altboy2011

Hi Sorry i have been so busy i have not had a look back. Yes this is my work, no this is not "homework' and yes i can see how it might look. I am not trying to get someone to answer homework this is literally just some notes. The explanation that i don’t really have to give is simple. I was doing my study one way, with different Hypothesis and different direction however i was forced to change things abruptly due to issues with data and time. I thank everyone who has commented constructively and helped, as I was having a problem getting my head around how to word things, and how to best approach what it was I was aiming to explore now that I was missing significant sample size and certain data that my original design had required. To those who seem to enjoy poking an prodding, making accusations and generally being rather rude, I think you should check your thought process and re-evaluate how you react to people who want general help and direction.

I appreciate the replies. But my supervisor has limited availability and there are not many resources available to me. Thus i hoped that there might be someone about with some incite that might help to alleviate my worry’s. I ask that unless you wish to offer some constructive criticism or assistance in clarifying or confirming the above information, please refrain from making pointless comments. I am not trying to be rude, i am just somewhat flat out trying to finish this study and my doubts loom over every word i write.

Hi. So this is what i got so far, I removed one as it seemed to be replicated in the two ANOVA i was doing, anyone got any input at all ? These are my new hypothesis. 1. Hypotheses,- As Line Length and the Numerosity Discrimination task are both assessing the same construct magnatatude the Accuracy (ACC) and Reaction Time (RT) should have strong positive correlation between individual tests. A correlation. Paired sample t –test. Anova. c. Test: ANOVA. d. (Reason,: As these tests are designed to measure the same thing, I need to confirm that they do in fact “correlate” with each other on both levels of ACC and RT, as if its only RT that correlate strongly and not ACC it is further support that general processes (information processing) is the predictive or common factor between magnitude test types.) 2. Hypotheses,- The time taken, (RT) to answer a magnitude discrimination task for both line length and numerosity comparison will have a strong positively correlation with the difficulty ratio I.E- ratio 1:1 -1:1.4. (As difficulty increase, so will RT). (1:1 most difficult to easiest 1;4.1) a. IV: Ratios 1:1 – 1:1.4 b. DV: time RT c. Test ? Repeate mesure ANOVA repeate ratios. As incfease if there is reliable change in RT. Or as diff decrease etc. Two ANOVA. 1 for ACC and one for RT. d. (Reason,: Essentially, it has been stated that as stimulus “difference”decreases, and the ratio of difficulty gets harder the time taken to answer should increase, I want to confirm this is the case in this data) 4. The time taken (RT) to correctly answer a Line Length, or Numerosity Comparison task will have a strong positive correlation with Inspection time task reaction time (RT), as both tasks require participants to make fast estimations of magnitude. a. IV: Reaction time for Line and Number b. DV: RT for Inspection time task. c. Test: not sure Standard Multiple regression maybe ? d. (Reason,: As both use the same general domain process of (information processing) for evaluating physical stimuli and making fast estimation of magnitude (line length) they should correlate strongly if they are both using the same underlining process for evaluation of magnitude. (if not further support for domain specific and support for ANS) 5. When compared, Reaction time (RT) for line-length, numerosity comparison tasks, and Inspection time task will account for the greater predictive variance than Accuracy (ACC), on all three tasks, when examining formal maths performance a. IV: Mean Reaction Time for Line length, number and inspection time. b. DV –Maths performance (combined mean scores) c. Test: MRA (standard multiple regression) or ? d. (Reason,: It seems that Reaction Time /IT is the general domain ability that underpins the fast approximations of magnitude, and is more likely the greater predictor of mathematical performance over the accuracy or reaction time of numerical estimations) So essentially, if Reaction Time (RT) in all three measures accounts for more predictive variance, this should provide further evidence that it’s not (ACC)-accuracy of the ANS or necessarily the ANS at all, but rather the speed at which fast estimations of magnitude is performed that predicts maths performance.

Its not homework ? its my own study that i am needing clarifcation on. If its not allowed please advise and i shall remove such.

