6210 Week 6 Assignment 2 How to Critique A Journal Article 1. Select a quantitative article from a peer-reviewed journal that reports on research that uses a t-test for statistical analysis. Remember, there are 3t-tests: independent samples, one sample, and paired samples. Your article must report on 1 of these tests. Do not select an article that uses multiple regression, ANOVA, multivariate analysis, correlation, etc. 2. Write a critique: State if the researchers chose the correct t-test and explain why or why not the choice is either correct or incorrect. Hint: if you believe the t-test choice is incorrect, find another article. Explain why the researchers chose a t test. Do this by examining the RQ, null hypothesis, IV and DV: The RQ should address differences in the DV based on the IV. The IV must be nominal with only 2 groups: for example, male or female. The DV should be interval or ratio. 3. Discuss the data display (datasets, charts, graphs, etc.): Hint; if the display requires a written explanation it should not be in the article. 4. Discuss if the data stand alone: Review the results section for statements that reject or fail to reject the null hypothesis and/or state statistical significance has or has not been achieved. If the statement(s) are supported by the statistics, then the data 'stand alone.' If the statement(s) are not supported by the statistics then the data do not 'stand alone.' The  t  Test  for  Related  Samples   The t  Test  for  Related  Samples   Program  Transcript     MATT  JONES:  As  its  name  implies,  the  independent  samples  t-­test  has  the   assumption  of  the  independence  of  observations.  But  that's not  always  the  case.   Sometimes  we  take  multiple  observations  of  the  same  unit  of  analysis,  such  as  a   person,  over  time.  In  this  case,  we'll  use  a  paired  sample  t-­test,  sometimes   referred  to  as  the  dependent  sample  t-­test.  Let's go  to  SPSS  to  see  how  we  do   this.     To  perform  the  paired  sample  t-­test  in  SPSS,  we  once  again  go  to  Analyze,   Compare  Means,  and  down  to  the  Paired  Sample  T-­test.  SPSS  doesn't  require   much  information  here;;  only  the  pair  of  variables  of  which  we  would  like  to  test.   We  have  a  simulated  data  set  here  for  statistical  anxiety  of  students.  Students   were  provided  with  an  instrument  that  measures  their  anxiety  around  statistical   topics  on  a  number  of  different  constructs-­-­  teachers,  interpretation,  asking  for   help,  worth,  and  self-­conceptualization.     They  were  given  the  test  at  the  beginning  of  the  class  and  at  the  conclusion  of  a   class.  Hence,  why  in  the  value  labels  we  see  pre-­test  and  post-­test.  As  a  teacher,   I  might  have  some  interest  in  determining  whether  students  felt  more  comfortable   with  me  or  had  lowering  anxiety  over  time.  This  is  perfect  for  a  paired  sample  t-­ test.  To  perform  this  paired  sample  t-­test,  we'll  go  to  Analyze,  Compare  Means,   the  Paired  Sample  T-­test.     SPSS doesn't  ask  for  much  information;;  only  the  pair  of  variables  of  which  I   would  like  to  test.  In  this  case,  teacher  pre-­test  and  teacher  post-­test.  So  this  is  a   classic  before  and  after.  The  first  piece  of  output  I  obtain  from  the  paired  sample   t-­test  are  some  descriptive  statistics,  specifically  around  the  pairwise  comparison   I'm looking  at,  which  is  the  teacher  subscale  pre-­test  and  post-­test.     I  see  that  there  is  mean  on  the  pre-­test  of  17.32  and  on  the  post-­test,  an  18.44.   So  it  appears,  at  least  from  a  descriptive  sense,  that  there  is  a  higher  mean  on   the  post-­test  than  the  pre-­test.  On  the  instrument,  higher  scores  on  an  item  or  the   subscale  indicate  higher  levels  of  anxiety  for  that  specific  attitude.  Except  for  this   specific  subscale,  fear  of  statistics  teachers,  where  higher  scores  actually   indicate  lower  levels  of  anxiety.     So  if  post  scores  are  higher  than  pre  scores,  that  means  on  average,  students   feel  lower  levels  of  anxiety  and  more  positive  attitude  about  their  statistics   teacher.  I  can  see  here,  at  least  from  a  descriptive  sense,  that  that  appears  to  be   the  case.  But  from  the  sample,  I  am  performing  a  test  of  statistical  significance.   Next  to  the  mean,  I'm provided  with  the  sample  size  25-­-­  25  observations  pre-­test   and  25  observations  post-­test,  all  the  same  person-­-­  the  standard  deviation  for   the  pre-­test  and  the  post-­test,  and  the  standard  error  of  the  mean.     ©2016  Laureate  Education,  Inc.   1 The  t  Test  for  Related  Samples   Next,  let's go  down  and  interpret  the  paired  sample  test  itself.  We  can  see  that  on   average,  there  was  a  difference  of  1.12  units  on  the  scale  with  a  standard   deviation  of  2.50.  From  the  95%  confidence  interval,  we  see  that  the  true   difference  is  somewhere  between  2.15  and  0.085.  We  have  a  t-­statistic  of  2.235   and  an  associated p-­value  of  0.035.     At  the  0.05  level,  the  results  are  statistically  significant  and  we  can  say  that  there   is  a  significant  difference  between  pre-­test  scores  and  post-­test  scores.   