan tests both groups of participants on physiological measures of sexual arousal in the presences of paraphilic stimuli. She also has participants record their thoughts into a digital recorder and journal throughout the day when having thoughts of a sexual nature involving paraphilic stimuli. She finds that the subjects who took the medication showed less sexual arousal in response to the stimuli than the control group. The researcher and her colleagues conclude that the medication is effective for the obsessive thinking and arousal in individuals with paraphilias.
Dr. Chan and her colleagues, encouraged by the original findings, decided to conduct the experiment using a double-blind procedure. Which of the following would be consistent with using a double-blind procedure? (Points : 1)
Avoid telling the participants that the medication is supposed to stop obsessive thinking.
Add a control group of individuals who do not have paraphilias and give them the medication.
Add a group of individuals who meet diagnostic criteria for paraphilias who are given a placebo and ensure that neither the participants nor those administering the post-tests and evaluating the audio and written journals know who is in the mdication group and who is in the control group.
Use the same design as in the original study but tell the subjects that the medicatio nis supposed to help a symptom other than obsessive thinking, such as mood.
===============\
A researcher decided to examine the development of second language learning using shallow-processing and deep-processing tasks. He tested 6-year-olds and 15-year-olds from the local schools. Half of the students in each age group were in the shallow-processing condition; the other half were in the deep-processing condition. The following table shows the percentage of second language words correctly used by the participatns in each of the four conditions.
An analysis reveals a statistically significant (reliable) interaction between the participants’ age and the processing condition. Which of the following statements would be the best interpretation of that interaction? (Points : 1)
Depth of processing h ...
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructions in reducing their high blood pressure whereas another was not given any instructions.
One group of men was provided access to an exercise program and tested two times over a 6-month period for heart health.
For Ch. 12 Data Set 3, comput.
HYPOTHESIS TESTING.pptx
This document discusses hypothesis testing, which is a statistical method used to make decisions using experimental data. It involves proposing and testing a null hypothesis against an alternative hypothesis. The null hypothesis is an initial claim that shows no significant difference or effect, while the alternative hypothesis is contrary to the null hypothesis and shows a real effect or difference. Some examples of hypothesis tests are provided relating to mean scores, medicine efficacy, student preferences, and more. Key terms discussed include null hypothesis, alternative hypothesis, population parameter, hypothesis testing, and p-value.
Psyc 355 Success Begins / snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
8. The paired-samples t test has three assumptions, including all but:
9. We have learned three t tests, including all of the following except
Psyc 355 Massive Success - snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
8. The paired-samples t test has three assumptions, including all but:
Experimentalresearch[1]
This chapter discusses experimental research methodology. Some key points covered include:
1. Experimental research aims to establish cause-and-effect relationships by manipulating an independent variable and observing its effects on a dependent variable.
2. True experimental designs involve random assignment of subjects to treatment and control groups in order to control for extraneous variables.
3. Threats to internal validity like subject characteristics, mortality, instrumentation, history, and implementation must be evaluated and controlled for. Random assignment, pretesting, and use of control groups help address these threats.
4. Different types of experimental designs are discussed including randomized posttest-only, pretest-posttest, Solomon four-group, matching, counter
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docx
WEEK 6 – EXERCISES
Enter your answers in the spaces provided. Save the file using your last name as the beginning of the file name (e.g., ruf_week6_exercises) and submit via “Assignments.” When appropriate,
show your work
. You can do the work by hand, scan/take a digital picture, and attach that file with your work.
1
.
A psychotherapist studied whether his clients self-disclosed more while sitting in an easy chair or lying down on a couch. All clients had previously agreed to allow the sessions to be videotaped for research purposes. The therapist randomly assigned 10 clients to each condition. The third session for each client was videotaped and an independent observer counted the clients’ disclosures. The therapist reported that “clients made more disclosures when sitting in easy chairs (
M
= 18.20) than when lying down on a couch (
M
= 14.31),
t
(18) = 2.84,
p
< .05, two-tailed.” Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
2.
A researcher compared the adjustment of adolescents who had been raised in homes that were either very structured or unstructured. Thirty adolescents from each type of family completed an adjustment inventory. The results are reported in the table below. Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Means on Four Adjustment Scales for
Adolescents from Structured versus Unstructured Homes
Scale
Structured Homes
Unstructured Homes
t
Social Maturity
106.82
113.94
–1.07
School Adjustment
116.31
107.22
2.03*
Identity Development
89.48
94.32
1.93*
Intimacy Development
102.25
104.33
.32
______________________
*
p
< .05
3.
