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Search Results: 1 - 10 of 1167 matches for " Noori Akhtar-Danesh "
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A Comparison between Major Factor Extraction and Factor Rotation Techniques in Q-Methodology  [PDF]
Noori Akhtar-Danesh
Open Journal of Applied Sciences (OJAppS) , 2017, DOI: 10.4236/ojapps.2017.74013
Abstract: The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.
Using Cohen’s Effect Size to Identify Distinguishing Statements in Q-Methodology  [PDF]
Noori Akhtar-Danesh
Open Journal of Applied Sciences (OJAppS) , 2018, DOI: 10.4236/ojapps.2018.82006
Abstract: Q-methodology was introduced more than 80 years ago to study subjective topics such as attitudes, perceptions, preferences, and feelings and there has not been much change in its statistical components since then. In Q-methodology, subjective topics are studied using a combination of qualitative and quantitative techniques. It involves development of a sample of statements and rank-ordering these statements by study participants using a grid known as Q-sort table. After completion of Q-sort tables by the participants, a by-person factor analysis (i.e., the factor analysis is performed on persons, not variables or traits) is used to analyze the data. Therefore, each factor represents a group of individuals with similar views, feelings, or preferences about the topic of the study. Then, each group (factor) is usually described by a set of statements, called distinguishing statements, or statements with high or low factor scores. In this article, we review one important statistical issue, i.e. the criteria for identifying distinguishing statements and provide a review of its mathematical calculation and statistical background. We show that the current approach for identifying distinguishing statements has no sound basis, which may result in erroneous findings and seems to be appropriate only when there are repeated evaluations of Q-sample from the same subjects. However, most Q-studies include independent subjects with no repeated evaluation. Finally, a new approach is suggested for identifying distinguishing statements based on Cohen’s effect size. We demonstrate the application of this new formula by applying the current and the suggested methods on a Q-dataset and explain the differences.
Long-Term Trends in the Survival of Women with Endometrial Cancer in Canada: A Population-Based Study  [PDF]
Laurie Elit, Alice Lytwyn, Noori Akhtar-Danesh
Journal of Cancer Therapy (JCT) , 2012, DOI: 10.4236/jct.2012.325109
Abstract: Introduction: Annually in Canada, endometrial cancer affects approximately 4500 women and 790 are expected to die of their disease. To better understand survival trends across the country we undertook this population based study of Canadian women diagnosed with endometrial cancer. Long term trends in relative survival were evaluated by age and geographic region of residence. Methods: Women with an ICD-10 code of C54 and endometrial cancer were identified from the Canadian Cancer Registry. They were included if the incident diagnosis occurred between 1992 and 2005, and they were 16 years and older at diagnosis. A flexible parametric model was used to determine relative survival ratio (i.e., the observed survival rate among cancer patients divided by the expected survival rate in the general population). Results: 18,486 women were diagnosed with endometrial cancer. Mean age was 63.4 (SD=11.8) year. Relative survival decreased with each successive age group cohort of patient (16-49 yr compared to over 60 years, p<0.001). When relative survival was adjusted for age, women in British Columbia had the best outcomes (eastern Canada compared to other jurisdictions p<0.001). Five-year survival outcomes improved for each age group cohort during the 1992 to 2005 time frame. Conclusions: Regional variations in relative survival were identified across Canada for women with endometrial cancer. This suggests that other factors related to the patient or processes of care are involved. Examining these factors in further detail may provide opportunities to improve the care of women with endometrial cancer in Canada.