Therefore,  we  can  reject  the  null  hypothesis  that  there  is  no  difference. On average,  it  appears  on  the  post-­test,  students  had  lower  levels  of  anxiety  about   their  statistics  teacher.     This  last  example  illustrated  that  students  felt  more  comfortable  with  statistics  as   time  progressed  and  specifically  felt  less  anxious  about  their  statistics  instructor.  I   certainly  hope  this  example  rings  true  for  you,  and  that  you  feel  comfortable  or  at   least  don't  self-­identify  as  being  anxious  about  statistics  at  the  conclusion  of  this   course.  I  encourage  you  to  review  your  textbook,  review  the  videos,  ask  your   instructor  for  help,  and  also  research  the  resources  here  available  at  Walden   University  to  help  you  succeed.       ©2016  Laureate  Education,  Inc.   2 The t Test for Independent Samples The t Test for Independent Samples Program Transcript MATT JONES: The independent samples t-test is a comparison of means test that compares two means across an independent categorical variable. Let's go to SPSS to see how we conduct this procedure. In my independent sample t-test, I would like to test for any possible differences between socioeconomic status and respondent's race. In SPSS in my Variable View for the race variable, with the label "What is respondent's race of first mention?," I can click on the values. The reason I'm doing this is because an independent sample t-test can only test for differences in two means at one time. Therefore, I can only choose two races for this test. I can see in the race variable that there are a number of races present within this variable. For this test, I will choose respondents that self-identified as white, which is denoted as 1, and respondents to self-identified as black or African American, denoted as 2. I'll need to remember those for the next procedure. To perform this procedure, Analyze, Compare Means, independent sample t-test. My test variable is my dependent variable for the variable of which a mean is calculated on. Therefore, it is that metric level variable, or any other variable where it makes sense to calculate a mean. In this case, it's the socioeconomic status index of the respondent. Click on that. Move it over to the test variable. The grouping variable is my categorical variable. And in this case, it is the respondent's race. I move respondents race over to the grouping variable. Right away, behind the variable name, you will see a set of parentheses with two question marks. This is SPSS's prompt to tell me what races I should enter. SPSS knows that it can only calculate two means and therefore is asking me to define groups. So I must click on the define groups, group 1 and group 2. For group 1, I'm going to enter the value number of 1, which were those respondents that self-identified as white. For group 2, I'm going to enter 2, which for those respondents that self-identified as black or African American. Click Continue, and once I click OK, I will receive the output for my independent sample t-test. The first piece of output I'm provided are the group statistics. I could look at the N and get an idea of the sample that ended up in the test. There are 1,094 white respondents and 191 black or African American respondents. I can see from the descriptive statistics that the mean socioeconomic status index score for whites is 50.99, and for black or African Americans it's 44.96. I'm also provided with standard deviations for each mean, as well as the standard error of the mean. Before I interpret the independent sample t-test, I must first examine the Levene's test for equality of variances. An assumption of the independent samples t-test is that variances are equal across the two groups. SPSS, by default, provides you with this test to test for equality of variances. There's an F ©2016 Laureate Education, Inc. 1 The t Test for Independent Samples statistic, an associated p value with it. The Levene's test tests the null hypothesis that variances are equal. As you can see, the p value is 0.059, which is slightly above the conventional 0.050 threshold. In this case, you have to make a decision whether you reject or retain this null. If you set your level of significance at 0.050, and since 0.0059 is slightly above that, you would fail to reject the null, and assume equal variances. Another option you might have is since this is so close to being statistically significant, you could also assume unequal variances, especially since you have an imbalance in the sample size above. For this specific test, I'm going to choose to interpret equal variances not assumed. As such, I interpret the bottom row. Here, I have a t statistic of 4.216, an associated p value of 0.000, which means the results are statistically significant at the 0.001 level. The mean difference between white and black or African Americans, on average, is 6.02, with a 95% confidence interval of the difference being between 3.21 and 8.84. Therefore, I can safely reject the null hypothesis and conclude that there is a significant difference in socioeconomic status between those who identify as white and those who identify as black or African American. ©2016 Laureate Education, Inc. 2
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