Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation

WHY: Calculating the sample size is essential to reduce the cost of a study and to prove the hypothesis effectively. HOW: Referring to pilot studies and previous research studies, we can choose a proper hypothesis and simplify the studies by using a website or Microsoft Excel sheet that contains formulas for calculating sample size in the beginning stage of the study. MORE: There are numerous formulas for calculating the sample size for complicated statistics and studies, but most studies can use basic calculating methods for sample size calculation.
Chi-Square Distribution ; *Research Design ; *Sample Size ; Statistics as Topic/*methods

As evidence-based practice has become an important issue in healthcare settings, the educational needs for knowledge and skills for the generation and utilization of healthcare evidence are increasing. Systematic review (SR), a way of evidence generation, is a synthesis of primary scientific evidence, which summarizes the best evidence on a specific clinical question using a transparent, a priori protocol driven approach. SR methodology requires a critical appraisal of primary studies, data extraction in a reliable and repeatable way, and examination for validity of the results. SRs are considered hierarchically as the highest form of evidence as they are a systematic search, identification, and summarization of the available evidence to answer a focused clinical question with particular attention to the methodological quality of studies or the credibility of opinion and text. The purpose of this paper is to introduce an overview of the fundamental knowledge, principals and processes in SR. The focus of this paper is on SR especially for the synthesis of quantitative data from primary research studies that examines the effectiveness of healthcare interventions. To activate evidence-based nursing care in various healthcare settings, the best and available scientific evidence are essential components. This paper will include some examples to promote understandings.
Data Interpretation, Statistical ; Evidence-Based Nursing ; Humans ; Information Storage and Retrieval/methods ; Meta-Analysis as Topic ; Research Design ; *Review Literature as Topic

PURPOSE: The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. METHODS: For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. RESULTS: Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. CONCLUSION: It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.
*Data Interpretation, Statistical ; Humans ; Nursing Research/*methods ; Research Design ; Sample Size ; Software

PURPOSE: The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. METHODS: For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. RESULTS: Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. CONCLUSION: It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.
*Data Interpretation, Statistical ; Humans ; Nursing Research/*methods ; Research Design ; Sample Size ; Software

OBJECTIVES: This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.
Adult ; *Data Interpretation, Statistical ; *Epidemiologic Methods ; Female ; Health Services Research/*methods ; Humans ; Insurance, Health/*statistics & numerical data ; Korea ; Male ; Middle Aged ; Patient Admission/*statistics & numerical data ; Regression Analysis ; Research Design ; Selection Bias

BACKGROUND: The revised Consolidated Standards for Reporting of Trials (CONSORT) were developed to improve the reporting of Randomized Controlled Trials. We studied to survey the extent to which RCTs report items included in the revised CONSORT recommendations. METHODS: A descriptive survey of RCTs enrolled in 2005 at KoreaMed, which is a representative database in Korea was done. The main outcome measures were the proportion of RCTs that reported each of 22 checklist items of CONSORT. RESULTS: We identified 125 RCTs from 26 journals. Random sequence implementation (0%), estimated effect size and its precision (0%), sample size determination (8.9%), method of random sequence generation (7.3%), allocation concealment (3.2%), participant flow (4.8%) and any other analysis (7.3%), generalizability of the trial findings (0.8%) were pooly reported. CONCLUSION: The proportions of following the CONSORT recommendations in Korean medical journals were very low. An effort to improve the reporting of RCTs by application and recommendation of CONSORT statement is required.
Checklist ; Double-Blind Method ; Korea ; Outcome Assessment (Health Care) ; Random Allocation ; Sample Size

BACKGROUND: The revised Consolidated Standards for Reporting of Trials (CONSORT) were developed to improve the reporting of Randomized Controlled Trials. We studied to survey the extent to which RCTs report items included in the revised CONSORT recommendations. METHODS: A descriptive survey of RCTs enrolled in 2005 at KoreaMed, which is a representative database in Korea was done. The main outcome measures were the proportion of RCTs that reported each of 22 checklist items of CONSORT. RESULTS: We identified 125 RCTs from 26 journals. Random sequence implementation (0%), estimated effect size and its precision (0%), sample size determination (8.9%), method of random sequence generation (7.3%), allocation concealment (3.2%), participant flow (4.8%) and any other analysis (7.3%), generalizability of the trial findings (0.8%) were pooly reported. CONCLUSION: The proportions of following the CONSORT recommendations in Korean medical journals were very low. An effort to improve the reporting of RCTs by application and recommendation of CONSORT statement is required.
Checklist ; Double-Blind Method ; Korea ; Outcome Assessment (Health Care) ; Random Allocation ; Sample Size

BACKGROUND: The revised Consolidated Standards for Reporting of Trials (CONSORT) were developed to improve the reporting of Randomized Controlled Trials. We studied to survey the extent to which RCTs report items included in the revised CONSORT recommendations. METHODS: A descriptive survey of RCTs enrolled in 2005 at KoreaMed, which is a representative database in Korea was done. The main outcome measures were the proportion of RCTs that reported each of 22 checklist items of CONSORT. RESULTS: We identified 125 RCTs from 26 journals. Random sequence implementation (0%), estimated effect size and its precision (0%), sample size determination (8.9%), method of random sequence generation (7.3%), allocation concealment (3.2%), participant flow (4.8%) and any other analysis (7.3%), generalizability of the trial findings (0.8%) were pooly reported. CONCLUSION: The proportions of following the CONSORT recommendations in Korean medical journals were very low. An effort to improve the reporting of RCTs by application and recommendation of CONSORT statement is required.
Checklist ; Double-Blind Method ; Korea ; Outcome Assessment (Health Care) ; Random Allocation ; Sample Size

BACKGROUND: The revised Consolidated Standards for Reporting of Trials (CONSORT) were developed to improve the reporting of Randomized Controlled Trials. We studied to survey the extent to which RCTs report items included in the revised CONSORT recommendations. METHODS: A descriptive survey of RCTs enrolled in 2005 at KoreaMed, which is a representative database in Korea was done. The main outcome measures were the proportion of RCTs that reported each of 22 checklist items of CONSORT. RESULTS: We identified 125 RCTs from 26 journals. Random sequence implementation (0%), estimated effect size and its precision (0%), sample size determination (8.9%), method of random sequence generation (7.3%), allocation concealment (3.2%), participant flow (4.8%) and any other analysis (7.3%), generalizability of the trial findings (0.8%) were pooly reported. CONCLUSION: The proportions of following the CONSORT recommendations in Korean medical journals were very low. An effort to improve the reporting of RCTs by application and recommendation of CONSORT statement is required.
Checklist ; Double-Blind Method ; Korea ; Outcome Assessment (Health Care) ; Random Allocation ; Sample Size