Relation between body mass index and depression: a structural equation modeling approach
Alina Dragan, Noori Akhtar-Danesh
BMC Medical Research Methodology , 2007, DOI: 10.1186/1471-2288-7-17
Abstract: In this SEM model we postulate that 1) BMI and depression are directly related, 2) BMI is directly affected by the physical activity and, 3)depression is directly influenced by stress. SEM was also used to assess the relation between BMI and depression separately for males and females.The results indicate that higher BMI is associated with more severe form of depression. On the other hand, the more severe form of depression may result in less weight gain. However, the association between depression and BMI is gender dependent. In males, the higher BMI may result in a more severe form of depression while in females the relation may not be the same. Also, there was a negative relationship between physical activity and BMI.In general, use of SEM method showed that the two major diseases, obesity and depression, are associated but the form of the relation is different among males and females. More research is necessary to further understand the complexity of the relationship between obesity and depression. It also demonstrated that SEM is a feasible technique for modeling the relation between obesity and depression.Obesity and depression are two major diseases associated with numerous health complications [1]. Obesity is linked with hypertension, dyslipidemia, diabetes mellitus, coronary heart disease, stroke, as well as increased all cause mortality [2]. Depression contributes to increased risk of coronary heart disease, myocardial infarction, heart failure in patients with systolic hypertension, low bone mineral density, and increased mortality [3-8].Both diseases share common health complications but there is inconsistent findings concerning the relationship between obesity and depression. Some studies concluded that there was no relation between obesity and depression [9,10], while others reported that obese people had higher risk of depression [11,12] or that heavier people were less depressed [13,14]. Goodman and Whitaker [15] showed that depressed adolescents are
Relation between depression and sociodemographic factors
Noori Akhtar-Danesh, Janet Landeen
International Journal of Mental Health Systems , 2007, DOI: 10.1186/1752-4458-1-4
Abstract: The CCHS-1.2 survey classified depression into lifetime depression and 12-month depression. The data were collected based on unequal sampling probabilities to ensure adequate representation of young persons (15 to 24) and seniors (65 and over). The sampling weights were used to estimate the prevalence of depression in each subgroup of the population. The multiple logistic regression technique was used to estimate the odds ratio of depression for each sociodemographic factor.The odds ratio of depression for men compared with women is about 0.60. The lowest and highest rates of depression are seen among people living with their married partners and divorced individuals, respectively. Prevalence of depression among people who live with common-law partners is similar to rates of depression among separated and divorced individuals. The lowest and highest rates of depression based on the level of education is seen among individuals with less than secondary school and those with "other post-secondary" education, respectively. Prevalence of 12-month and lifetime depression among individuals who were born in Canada is higher compared to Canadian residents who immigrated to Canada irrespective of gender. There is an inverse relation between income and the prevalence of depression (p < 0.0001).The patterns uncovered in this dataset are consistent with previously reported prevalence rates for Canada and other Western countries. The negative relation between age and depression after adjusting for some sociodemographic factors is consistent with some previous findings and contrasts with some older findings that the relation between age and depression is U-shaped. The rate of depression among individuals living common-law is similar to that of separated and divorced individuals, not married individuals, with whom they are most often grouped in other studies.Depression is a significant public health concern worldwide and has been ranked as one of the illnesses having the greatest b
Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database
Adefowope Odueyungbo, Dillon Browne, Noori Akhtar-Danesh, Lehana Thabane
BMC Medical Research Methodology , 2008, DOI: 10.1186/1471-2288-8-28
Abstract: We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.Investigators often encounter situations in which plausible statistical models for observed data require an assumption of correlation between successive measurements on the same subjects (longitudinal data) or related subjects (clustered data) enrolled in clinical studies. Statistical models that fail to account for correlation between repeated
How does correlation structure differ between real and fabricated data-sets?
Noori Akhtar-Danesh, Mahshid Dehghan-Kooshkghazi
BMC Medical Research Methodology , 2003, DOI: 10.1186/1471-2288-3-18
Abstract: Three examples are presented where outcomes from made up (fabricated) data-sets are compared with the results from three real data-sets and with appropriate simulated data-sets. Data-sets were made up by faculty members in three universities. The first two examples are devoted to the correlation structures between continuous variables in two different settings: first, when there is high correlation coefficient between variables, second, when the variables are not correlated. In the third example the differences between real data-set and fabricated data-sets are studied using the independent t-test for comparison between two means.In general, higher correlation coefficients are seen in made up data-sets compared to the real data-sets. This occurs even when the participants are aware that the correlation coefficient for the corresponding real data-set is zero. The findings from the third example, a comparison between means in two groups, shows that many people tend to make up data with less or no differences between groups even when they know how and to what extent the groups are different.This study indicates that high correlation coefficients can be considered as a leading sign of data fabrication; as more than 40% of the participants generated variables with correlation coefficients greater than 0.70. However, when inspecting for the differences between means in different groups, the same rule may not be applicable as we observed smaller differences between groups in made up compared to the real data-set. We also showed that inspecting the scatter-plot of two variables can be considered as a useful tool for uncovering fabricated data.Misconduct in medical research has been the subject of many papers in recent years [1-5]. The usual types of misconduct include fabrication and falsification of data, plagiarism, deceptive reporting of results, suppression of existing data, and deceptive design or analysis [4,6]. At the same time, there has been much effort to reveal f