Do men with higher levels of a particular hormone show higher levels of assertiveness? Levels of this hormone were tested in 100 men. The top 10 and the bottom 10 were selected for the study. All participants took part in a laboratory simulation in which they were asked to role-play a person picking his car up from a mechanic’s shop. The simulation was videotaped and later judged by independent raters on each of four types of assertive statements made by the participant. The results are shown in the table below. Explain these results to a person who fully understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Mean Number of Assertive Statements
Type of Assertive Statement
Group
1
2
3
4
Men with High Levels
2.14
1.16
3.83
0.14
Men with Low Levels
1.21
1.32
2.33
0.38
t
3.81**
0.89
2.03*
0.58
______________________
*
p
< .05;
**
p
< 0.1
4.
A manager of a small store wanted to discourage shoplifters by putting signs around the store saying “Shoplifting is a crime!” However, he wanted to make sure this would not result in customers buying less. To test this, he displayed the signs every other W.
Week 5 Independent t Test ExercisesPart IThe hypothesis bein.docx
Week 5 Independent t Test Exercises
Part I
The hypothesis being tested is: Women who are working will have a lower level of depression as compared to women who are not working.
Using Polit2SetC SPSS dataset, which contains a number of mental health variables, determine if the above hypothesis is true.
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Independent Sample T-test.
Move the Dependent Variable (CES_D Score “cesd”) in the area labelled Test Variable.
Move the Independent Variable (Currently Employed “worknow”) into the area labelled Grouping Variable. The worknow variable is coded as (0= those women who do not work and 1= those women who are working). Click on Define Groups in group 1 box type 0 and in group 2 box type 1. Click Continue.
Click continue and then click OK.
Assignment: Through analysis of the data and use of the questions below write one to two paragraphs summarizing your findings from this t-test.
How many women were employed versus not employed in the sample?
What is the total sample size?
What are the mean (SD) CES-D scores for each group?
Interpret the Levene’s statistic. (Hint: Is the assumption of homogeneity of variance met? Are equal variances assumed or not assumed?)
What is the value of the t-statistic, number of degrees of freedom and the p-value?
Does the data support the hypothesis? Why or why not?
Part II
Hypothesis: Women who reported depression scores in wave 1 and wave 2 of the study did not have a significant difference in their level of depression.
Using Polit2SetC SPSS dataset, determine if the above hypothesis is true.
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Paired Samples T-test.
First click on CES-D Score (cesd) and move it into the box labelled Paired Variables (in the rectangle for Pair 1 of Variable 1 and then click on CESD Score, Wave 1 (cesdwav1) and move it into the Paired Variables box (in the rectangle next to CES-D Score, pair 1, variable 2).
Click continue and then click OK.
Assignment: Through analysis of the data and use of the questions below write one to two paragraphs summarizing your findings from this t-test.
What is the total sample size?
What are the mean (SD) CES-D scores at wave 1 and wave 2?
What is the mean difference between the two time periods?
What is the value of the t-statistic, number of degrees of freedom and the p-value(sig)?
Does the data support the hypothesis? Why or why not?
Part III
Using Polit2SetC dataset, run independent groups t-tests for three outcomes. The outcome variables are CES-D Score (cesd), SF12: Physical Health Component Score, standardized (sf12phys) and SF12: Mental Health Component Score, standardized (sf12ment).
Follow these steps when using SPSS:
Open Polit2SetC dataset.
Click Analyze then click Compare Means, then Independent Sample T-test.
Move the Dependent Variables (.
This is the final projectIn this final assignment, we will revi.docx
This document outlines the final project assignment which involves revisiting and analyzing datasets from previous weekly assignments. It provides instructions to analyze mortality data by pooling all cancer causes together, analyze birth data by racial group and seasonality, evaluate a diagnostic test for liver cancer using biomarker data, test for period effects in a medical trial, and analyze differences in physical and cognitive variables by gender and birth order using youth data. It specifies the requirements for completing and formatting the final paper report.
hypothesis testing.pptx
The document provides examples of hypothesis testing using z-tests, t-tests, F-tests (ANOVA), and describes how to conduct each test. It includes examples testing hypotheses about means of different groups for variables like exam scores, car crash tests, and sales data. The final example tests whether the monthly sales means are equal to determine which salesman is most likely to be promoted.
