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Brief Article Review Guideline Even though article review guidelines can vary across different situations, journals, institutions, and professors, we can use article review format as persented below. 1⃣ Citation: write the following information about the article you are going to review ➖Include full title, all authors (last name, initials), full journal title, year, volume number, and page numbers. 2⃣Summary: ➖Present summary of essential contents and main ideas 3⃣Study Titles, problem &purpose ➖is the title important? Self explanatory? ➖Was the purpose and/or research question stated clearly? ➖A clear statement of purpose or research questions helps you determine if the topic is important, relevant, and of interest to you. ➖Does the author clearly define a research problem or topic? ➖Is its significance explained? Are core issues or research variables identified? ➖Is specialized terminology usefully defined? 4⃣Was relevant background literature reviewed? ➖A review of the literature should be included in an article describing research to provide some background to the study. ➖Does the author provide an adequate literature review? ➖Does it discuss current research on the problem, and help to situate the authors own research? 5⃣Are the research objectives clearly stated? ➖Are hypotheses or specific research questions identified? 6⃣Methodology ➖Does the author clearly identify the research methodology and any associated limitations of the research design? ➖Are participants described, including the method of sample selection if appropriate? ➖Are instruments adequately described, including issues of appropriateness, validity and reliability? ➖Do any evident biases or ethical considerations arise in relation to the methodology? ➖Are the methods for measuring results clearly explained and appropriate? 7⃣Results ➖What are the author's major findings and conclusions? ➖Have these been supported by the author's analyses, arguments, findings or evidence? ➖Has the author overlooked anything? 8⃣Discussion & conclusion ➖Do the research results validate the authors conclusions and/or recommendations? ➖is the finding adquetly discussed and the author compared the finding with other similar studies?? ➖is the conclusion reported inline with its objectives?? 9⃣ Suggestion for future research: ➖Does the author suggest areas for further research or discussion? 🔟References ➖Are references given (footnotes or bibliography)? ➖What is the size of the reference section? ➖Are the references recent, important? ➖How are the references used: for support, rebuttal, etc.? Please #Share our channel to your friends and classmates and encourage us! @zionresearch Discussion group @researchers_room

The calculation of design effect (DEFF) depends on the specific sampling method used. Here's a breakdown for two common methods: 1. Stratified Sampling: For stratified sampling, where the population is divided into subgroups (strata), the DEFF can be estimated using the following formula: DEFF = 1 + [(n_h - 1) * ρ_h] / (N - n) Where: * DEFF: Design effect * n_h: Sample size from stratum h * ρ_h: Intraclass correlation coefficient within stratum h (measures how similar units within a stratum are) * N: Total population size * n: Total sample size (sum of n_h across all strata) 2. Cluster Sampling: In cluster sampling, where groups (clusters) are selected instead of individual units, the DEFF can be calculated with this formula: DEFF = 1 + (ICC * (average cluster size - 1)) Where: * DEFF: Design effect * ICC: Intraclass correlation coefficient (measures how similar units within a cluster are) * Average cluster size: The average number of units within a cluster If ✅DEFF = 1: your complex design is as efficient as SRS). ✅DEFF > 1: The variance increases, results might be less precise). You might need a bigger sample size to compensate. ✅DEFF < 1 (rare): The variance decreases, results might be more precise than SRS) - this can happen with some stratification techniques. Important points to remember: 📋 These formulas require specific values like intraclass correlation coefficient (ICC), which may need to be estimated from pilot studies or previous research. 📋There are more complex formulas for DEFF that account for weighting adjustments and other design features. ✅Software packages for statistical analysis often have built-in functions to calculate DEFF based on your sampling design. ✌️For more complex sampling designs or if you need help with specific calculations, consulting a statistician is recommended.

DESIGN EFFECT The design effect (DEFF) essentially tells you how much a particular sampling design impacts the variance of your results, compared to a simple random sample (SRS). The design effect (DEFF) is most relevant for complex sampling methods where the selection process deviates from a simple random sample (SRS). 📈 Here are some common sampling methods where DEFF is applied: ➡️ Stratified Sampling: This method divides the population into subgroups (strata) based on relevant characteristics. DEFF helps account for the increased variance within strata compared to SRS. ➡️ Cluster Sampling: Here, groups (clusters) are selected instead of individual units. DEFF reflects the impact of clustering on variance, which can be higher if units within clusters are similar. ➡️ Multistage Sampling: This involves selecting units at multiple stages, like counties then households within those counties. DEFF considers the combined effect of these stages on variance. ➡️ Unequal Probability Sampling: If some units have a higher chance of being selected, DEFF helps adjust for this by incorporating the selection probabilities. 🚫 As a rule of thumb the design effect of 1.5 and 2.0 are usually applied to adjust sample size in complex sampling, however DEFF has it's own estimation. It's important to note that DEFF calculations depend on the specific sampling design. There are different formulas for stratified sampling, cluster sampling, and so on. However, the core concept remains the same - comparing the variance under the chosen design to that of a hypothetical SRS. Let's look at some methods of calculation 👇👇👇

What is Design Effect? How can we calculate it? When do we apply it? @zionresearch