Childhood obesity, prevalence and prevention
Mahshid Dehghan, Noori Akhtar-Danesh, Anwar T Merchant
Nutrition Journal , 2005, DOI: 10.1186/1475-2891-4-24
Abstract: Almost all researchers agree that prevention could be the key strategy for controlling the current epidemic of obesity. Prevention may include primary prevention of overweight or obesity, secondary prevention or prevention of weight regains following weight loss, and avoidance of more weight increase in obese persons unable to lose weight. Until now, most approaches have focused on changing the behaviour of individuals in diet and exercise. It seems, however, that these strategies have had little impact on the growing increase of the obesity epidemic. While about 50% of the adults are overweight and obese in many countries, it is difficult to reduce excessive weight once it becomes established. Children should therefore be considered the priority population for intervention strategies. Prevention may be achieved through a variety of interventions targeting built environment, physical activity, and diet. Some of these potential strategies for intervention in children can be implemented by targeting preschool institutions, schools or after-school care services as natural setting for influencing the diet and physical activity. All in all, there is an urgent need to initiate prevention and treatment of obesity in children.Childhood obesity has reached epidemic levels in developed countries. Twenty five percent of children in the US are overweight and 11% are obese. About 70% of obese adolescents grow up to become obese adults [1-3]. The prevalence of childhood obesity is in increasing since 1971 in developed countries (Table 1). In some European countries such as the Scandinavian countries the prevalence of childhood obesity is lower as compared with Mediterranean countries, nonetheless, the proportion of obese children is rising in both cases [4]. The highest prevalence rates of childhood obesity have been observed in developed countries, however, its prevalence is increasing in developing countries as well. The prevalence of childhood obesity is high in the Middle Eas
Imputation strategies for missing binary outcomes in cluster randomized trials
Jinhui Ma, Noori Akhtar-Danesh, Lisa Dolovich, Lehana Thabane, the CHAT investigators
BMC Medical Research Methodology , 2011, DOI: 10.1186/1471-2288-11-18
Abstract: We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC) method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE) logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT) which has complete data, we designed a simulation study to investigate the performance of above MI strategies.The estimated treatment effect and its 95% confidence interval (CI) from generalized estimating equations (GEE) model based on the CHAT complete dataset are 1.14 (0.76 1.70). When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75) if complete case analysis is used, 1.12 (0.72 1.73) if within-cluster MCMC method is used, 1.21 (0.80 1.81) if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64) if standard logistic regression which does not account for clustering is used.When the percentage of missing data is low or intra-cluster correlation coefficient is small, different approaches for handling missing binary outcome data generate quite similar results. When the percentage of missing data is large, standard MI strategies, which do not take into account the intra-cluster correlation, underestimate the variance of the treatment effect. Within-cluster and across-cluster MI strategies (except for random-effects logistic regression MI strategy), which take the intra-cluster correlation into account, seem to be more appropriate to handle the missing outcome from CRTs. Under the same imputation strategy and percentage of missingness, the estimates of the treatment effect from GEE and RE log
A Severe Acute Respiratory Syndrome extranet: supporting local communication and information dissemination
Ruta K Valaitis, Noori Akhtar-Danesh, Cathy M Kealey, Glenn M Brunetti, Helen Thomas
BMC Medical Informatics and Decision Making , 2005, DOI: 10.1186/1472-6947-5-17
Abstract: During July, 2003, a web-based and paper-based survey was conducted with 53 SARS Steering Committee members in Hamilton. It assessed the use and perceptions of the Extranet that had been built to support the committee during the SARS outbreak. Before distribution, the survey was user-tested based on a think-aloud protocol, and revisions were made. Quantitative and qualitative questions were asked related to frequency of use of the Extranet, perceived overall usefulness of the resource, rationale for use, potential barriers, strengths and limitations, and potential future uses of the Extranet.The response rate was 69.4% (n = 34). Of all respondents, 30 (88.2%) reported that they had visited the site, and rated it highly overall (mean = 4.0; 1 = low to 5 = high). However, the site was rated 3.4 compared with other communications strategies used during the outbreak. Almost half of all respondents (44.1%) visited the site at least once every few days. The two most common reasons the 30 respondents visited the Extranet were to access SARS Steering Committee minutes (63.3%) and to access Hamilton medical advisories (53.3%). The most commonly cited potential future uses for the Extranet were the sending of private emails to public health experts (63.3%), and surveillance (63.3%). No one encountered personal barriers in his or her use of the site, but several mentioned that time and duplication of email information were challenges.Despite higher rankings of various communication strategies during the SARS outbreak, such as email, meetings, teleconferences, and other web sites, users generally perceived a local Extranet as a useful support for the dissemination of local information during public health emergencies.An Extranet is a private network that uses the Internet to securely share information or operations with selected partners; it is accessible from any web browser. The literature describes the use and potential benefits of local area Extranets in hospitals,[1] physi
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