Assessment 3 ContextYou will review the theory, logic, and a.docx
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
Lesson 2 Statistics Benefits, Risks, and MeasurementsAssignmen.docx
Lesson 2: Statistics: Benefits, Risks, and Measurements
Assignments
· See your Course Syllabus for the reading assignments.
· Work through the Lesson 2 online notes that follow.
· Complete the Practice Questions and Lesson 2 Assignment.
Learning Objectives
Chapters 1 and 3
After successfully completing this lesson, you should be able to:
· Identify the three conditions needed to conduct a proper study.
· Apply the seven pitfalls that can be encountered when asking questions in a survey.
· Distinguish between measurement variables and categorical variables.
· Distinguish between continuous variables and discrete variables for those that are measurement variables.
· Distinguish between validity, reliability, and bias.
Terms to Know
From Chapter 1
· statistics
· population
· sample
· observational study
· experiment
· selection bias
· nonresponse bias
From Chapter 3
· data (variable)
· categorical variables
· measurement variables
· measurement (discrete) variables
· measurement (continuous) variables
· validity
· reliability
· bias
2.1 What is Statistics?
Section 2.1. Chapter 1
Overview
What is statistics? If you think statistics is just another math course with many formulas and lifeless numbers, you are not alone. However, this is a myth that hopefully will be debunked as you work through this course. Statistics is about data. More precisely, statistics is a collection of procedures and principles for gaining and processing information from collected data. Knowing these principles and procedures will help you make intelligent decisions in everyday life when faced with uncertainty. The following examples are meant to illuminate the definition of statistics.
Example 2.1. Angry Women
Who are those angry women? (Streitfield, D., 1988 and Wallis, 1987.) In 1987, Shere Hite published a best-selling book called Women and Love: A Cultural Revolution in Progress. This 7-year research project produced a controversial 922-page publication that summarized the results from a survey that was designed to examine how American women feel about their relationships with men. Hite mailed out 100,000 fifteen-page questionnaires to women who were members of a wide variety of organizations across the U.S. These organizations included church, political, volunteer, senior citizen, and counseling groups, among many others. Questionnaires were actually sent to the leader of each organization. The leader was asked to distribute questionnaires to all members. Each questionnaire contained 127 open-ended questions with many parts and follow-ups. Part of Hite’s directions read as follows: “Feel free to skip around and answer only those questions you choose.” Approximately 4500 questionnaires were returned. Below are a few statements from this 1987 publication.
· 84% of women are not emotionally satisfied with their relationships
· 95% of women reported emotional and psychological harassment from their partners
· 70% of women married 5 years or more are having extramarital ...
1.3 Experimental Design and Observational Studies
This document discusses experimental design and observational studies. It defines an experiment as a controlled study that establishes cause and effect by varying factors and comparing treatment groups to a control group. An observational study merely observes or collects existing data without influencing variables and cannot prove causation. The document provides guidelines for planning experiments and describes randomized, matched pairs, and rigorously controlled experimental designs. It also discusses placebos, replication, blinding, and examples of an experimental drug study and observational study types.
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Dr. Chan developed a medication that she hypothesizes will reduce .docx
Dr. Chan developed a medication that she hypothesizes will reduce .docx
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Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docx
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docx
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Week 5 Independent t Test ExercisesPart IThe hypothesis bein.docx
This is the final projectIn this final assignment, we will revi.docx
This is the final projectIn this final assignment, we will revi.docx
Assessment 3 ContextYou will review the theory, logic, and a.docx
Assessment 3 ContextYou will review the theory, logic, and a.docx
Lesson 2 Statistics Benefits, Risks, and MeasurementsAssignmen.docx
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This document appears to be slides from a lesson on reviewing homework on bivariate relationships and statistics concepts. It contains sample bivariate data and works through calculating and interpreting various statistics like the mean, standard deviation, skewness, kurtosis, covariance, and Pearson's r correlation coefficient. It also discusses how these statistics are affected by additive and multiplicative constants.
Unit 1 lesson 5
This document discusses measures of distribution in statistics, including skewness, kurtosis, and the normal distribution. Skewness measures the asymmetry of a distribution and whether it is positively or negatively skewed. Kurtosis measures the peakedness of a distribution relative to a normal distribution. The normal distribution is symmetric with a skewness of 0 and kurtosis of 0. Examples of skewness and kurtosis are provided to illustrate different distribution shapes.