So, i am doing a study. I don’t want to post up to much information, but i am getting confused about what my IV & DV are and what tests i should use. If you can read below and offer some suggestions i would be greatly appreciate for the help. I just want to confirm IV/DV and what tests I should do, but other advice on papers etc is welcome. In this study we investigated individual differences in ability of undergraduate students on non-symbolic magnitude estimation tasks Line-Length(LL) and Numerosity comparisons (NC) used to measure the Automatic Number Sense. Firstly, i examine the correlations between accuracy and inspection time for the two independent measure of magnitude, then compare reaction times (RT) and accuracy (ACC) on both tasks for different ratios of difficulty, to confirm they measure the same construct (ANS). Furthermore we compare these tasks to Inspection Time Task (IT) to examine if Reaction Time (RT) on the three independent measures can account for more predictive variance in maths performance than the individual measures of magnitude used to assess the ability of the Automatic Number Sense. hypothesis. 1. Hypotheses,- As Line Length and the Numerosity Discrimination task are both assessing the same construct (ANS) the Accuracy (ACC) and Reaction Time (RT) should have strong positive correlation between individual tests. a. IV: - b. DV – c. Test: Paired t-test ? d. (Reason,: As these tests are designed to measure the same thing, I need to confirm that they do in fact “correlate” with each other on both levels of ACC and RT, as if its only RT that correlate strongly and not ACC it is further support that general processes (information processing) is the predictive or common factor between magnitude test types.) 2. Hypotheses,- The time taken, (RT) to answer a magnitude discrimination task for both line length and numerosity comparison will have a strong positively correlation with the difficulty ratio I.E- ratio 1:1 -1:1.4. (As difficulty increase, so will RT). a. IV: Reaction time for Ratios 1:1 – 1:1.4 b. DV: ACC: for Ratios 1:1 – 1:1.4 c. Test ? NO idea d. (Reason,: Essentially, it has been stated that as stimulus “difference”decreases, and the ratio of difficulty gets harder the time taken to answer should increase, I want to confirm this is the case in this data) 3. Hypotheses,- There should be a strong positive correlation between the number and line task’s ratio-difficulty and the accuracy of participant answers. a. IV: Ratio of difficulty. b. DV: Number * line accuracy (ACC) c. Test: (ANOVA ? or … Standard Multiple regression ?) d. (As the difficulty ratio increases from 1:1 to 1:1.4, on number and line magnitude tasks, the accuracy (ACC) of answers should decrease, if both are taping the same system they should show good correlations in the accuracy when examined under the ratios) 4. The time taken (RT) to correctly answer a Line Length, or Numerosity Comparison task will have a strong positive correlation with Inspection time task reaction time (RT), as both tasks require participants to make fast estimations of magnitude. a. IV: Reaction time for Line and Number b. DV: RT for Inspection time task. c. Test: not sure Standard Multiple regression maybe ? d. (Reason,: As both use the same general domain process of (information processing) for evaluating physical stimuli and making fast estimation of magnitude (line length) they should correlate strongly if they are both using the same underlining process for evaluation of magnitude. (if not further support for domain specific and support for ANS) 5. When compared, Reaction time (RT) for line-length, numerosity comparison tasks, and Inspection time task will account for the greater predictive variance than Accuracy (ACC), on all three tasks, when examining formal maths performance a. IV: Mean Reaction Time for Line length, number and inspection time. b. DV –Maths performance (combined mean scores) c. Test: MRA (standard multiple regression) or ? d. (Reason,: It seems that Reaction Time /IT is the general domain ability that underpins the fast approximations of magnitude, and is more likely the greater predictor of mathematical performance over the accuracy or reaction time of numerical estimations) So essentially, if Reaction Time (RT) in all three measures accounts for more predictive variance, this should provide further evidence that it’s not (ACC)-accuracy of the ANS or necessarily the ANS at all, but rather the speed at which fast estimations of magnitude is performed that predicts maths performance. DATA The data file contains RT and ACC for Line length, Number, Inspection time Task for 48 participants. And then Accuracy and Attempts for 4 simple maths tests I.E -dev,math,sub,add. The idea is to compare these to find out the underlining predictor for maths performance. IE is it the RT or ACC of magnitude tasks, and dose the Inspection Time Task that measures RT accurately predicting the same thing. (dose RT for all tests compare) and is this the true indicator of maths performance in magnitude tasks or do individual magnitude task appear to measure different aspects, different underlining ability’s not just one ? It is essentially making a case that “general” domain ability such as speed of information processing underline these tasks and are the true predictor of maths performance, and not a modular or domain specific ability such as the ANS (automatic number sense).