Unit 1 lesson 4
This document discusses different measures of spreadoutness in statistics, including range, standard deviation, z-scores, and standard error of the mean. It explains that range is the simplest measure but has limitations, while standard deviation measures how far scores are from the mean in the data's own units. Z-scores indicate an individual score's distance from the mean in units of standard deviations and allow comparison across data sets with different means and standard deviations. The standard error of the mean represents sampling error and is used to construct confidence intervals.
Unit 1 lesson 3
This document discusses different types of graphs used in statistics including frequency distributions, histograms, scatterplots, and making your own scatterplot. It introduces concepts such as frequency, cumulative frequency, intervals, and bivariate relationships. The document is part of a lesson on graphing and distributions in a statistics course for educational research.
Unit 1 lesson 2
This document discusses three measures of central tendency: mode, median, and mean. The mode is the value that occurs most frequently in a data set. A data set can have more than one mode, and the scale of measurement does not need to be numeric for a mode to be computed. The median is the middle value of a data set when the values are arranged in order. It divides the data set into two equal halves. The mean is the average value of all scores and is computed by summing all values and dividing by the number of values. These three measures provide different but important information about the central or typical values in a data set.
Unit 1 lesson 1
This document discusses levels of scale in statistics, including nominal, ordinal, interval, and ratio scales. Nominal scales involve categories with no logical order, ordinal scales involve categories with a logical order but unknown differences between values, interval scales involve categories with a logical order and equal differences between values, and ratio scales involve all the properties of interval scales plus a true zero point. The different scale levels determine what mathematical operations can be performed on the data.
Unit 10 lesson 1
This document discusses repeated measures ANOVA, which is an extension of one-way ANOVA used when multiple measurements are taken on the same subjects. It explains how repeated measures ANOVA partitions the variation due to individual differences and occasions, rather than treating all scores independently. The assumptions and process of conducting repeated measures ANOVA using SPSS are outlined, including checking for sphericity and applying corrections if it is not met. Polynomial trends that can be tested with repeated measures ANOVA are also introduced.
Lecture 10.19.10
This document provides guidance for writing chapters 4 and 5 of a dissertation. It reviews progress made so far, discusses dissertation style guidelines, and offers tips for good writing. Key points covered include conducting a literature review with the research questions in mind, analyzing studies critically rather than just summarizing, and avoiding common mistakes in research methods and data analysis. Guidelines are provided for writing quantitative and qualitative chapters, developing the dissertation proposal, and drafting the methods chapter so others can replicate the study.
Lecture 10.5.10
This document provides an overview of research methods, including qualitative and quantitative approaches. It discusses key aspects of developing a literature review, such as beginning with a broad topic and focusing on specific research questions. The document also compares different types of research (e.g. basic vs. applied), research paradigms (e.g. quantitative vs. qualitative), and study designs (e.g. experimental vs. case study). Fundamental questions about the nature of knowledge and how to choose an appropriate research method are explored.
Lecture 9.28.10
This document provides an overview of how to conduct a literature review. It discusses key aspects of developing good research questions, operational definitions, and formulating hypotheses. It also reviews important components of the literature review chapter, including reviewing theories, types of hypotheses, and evaluating primary vs. secondary sources. Finally, it outlines steps for analyzing literature, such as distinguishing between assertions and evidence, identifying trends, and evaluating references for currency and coverage of the topic. The overall document serves as a guide for graduate students on how to effectively conduct a literature review.
Lecture 9.21.10
This document provides an overview of key concepts related to developing a research study, including theory and hypothesis, research questions, definition of terms, and dissertation structure. Some main points:
- A theory pulls together observations and relationships between variables to make predictions, while a hypothesis predicts the relationship between two or more variables based on a theory.
- Good research questions are clear, feasible, significant, ethical, and have a relationship to the theory or literature. Hypotheses should be testable and come from the research questions.
- Key terms like independent and dependent variables should be operationally defined. Assumptions, limitations, and delimitations should also be outlined.
- A dissertation proposal typically includes chapters on introduction
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- 2. Independent Samples t Test Two different groups measured on the same construct, typically on the same occasion Males ↔ Females Hispanic ↔ Asian Special Ed. Kids ↔ Non Special Ed. Kids Received intervention ↔ Did not receive intervention Next Slide
- 3. Null Hypothesis Next Slide 70 85 80 86 92 77 54 78 56 65 85 98 77 95 78 89 Females Males
- 4. Independent Samples t Test in SPSS Analyze Compare Means Independent-Samples T Test Two Variables: 1 for the score 1 for the grouping var. Next Slide
- 5. Independent Samples t Test in SPSS Next Slide
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